The legacy cafeteria model of financial services offered by banks -- using the originate-to-distribute approach to spread risk and reduce financing costs -- isn’t working anymore. Customers no longer seek the single source of products their neighborhood banks provide, but instead are shopping for specific services that meet well defined needs, such as in-depth analytics, complex decision support, and social demands for equitable credit access.

To make matters worse, a bevy of fintech startups now offer just these services, and they do it à la carte, with sophistication and efficiency. This puts pressure on the profit models of legacy institutions. Where banks previously enjoyed a wide range of ready in-house borrowers -- giving banks latitude in choosing and managing credit exposure risks -- today’s financial customers are shopping across competitors for each financial product.

When banks can’t meet their customers’ changing needs, those customers will shop around, and find plenty of new fintech alternatives. New apps and online services let customers move individual products to a competitor, and often you, as their banker, never realize it’s happened until it is too late. Even when legacy banks do manage to win new customers, those clients come without the known financial history needed for reliable ROI forecasting, increasing risk with no corresponding gain.

To make sound business decisions in the new fintech world -- balancing risks, returns, and obligations -- banks need actionable insights drawn from across lines of business to truly understand the profiles, needs, and predictable behaviors of their customers.

Banks need a digital banking platform -- a set of cohesive, integrated analytical tools -- to crank through data in real time, apply decision-making algorithms, and automate processing going forward. Such a toolset lets you unleash the power of analytics and artificial intelligence (AI) to enable smarter business decisions at scale, including:

Simulating End-to-End Outcomes

In a digital customer environment, you need to put aside legacy product lines and think about your broader portfolio, putting the consumer first: looking at new variables, such as income verification, customer digital footprints, and your own previously ignored data, such as checks written to outside investment firms. You need to understand how and where consumers are spending their time, in order to understand what they are actually buying.

A key differentiator for this new analytics approach is simulation: producing an action-effect model that lets you predict the performance of proposed risk and exposure models, and then modify build automated tools that implement the model. This model incorporates all these new data streams, in order to identify hidden relationships and new cause-and-effect scenarios.

Consider a simple credit card portfolio, which has a certain yield today for a specific risk. You want to know if increasing the risk by X provides >X ROI to mitigate the higher risk. Simulation answers that question. That answer leads directly to a decision to accept the greater risk, and a feedback loop to monitor the resulting ROI to ensure it matches the simulation model.

Winning

Modern digital banking platforms let you get customer-centric today, with AI-driven simulation, decision-making, and automated optimization tools that jumpstart and your transition from brick and mortar to multichannel, digital products.

For a detailed look at FICO’s Decision Management digital banking platform, see our FICO product page.

Bill Waid is General Manager of the FICO Decision Management line of business, which builds and delivers analytics, decisioning and optimization solutions that help enterprises identify and take action on unique, predictive insights, in real-time. Bill joined FICO in 2002 to lead the formation of the Decision Management business, now one of FICO’s largest and fast-growing offerings, serving thousands of customers in a wide range of industries across the globe.