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Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

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\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

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\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n
LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n
\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n
\n\n\n\n

Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n
\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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Latest

\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

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LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

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But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

Search

Latest

\n

The cultural aspect of digital transformation has perhaps traditionally been overlooked in favor of hype around glamorous digital technologies. But as those with experience of corporate transformation will attest, new technologies will have little impact within an organization whose practices and processes do not change to accommodate them. <\/p>\n\n\n\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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Latest

\n

A new cultural paradigm<\/strong><\/h4>\n\n\n\n

The cultural aspect of digital transformation has perhaps traditionally been overlooked in favor of hype around glamorous digital technologies. But as those with experience of corporate transformation will attest, new technologies will have little impact within an organization whose practices and processes do not change to accommodate them. <\/p>\n\n\n\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

Relatively simple, starting-point applications of AI in oil and gas<\/a> include in the inspection and maintenance of physical assets, security and surveillance of assets, and optimization of plans of asset maintenance and inspection. Yet even here the industry runs into trouble, with many companies unwilling to share the data that would make these tasks viable, citing concerns over competition and cybersecurity. That\u2019s why, as oil and gas digitization moves forward, ensuring cybersecurity will need to be a top priority. But just as important - if not more so - will be ensuring that as the technology modernizes, so does company culture.<\/p>\n\n\n\n

A new cultural paradigm<\/strong><\/h4>\n\n\n\n

The cultural aspect of digital transformation has perhaps traditionally been overlooked in favor of hype around glamorous digital technologies. But as those with experience of corporate transformation will attest, new technologies will have little impact within an organization whose practices and processes do not change to accommodate them. <\/p>\n\n\n\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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What\u2019s more, <\/strong>5G technology is sure to accelerate the move by hydrocarbon extractors toward more process automation and robotics, given that 5G networks are able to deal with huge amounts of data across operational areas including logistics, supply and production. <\/p>\n\n\n\n

Relatively simple, starting-point applications of AI in oil and gas<\/a> include in the inspection and maintenance of physical assets, security and surveillance of assets, and optimization of plans of asset maintenance and inspection. Yet even here the industry runs into trouble, with many companies unwilling to share the data that would make these tasks viable, citing concerns over competition and cybersecurity. That\u2019s why, as oil and gas digitization moves forward, ensuring cybersecurity will need to be a top priority. But just as important - if not more so - will be ensuring that as the technology modernizes, so does company culture.<\/p>\n\n\n\n

A new cultural paradigm<\/strong><\/h4>\n\n\n\n

The cultural aspect of digital transformation has perhaps traditionally been overlooked in favor of hype around glamorous digital technologies. But as those with experience of corporate transformation will attest, new technologies will have little impact within an organization whose practices and processes do not change to accommodate them. <\/p>\n\n\n\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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Latest

\n

On the technology side, 5G and artificial intelligence<\/strong><\/a> are set to be the most pivotal developments for the transformation of oil and gas. With 5G\u2019s high speed and ultra-low latency, oil and gas companies will be able to communicate and share data between onshore and offshore operations more smoothly and reliably than ever before, in the process replacing wired systems with wireless ones, and increasing capabilities in remote monitoring and real-time asset management. Both are priority areas at a time when offshore drilling in ever-more exotic locations is on the rise.<\/p>\n\n\n\n

What\u2019s more, <\/strong>5G technology is sure to accelerate the move by hydrocarbon extractors toward more process automation and robotics, given that 5G networks are able to deal with huge amounts of data across operational areas including logistics, supply and production. <\/p>\n\n\n\n

Relatively simple, starting-point applications of AI in oil and gas<\/a> include in the inspection and maintenance of physical assets, security and surveillance of assets, and optimization of plans of asset maintenance and inspection. Yet even here the industry runs into trouble, with many companies unwilling to share the data that would make these tasks viable, citing concerns over competition and cybersecurity. That\u2019s why, as oil and gas digitization moves forward, ensuring cybersecurity will need to be a top priority. But just as important - if not more so - will be ensuring that as the technology modernizes, so does company culture.<\/p>\n\n\n\n

A new cultural paradigm<\/strong><\/h4>\n\n\n\n

The cultural aspect of digital transformation has perhaps traditionally been overlooked in favor of hype around glamorous digital technologies. But as those with experience of corporate transformation will attest, new technologies will have little impact within an organization whose practices and processes do not change to accommodate them. <\/p>\n\n\n\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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Key technologies: 5G and AI<\/strong><\/h4>\n\n\n\n

