How to use more data for fewer defaults

Community banks are analyzing the use of a broader set of data to better determine credit risk of borrowers. The result: more consistent lending practices and fewer defaults.

By Karen Epper Hoffman

In this hyper-competitive financial marketplace, one of the critical advantages for community banks is that they have long been the lenders of choice for individuals and small businesses who require a more qualitative assessment of their borrowing risk.

However, taking a page from more tech-focused marketplace lenders and bigger banks, many community institutions are seeing the value in using a broader set of data analysis beyond the basic credit scores and asset review they have looked to historically. Forward-looking community bank lenders are harvesting vast quantities of consumer data from public information, as well as from applications, to assess would-be borrowers’ creditworthiness. 

Take, for example, Sunrise Banks, an $800 million-asset community bank based in St. Paul, Minn. It has not replaced its qualitative, work-with-the-customer approach to lending. Rather, it has coupled it with a newer focus on outside data and analysis.

“Many community banks use their own internally generated data, such as past due reporting, and customer-generated financial statements to inform credit decisions,” says David Reiling, CEO of Sunrise Banks. “This data can be supplemented with various resources such as newspapers or journals or media coverage about the customer, to gauge the credit risk for individual customers as well as a picture of the larger loan portfolio.”

Who dares, wins
What’s driving community banks to enhance their credit risk analysis? It’s not only a more competitive (and increasingly crowded) lending market—with fintech lenders, mega-banks and credit unions all trying to shove their way to the trough of good borrowers—but also the heightened need to remain compliant, say banking experts. Credit risk is still the top priority for federal banking regulators, cited as the number-one matter requiring attention (MRA) at community banks, according to a release last year from the Office of the Comptroller of the Currency.

With the market trending in recent years toward looser underwriting standards, many lenders are pushing the credit risk boundaries more than they have in a dozen years to win business in high-growth loan products, remain competitive with rival lenders and attract more highly educated borrowers. The result: Community banks that want to push forward need more than just a credit score and a gut check to best calculate how risky each loan may be.

Sources of data
Traditionally, community banks have used two main information sources to evaluate credit risk, according to Priyanka Prakash, a writer and lending expert for Fundera. First, she says, they have relied on traditional credit data, “similar to what large national banks use in their underwriting processes. It’s almost impossible to find a community bank that won’t pull a prospective borrower’s credit report and check their credit score, credit utilization and payment history.”

Secondly, they use their personal knowledge of the borrower and the community.

“It’s here that community banks really have a leg up over the national bank chains,” says Prakash. “Community banks have often been in the same location for dozens of years and have a repository of valuable knowledge about the neighborhood, primary employers and businesses in the area, the location of the most desirable schools and residential areas. All of this can help a community bank evaluate risk.”

However, the traditional community bank approach, while effective in many ways, tends to be broader and less analytical, and it can produce uneven results, according to Jeff Hall, senior vice president of quantitative products for CRMa. “The biggest issue we see is an untapped potential here to use risk grades,” Hall says, adding that such grades, based on more holistic data analysis, would allow community banks to more accurately assess risk and price loan products better. “In many cases, the pricing has been more or less based on intuition,” he adds. “If you don’t differentiate clearly, you end up self-selecting the lower-tier clients and drifting into riskier loans.”

Chris Henkel, senior director at Moody’s Analytics, has seen more community banks collecting a broader array of borrower data and analyzing it by group as well as by individual. In this way, they can more easily see how certain financial signs can link to specific credit events and better predict which borrowers are likely to pay back their loans. “Are there certain financial ratios that are linked with past-dues or charge-offs? Of course there are,” Henkel says.

While community banks do not always have the resources to employ a full-time data scientist or analyst, bank service providers are stepping up. Experian, for example, recently launched a “tri-bureau leveled trended attributes” product that blends data from all three credit bureaus to help lenders create a picture of consumer risk over time, rather than at a single moment.

In the past five or six years, Henkel adds that banks have begun embracing a more analytical and granular approach to credit risk in order to comply with the Current Expected Credit Loss (CECL) standard. In the wake of the financial crisis and the flood of new (often risk-focused) regulation to which they must now adhere, many banks quickly realized that “if they had to collect more data anyway, they could also do more with it,” he says. “They wanted to do things differently.”

Karen Epper Hoffman is a writer in Washington state.