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Regulators have had their eye on credit risk for some time, but with several rate increases forecast for 2017, bankers may do well to invest in business intelligence and data analytics tools to better mitigate that risk and root out fraud in a rising rate environment.

The story, by now, should sound familiar: The prolonged low interest rate environment, combined with tightening regulations on residential mortgage lending, has led many banks to “reach for yield” on the commercial side of the house. One only need listen to quarterly earnings calls to hear bankers talk in vaguely ominous tones about the loosening of covenants and high loan-to-value ratios.

The OCC recently raised the issue in its latest Semiannual Risk Perspective, pointing to growth in commercial real estate and easing of underwriting standards and highlighting the need for concentration risk management. That takes on a new relevance now that the Fed has indicated further interest rate increases may be on the horizon this year.

And as rates continue their rise to normalcy, or something like it anyway, commercial borrowers are going to largely fall into three categories, said David O’Connell, senior analyst at Aite Group.

“There will be the kind that are still money good with their current deal and they’ll be fine. There will be the kind that are not money good with their existing credit provider because they’re too risky and they will have to shop around for a new deal. The third kind are the more marginal creditors that are just plain in trouble and will have a hard time finding a new credit provider,” he said.

On the whole, though, we seem to have mostly learned our lesson from the Great Recession, said Robert Ashbaugh, a senior risk consultant with the financial information company Sageworks. The environment a decade ago was almost a perfect storm, he said. While banks today might wiggle on covenants or pricing to gain an edge over a competitor down the street, they’re doing it more prudently than in the past.

“I think the banks that do well will be the ones that really keep the focus on the credit,” Ashbaugh said. “They may be loosening the standards a little bit, but they’re still managing their exceptions, they’re still making credit a priority. The ones that do that will be the ones that survive.”

A Greater Need For Data Analytics

But as rates rise and banks compete for those few good credits in the market, experts say business intelligence and data analytics tools will be ever more necessary for a range of functions.

“[Data is] probably one of their more important assets that they’re starting to come to grips with. A lot of banks are really starting to stress test their portfolio using their data,” Ashbaugh said. “They may not necessarily be required to do it but they’re realizing that it’s an advantage to do that.”

Data can be especially useful for sniffing out which borrowers might be in danger of defaulting, but with loan originators and underwriters largely occupied with the next or current deal, bankers are going to need automated systems that can make those connections and push out alerts to lenders.

That’s where companies like LexisNexis can play a role. Ben Cutler, senior director of small business risk at LexisNexis Risk Solutions, said the company has focused in recent years on collecting alternative data from more than 13,000 sources. Lenders – ranging from local credit unions to small banks to big banks to alternative lenders – use that information to better inform their decisions about businesses’ creditworthiness.

“It’s kind of like small footprints,” he said. “A small business does a lot of things that just don’t show up in a credit report and it leaves these footprints out in the data environment that indicate its existence, that indicate that it’s operating.”

Those footprints are also going to be increasingly useful for rooting out fraud, too. Cutler gives the example of a particular storage unit in Texas that has more than 1,000 names of individuals associated with it. An automated system can catch that discrepancy more easily than a data analyst poring over that information manually.

“Banks are going to have to be really sharp with their monitoring of all things regarding their borrowers and they will need to use business intelligence to look for patterns, maybe patterns of an unknown nature,” O’Connell said. “We all know that a borrower who is late giving you their financial statements three times in a row is at a higher risk of defaulting. We will need business intelligence that will catch that – because the human eye can’t.”