On the technology side, 5G and artificial intelligence<\/strong><\/a> are set to be the most pivotal developments for the transformation of oil and gas. With 5G\u2019s high speed and ultra-low latency, oil and gas companies will be able to communicate and share data between onshore and offshore operations more smoothly and reliably than ever before, in the process replacing wired systems with wireless ones, and increasing capabilities in remote monitoring and real-time asset management. Both are priority areas at a time when offshore drilling in ever-more exotic locations is on the rise.<\/p>\n\n\n\n

What\u2019s more, <\/strong>5G technology is sure to accelerate the move by hydrocarbon extractors toward more process automation and robotics, given that 5G networks are able to deal with huge amounts of data across operational areas including logistics, supply and production. <\/p>\n\n\n\n

Relatively simple, starting-point applications of AI in oil and gas<\/a> include in the inspection and maintenance of physical assets, security and surveillance of assets, and optimization of plans of asset maintenance and inspection. Yet even here the industry runs into trouble, with many companies unwilling to share the data that would make these tasks viable, citing concerns over competition and cybersecurity. That\u2019s why, as oil and gas digitization moves forward, ensuring cybersecurity will need to be a top priority. But just as important - if not more so - will be ensuring that as the technology modernizes, so does company culture.<\/p>\n\n\n\n

A new cultural paradigm<\/strong><\/h4>\n\n\n\n

The cultural aspect of digital transformation has perhaps traditionally been overlooked in favor of hype around glamorous digital technologies. But as those with experience of corporate transformation will attest, new technologies will have little impact within an organization whose practices and processes do not change to accommodate them. <\/p>\n\n\n\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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But legacies notwithstanding, change will inevitably come. In fact, we can already see it taking shape in two key forms: technological and cultural.<\/p>\n\n\n\n

Key technologies: 5G and AI<\/strong><\/h4>\n\n\n\n

On the technology side, 5G and artificial intelligence<\/strong><\/a> are set to be the most pivotal developments for the transformation of oil and gas. With 5G\u2019s high speed and ultra-low latency, oil and gas companies will be able to communicate and share data between onshore and offshore operations more smoothly and reliably than ever before, in the process replacing wired systems with wireless ones, and increasing capabilities in remote monitoring and real-time asset management. Both are priority areas at a time when offshore drilling in ever-more exotic locations is on the rise.<\/p>\n\n\n\n

What\u2019s more, <\/strong>5G technology is sure to accelerate the move by hydrocarbon extractors toward more process automation and robotics, given that 5G networks are able to deal with huge amounts of data across operational areas including logistics, supply and production. <\/p>\n\n\n\n

Relatively simple, starting-point applications of AI in oil and gas<\/a> include in the inspection and maintenance of physical assets, security and surveillance of assets, and optimization of plans of asset maintenance and inspection. Yet even here the industry runs into trouble, with many companies unwilling to share the data that would make these tasks viable, citing concerns over competition and cybersecurity. That\u2019s why, as oil and gas digitization moves forward, ensuring cybersecurity will need to be a top priority. But just as important - if not more so - will be ensuring that as the technology modernizes, so does company culture.<\/p>\n\n\n\n

A new cultural paradigm<\/strong><\/h4>\n\n\n\n

The cultural aspect of digital transformation has perhaps traditionally been overlooked in favor of hype around glamorous digital technologies. But as those with experience of corporate transformation will attest, new technologies will have little impact within an organization whose practices and processes do not change to accommodate them. <\/p>\n\n\n\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


\n\n\n\n

The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

\n
LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


\n\n\n\n
Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


\n\n\n\n

Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


\n\n\n\n

Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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\n

Industry majors are slowly waking up to this reality, as evidenced by the fact that many have launched digital transformation programs. These initiatives, most still in their early stages, are designed to bring about change inside what are often very traditional companies with more than one hundred years of history behind them.<\/p>\n\n\n\n

But legacies notwithstanding, change will inevitably come. In fact, we can already see it taking shape in two key forms: technological and cultural.<\/p>\n\n\n\n

Key technologies: 5G and AI<\/strong><\/h4>\n\n\n\n

On the technology side, 5G and artificial intelligence<\/strong><\/a> are set to be the most pivotal developments for the transformation of oil and gas. With 5G\u2019s high speed and ultra-low latency, oil and gas companies will be able to communicate and share data between onshore and offshore operations more smoothly and reliably than ever before, in the process replacing wired systems with wireless ones, and increasing capabilities in remote monitoring and real-time asset management. Both are priority areas at a time when offshore drilling in ever-more exotic locations is on the rise.<\/p>\n\n\n\n

