Why Community Banks should focus on Data Rich Businesses for Future?

Jerry Duan, Vice President, Director of Treasury Analytics & Stress Testing , United Community Bank [NYSE: UCB]

Jerry Duan, Vice President, Director of Treasury Analytics & Stress Testing

Community banks and large banks offer many of the same financial products and services, including bank accounts, personal and business loans, debit and credit cards, etc. Community banks gain their reputation by offering more personal customer support, face-to-face interaction with customers and more customized products. To offer customized products, community banks need to have better knowledge about their customers. The secret sauce of maintaining this knowledge over time and being able to transfer the knowledge among their employees can often attribute to their customer service culture and lower staff turnover, community banks tend to hire locally and serve local customers.

However, community banks have been losing this advantage in recent years as young generations bank online more and prefer less face-to-face interactions. Knowledge about customers may become difficult to attain through the traditional way. On another side, to be able to offer customized products, community banks need to more accurately price the risks embedded in customized products, given the fact that most community banks rely on balance sheet (loans and deposits) to generate their main income source, net interest income. Net interest income, in its nature, is the return to the banks by taking various risks, including credit risk, interest rate risk and liquidity risk. For example, banks get paid interests by taking credit risks from borrowers and by taking interest rate risk if the borrowers prepay when interest rates decrease. Banks are able to get cheap funding from depositors while taking liquidity risks as depositors can withdraw their money anytime they want.

Here the challenge comes. Toget returns by taking risks, community banks need to price risks more accurately, while their traditional way to get knowledge of customers to enable them to price more accurately is losing advantage. Also, community banks in general have disadvantages over large banks as they don’t have the same level of diversity as large banks do. Diversity is a free launch and community banks have smaller and less diversified customer base.

"Investment in talents in data analytics and machine learning should allow community banks to turn being data-rich toinformation-rich"  

To maintain competitive advantages, community banks should focus on data rich businesses to gain and retain knowledge about customers, along with strongcustomer service culture, to better price the risks and serve customers. Generating data about customers should be another dimension that community banks consider in making decisions and allocating resources. Businesses that can bring more data about customers including credit card, debit card and mobile banking should be favored. Many community banks have been developing mobile banking platform in recent years. However, less focus has given to promote debit card and credit card usage, especially credit card.

Many community banks don’t carry credit cards on balance sheet, even though community banks offer credit cards but the services are actually provided by other banks. Those community banks don’t own the credit card relationship and therefore don’t get transaction data about customers. One reason community banks don’t carry credit cards on balance are some unfavorable accounting and regulatory treatment which makes carrying credit card on balance expensive.

Community banks should consider the ability of generating customer data as one of the dimensions in determining business profitability in order to maintain their competitive advantages over large banks.

For the Road Ahead

Being data rich doesn’t necessarily mean being information rich. How to use data to generate useful information for decision making is critical. Community banks in the past had large disadvantages over bigger banks due to the lack of scale of economy in IT infrastructure. Things have changed in recent years. Cloud solution providers can solve data storage and computation problems, and open source machine learning libraries and many trained machine learning models became available. Many large banks also leverage Cloud and the open source libraries and models for advanced analytics. Community banks don’t need a heavy investment upfront anymore. Investment in talents in data analytics and machine learning should allow community banks to turn being data-rich to information-rich.

Machine Learning

Financial

IT Infrastructure

Storage