Regional and community banks are struggling with growth and profitability in the face of competitive pressure from large national banks and fintech startups. Executives at these institutions are instructed to invest in technology, and to leverage data and artificial intelligence to compete more effectively.
While that sounds good, smaller banks are often constrained by a dependence on legacy core vendors that limits their IT potential, encounter difficulties in accessing their own data, lack skilled data scientists, and have no clear vision on where to start.
This conundrum came up during Bank Director’s 2020 Acquire or Be Acquired conference in Phoenix. I rubbed shoulders with fellow conference attendees over the course of three days, sharing ideas about the state of the banking sector and how community banks could leverage data and AI to drive business results. The talent gap was a frequent topic. Perhaps unsurprisingly, only a miniscule number of community banks have hired data scientists. The majority of banks have not prioritized data science capabilities; the few who are actively recruiting data scientists are struggling to attract the right talent.
But even if community banks could arm up with data scientists, what volume of data will they be working on to derive insights to fuel their business strategy? A $1 billion asset bank may have 50,000 to 75,000 customers — not a lot of data to start with. Furthermore, a number of bankers point to the difficulties they encounter in accessing their data in their legacy core systems.
As we were having these discussions, conversations were raging about the need for smaller banks to prepare for an existential threat. At the World Economic Forum in Davos, attendees were assessing comments from Bank of America Corp. Chairman and CEO Brian Moynihan that the bank could double its U.S. consumer market share. Back-of-envelope calculations indicate that if Bank of America manages to accomplish that growth, more than 1,000 community banks could be out of business. Can technology enable these banks to retain their core customer base, grow and avoid this fate? I think so.
One promising area of AI application for community banks is loan and deposit pricing. Community banks have little or no analytic tools beyond competitive rate surveys; most rely on anecdotal feedback from customers and front-line bankers. But price setting and execution on both assets and liabilities is one of the most important levers a bank can use to drive both growth and improve its net interest margin. Community banks should take advantage of new tools and data to level the playing field with the big banks, which are already well ahead of them in adopting price optimization technology.
Small banks can gain the upper hand in this “David versus Goliath” scenario because accessible cloud-based technology works in their favor. True, big banks have worked with price optimization technology and leveraged large amounts of customer behavioral data for years. But community banks tend to have stronger customer relationships and often better pricing power than their larger competitors. Now is the time for community banks to take control of their destiny by adopting new technology and tools so they can better compete on price.
There are three reasons why cloud computing and the power of AI will be the slingshot of these ‘David’ banks:
- Scalable computing power, instantly on tap. Cloud-based computing and pre-configured pricing solutions are affordable and can be implemented in days, not months.
- Big data — as a service. Community banks can quickly leverage AI-based pricing models that have been trained on hundreds of millions of transactions. There is no need to build their own analytic models from a small customer footprint.
- Plug-and-play IT. It’s much easier today to integrate cloud-based platforms with a bank’s core system providers, which makes accessing their own data and implementing smarter pricing feasible.
Five years ago, it would have seemed crazy to think that in 2020, community banks would be applying AI to compete against the nation’s top banks. But the first wave of early adopters are already deploying AI for pricing. I predict we’ll see more institutions embracing AI and machine learning to improve their NIM and increase growth over the coming years.