What\u2019s more, <\/strong>5G technology is sure to accelerate the move by hydrocarbon extractors toward more process automation and robotics, given that 5G networks are able to deal with huge amounts of data across operational areas including logistics, supply and production. <\/p>\n\n\n\n

Relatively simple, starting-point applications of AI in oil and gas<\/a> include in the inspection and maintenance of physical assets, security and surveillance of assets, and optimization of plans of asset maintenance and inspection. Yet even here the industry runs into trouble, with many companies unwilling to share the data that would make these tasks viable, citing concerns over competition and cybersecurity. That\u2019s why, as oil and gas digitization moves forward, ensuring cybersecurity will need to be a top priority. But just as important - if not more so - will be ensuring that as the technology modernizes, so does company culture.<\/p>\n\n\n\n

A new cultural paradigm<\/strong><\/h4>\n\n\n\n

The cultural aspect of digital transformation has perhaps traditionally been overlooked in favor of hype around glamorous digital technologies. But as those with experience of corporate transformation will attest, new technologies will have little impact within an organization whose practices and processes do not change to accommodate them. <\/p>\n\n\n\n

The transformation of oil and gas is no exception, with the change now taking place in the industry perhaps well characterized as a shift from being reactive to being proactive. This change of mindset is driven by the availability of more data (and, increasingly, AI-generated insights) to not just business leaders but managers and engineers too. This influx of information, if used properly, allows for advances such as predicting maintenance issues as much as ten days before they appear. <\/p>\n\n\n\n

But to reach that state of affairs, where the benefits of new technologies are realized in concrete cost and time savings, requires several steps. Investment in workforce training and upskilling is among them, as is the need to structure a company such that decisions made on the basis of data can then smoothly translate into the business processes that will ripple out across an organization.<\/p>\n\n\n\n

That is much easier said than done, though there are templates to look to for guidance, such as the agile methodology popular in software development. Adopting similar ideas is today at the top of the agenda for many oil and gas executives who are looking for new paradigms to accompany the many new technologies that are now available to them. <\/p>\n\n\n\n

Furthermore, those same executives have long realized that a new mindset is essential if the industry is to attract talent among millennials and generation Z, who have come to think of oil and gas as outdated in comparison to enterprises perceived as true \u2018tech companies\u2019, such as Google and Amazon.<\/p>\n\n\n\n

Making the business case<\/strong><\/h4>\n\n\n\n

When all is said and done, digital transformation is, ultimately, about driving better business outcomes. Hydrocarbon enterprises would do well to keep this in mind today, given the demands placed upon them to meet criteria for environmental sustainability, while at the same time weathering a downturn in which global demand for oil and gas is at best uncertain.<\/p>\n\n\n\n

This problem - of needing to do more with less - is undoubtedly a tough one to face but in a world of connected devices and at the dawn of the 5G era, digital transformation offers a ready solution, for those willing to make the journey.<\/p>\n\n\n\n


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The oil and gas sector is going through a period of intense change. Companies in the sector, in turbulent times like these, must change too if they are to survive. Our online program for oil and gas executives teaches the practical steps for innovation, and connects program participants to leading Silicon Valley innovators. Start your journey today!<\/em><\/p>\n\n\n\n

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LEARN MORE<\/strong><\/a><\/div>\n<\/div>\n","post_title":"Shifting Cultures and Technologies: The Digital Transformation of Oil & Gas","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"shifting-cultures-and-technologies-digital-transformation-of-oil-and-gas","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:36","post_modified_gmt":"2021-12-10 15:08:36","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=8075","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":6931,"post_author":"17","post_date":"2021-04-01 06:21:00","post_date_gmt":"2021-04-01 13:21:00","post_content":"\n

New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.<\/strong><\/p>\n\n\n\n

In the age of mobile, a successful digital experience is not just personal but personalized. <\/p>\n\n\n\n

A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they\u2019ve watched recently, updates on their daily commute route from their preferred navigation app and recommended products from their e-commerce website of choice. With offerings tailored to their needs and interests across the board, consumers now expect highly customized digital experiences. And the financial industry<\/a> is no exception. <\/p>\n\n\n\n

Over the last decade, banking institutions have migrated to digital<\/a>, offering their customers round-the-clock access to banking services in online platforms and mobile apps. Users can access their balance and bank statements on the go, wherever they are, whenever they need it. Now, what if customers could access even more information? What would the engagement <\/a>to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables? <\/p>\n\n\n\n

Enter hyper-personalization<\/em><\/strong>.<\/p>\n\n\n\n

Today, machine learning and data analytics <\/a>can be harnessed to deliver an omni-channel digital experience to your customers. For banks and finance companies with a wealth of data available, hyper-personalization represents a window of opportunity to stay ahead of the curve<\/a> with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating <\/a>that successful personalization at scale could represent an increase of 10% in a bank\u2019s annual revenue.<\/p>\n\n\n\n

