New applications in AI and big data are enabling the banking industry to offer the hyper-personal digital experiences customers want.
In the age of mobile, a successful digital experience is not just personal but personalized.
A customer unlocks their phone and sees notifications of new releases on their favorite streaming platform based on what they’ve 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 is no exception.
Over the last decade, banking institutions have migrated to digital, 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 to their banking provider be if they received insights and recommendations based on their behavior, cashflow, searches, app usage, location and demographic variables?
Enter hyper-personalization.
Today, machine learning and data analytics 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 with a value proposition that makes customers feel understood. It also promises significant gains, with Boston Consulting Group estimating that successful personalization at scale could represent an increase of 10% in a bank’s annual revenue.
Read on to learn more about how fintechs are harnessing the potential of AI and data to create valuable offerings solutions for the age of digital innovation.
Building customer relations with data
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
Banking of the Future: Finance in the Digital Age – HSBC, 2019.
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.
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 report already showed that 40% of customers would remain loyal to their banking provider with a more personalized service.
One such company is Crayon Data. 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’s 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.
Other startups are now helping financial companies optimize their existing data by integrating new sources of information to better understand their client’s current needs and even predict their future ones. Take Canadian startup Flybits, 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.
Some companies are even innovating on channels for communication with consumers and delivery of digital experiences. United Arab Emirates startup BankBuddy, 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.
Artificial intelligence, intelligent applications
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.
The Future of Retail Banking Report – MarketForce, 2017
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’s proposition. Today, innovative startups around the world are developing new, original applications for security, savings, transactions, fraud detection and even cash flow, all of them tailored to specific needs.
One example is Trim, a startup headquartered in San Francisco that describes itself as a “financial health company” with the mission of tackling spending. By deploying an AI assistant, Trim analyzes customers’ credit card usage, shows them how much is being spent on services and then simplifies the process of cancelling them. To date, the company’s technology has helped users save more than $20 million.
Other companies are leveraging real-time personalized metrics to provide recommendations for what customers need. Japanese startup Alpaca 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.
Another notable player is Boston-based DataRobot, 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.
Hyper-personalization at a glance
Key takeaways: 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.
What this means for your business: 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.
What it means for your customers: 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.
Finding the right response to industry disruptors
In all the excitement over all that is new, it is easy to forget that industry incumbents remain just that – 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 – by default as one of conflict – 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.