Read on to learn more about how fintechs are harnessing the potential of AI <\/a>and data to create valuable offerings solutions for the age of digital innovation.<\/p>\n\n\n\n

Building customer relations with data<\/h3>\n\n\n\n

As banks look to the future, new technologies like big data and AI are set to unlock unprecedented potential to improve services and bring institutions closer to customers<\/em><\/p>

Banking of the Future: Finance in the Digital Age - HSBC, 2019. <\/p><\/blockquote>\n\n\n\n

The financial industry services an incredibly diverse set of customers, from young graduates starting their careers and long-term customers planning for their retirements to high-earners looking for new investment options and eager entrepreneurs requesting a loan for their new business.<\/p>\n\n\n\n

In the past, providing a highly-customized digital experience for each of those customers would have been mere wishful thinking, but AI and big data have made it both real and scalable. Today, new players in the fintech space are developing personalized solutions that celebrate customers' individuality and meet their lifestyle banking needs, increasing customer satisfaction, loyalty and activity. In fact, a 2016 Accenture repor<\/a>t already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.<\/p>\n\n\n\n

One such company is Crayon Data<\/a>. Based in Singapore, the startup delivers big data solutions centered on choice that help companies better engage with their customers across all channels. The startup\u2019s personalization engine, maya.ai, enables businesses to offer users personalized lifestyle choices, which translates into customer activation, higher response rate, loyalty improvements and increases in both frequency of card usage and spending. <\/p>\n\n\n\n

\"SVIC<\/figure>\n\n\n\n

Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client\u2019s current needs and even predict their future ones. Take Canadian startup Flybits<\/a>, which builds personalization solutions with contextual intelligence. Through a triage of data, content and context, Flybits enables finance providers to better understand their customers and segment them. Moreover, these insights can then be funneled into digital experiences with highly-engaging content and recommendations for each of them. <\/p>\n\n\n\n

Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy<\/a>, for example, is leading the way in cognitive banking by embedding functionalities such as voice IVR, multilingual bots, natural language processing, machine vision and AI-powered recommendations. One of their solutions allows customers to carry transfers, payments, remittances and loans on their phone without having to download a banking app - just using the BankBuddy Whatsapp bot. A seamless customer experience that feels as intuitive and natural as chatting with a friend.<\/p>\n\n\n\n

A<\/strong>rtificial intelligence, intelligent applications<\/h3>\n\n\n\n

With access to vast reams of customer data, banks will be well placed to deliver proprietary, personalised insights and advice to help customers optimise their finances and meet their personal goals in life<\/em>. <\/p>

The Future of Retail Banking Report - MarketForce, 2017 <\/p><\/blockquote>\n\n\n\n

Omni-channel personalization powered by AI and data can not only improve bank-customer communication and the overall consumer experience, but can also be deployed to enhance or simplify existing systems and add value to a company\u2019s proposition. Today, innovative startups around the world are developing new, original applications for security<\/a>, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs. <\/p>\n\n\n\n

One example is Trim<\/a>, a startup headquartered in San Francisco that describes itself as a \u201cfinancial health company\u201d with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers\u2019 credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company\u2019s technology has helped users save more than $20 million. <\/p>\n\n\n\n

Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca<\/a> is one of them, delivering predictive platforms for global capital markets which not only helps financial institutions analyze market patterns but also forecast best-value opportunities for clients. In a partnership with Jibun Bank, Alpaca developed an AI-powered solution which alerted customers wanting to use deposits in foreign currency of the most favorable exchange rate in real time. <\/p>\n\n\n\n

Another notable player is Boston-based DataRobot<\/a>, which offers a suite of financial solutions, including ML algorithms that can predict profitable clients. One of their most innovative solutions today leverages machine learning to address a long-standing concern: cash management. While this might seem a brick-and-mortar issue, it is ML-powered predictive models that are helping banks forecast ATM cash requirements and prepare with the precise amount of cash. The company also offers solutions for fraud detection, with real-time machine learning models that can detect potentially fraudulent transactions before they happen, reducing fraud losses and providing a first-class service to clients. 
<\/p>\n\n\n\n


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Hyper-personalization at a glance<\/h5>\n\n\n\n

Key takeaways<\/strong>: tapping the potential of AI and data applications represent a major opportunity for financial institutions to gain insights from their existing data and channel that data it into hyper-personal digital experiences tailored to their customers interests, with applications throughout the customer lifecycle. Successful execution of hyper-personalization adds value to the existing service offering with improved onboarding processes, increased activity from consumers and strengthening of customer loyalty. BCG estimated in 2019 that personalization of the consumer experience could earn banks up to $300 million in revenue growth for every $100 billion held in assets.<\/p>\n\n\n\n

What this means for your business<\/strong>: staying ahead of the curve as you get to know your customer better and then channel those insights and trends into highly-customized digital experience that contribute to activity and trust.  <\/p>\n\n\n\n

What it means for your customers<\/strong>: while users now expect a certain level of customization, hyper-personalized experiences in personal finance can lead to increased satisfaction and engagement, fraud-prevention, better decision-making and a feeling of human understanding from their bank.<\/p>\n\n\n\n


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Finding the right response to industry disruptors<\/h3>\n\n\n\n

In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that \u2013 incumbent. Their positions, though indeed threatened by startups, enjoy their own advantages, possession of a wealth of historical consumer data not least among them. Yet even to define the relationship between early-stage and mature businesses in this way \u2013 by default as one of conflict \u2013 obscures a more complex truth: collaboration, not a competition, is, as it always has been, the more powerful engine of growth and progress. Find out how we can help your company engage with the world's most innovative startups<\/a>. <\/p>\n\n\n\n

LEARN MORE<\/a><\/div>\n","post_title":"Hyper-Personalization: the Next Stage of Digital Banking","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"hyper-personalization-digital-banking","to_ping":"","pinged":"","post_modified":"2021-12-10 07:08:29","post_modified_gmt":"2021-12-10 15:08:29","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/?p=6931","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":527,"post_author":"1","post_date":"2019-04-29 21:38:00","post_date_gmt":"2019-04-30 04:38:00","post_content":"\n

Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.<\/p>\n\n\n\n

As a result of this adoption, Autonomous<\/a> forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.<\/p>\n\n\n\n

1. Signifyd<\/a><\/h2>\n\n\n\n

Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.<\/p>\n\n\n\n

When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.<\/p>\n\n\n\n

2. DataVisor<\/a><\/h2>\n\n\n\n

Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.<\/p>\n\n\n\n

It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.<\/p>\n\n\n\n

3. HyperScience<\/a><\/h2>\n\n\n\n

Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.<\/p>\n\n\n\n

Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.<\/p>\n\n\n\n

4. AppZen<\/a><\/h2>\n\n\n\n

Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.<\/p>\n\n\n\n

To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen\u2019s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.<\/p>\n\n\n\n

5. ZestFinance<\/a><\/h2>\n\n\n\n

ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.<\/p>\n\n\n\n

The startup\u2019s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.<\/p>\n\n\n\n

6. Upstart<\/a><\/h2>\n\n\n\n

Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.<\/p>\n\n\n\n

By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.<\/p>\n\n\n\n

7. Cape Analytics<\/a><\/h2>\n\n\n\n

Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.<\/p>\n\n\n\n

In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.<\/p>\n\n\n\n

8. FutureAdvisor<\/a><\/h2>\n\n\n\n

FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor\u2019s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.<\/p>\n\n\n\n

With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.<\/p>\n\n\n\n

9. Wealthfront<\/a><\/h2>\n\n\n\n

Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.<\/p>\n\n\n\n

By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront\u2019s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.<\/p>\n\n\n\n

10. Ayasdi<\/a><\/h2>\n\n\n\n

Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.<\/p>\n\n\n\n

For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.<\/p>\n\n\n\n

The Next Iteration of AI in Finance<\/h2>\n\n\n\n

AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.<\/p>\n\n\n\n

However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.<\/p>\n\n\n\n


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Navigating FinTech Disruption Executive Immersion Program<\/h3>\n\n\n\n

The Navigating FinTech Disruption<\/a> executive immersion program is a five-day experience where business leaders learn about the latest trends in FinTech, directly from the Silicon Valley insiders. Tailor-made for financial services executives, the program includes 5 days of insightful experiences with the most innovative Silicon Valley companies as well as presentations by experts to provide insights into the impact of technology innovations on finance.<\/p>\n","post_title":"Top 10 Startups in AI for Finance","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"top-10-startups-in-ai-for-finance","to_ping":"","pinged":"","post_modified":"2020-03-02 16:46:46","post_modified_gmt":"2020-03-03 00:46:46","post_content_filtered":"","post_parent":0,"guid":"https:\/\/siliconvalley.center\/blog\/top-10-startups-in-ai-for-finance\/","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}],"next":false,"prev":true,"total_page":6},"paged":1,"column_class":"jeg_col_2o3","class":"epic_block_5"};

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