| Commercial banks are good at gathering data points and performing due diligence on commercial loans, but may not be as adept at putting proprietary data to good use. And if commercial bankers analyzed their data like marketing specialists do, they might be able to realize significant improvements in their rates of loan losses.
Those are the findings of “Reducing Commercial Loan Losses: If You’ve Got It, Analyze It,” a study released May 5 by David O’Connell, senior analyst in wholesale banking at Aite Group, an independent research and advisory firm. He polled 100 commercial bankers regarding their data-gathering methodologies. While they knew how to gather data points, perform due diligence and incorporate the findings of ratings agencies, they were less adept at evaluating the proprietary data sets that could be used to predict future loan performance.
The result: When asked what percentage of loan losses in the previous 12 months could have been avoided if proprietary credit data had fully taken into account, 51 percent of the respondents said that between 10 and 30 percent of losses could have been reduced or avoided. Twenty-four percent said that up to 10 percent could have been reduced or avoided.
Beyond the balance sheet, less high-profile data such as timely submission of financials and loan officer comments sometimes dropped below the radar; the bankers tended not to be incorporating that data. O’Connell’s study calls for more attention paid to credit upgrades and downgrades, and any dilution of assets. “I’m certain there are opportunities in [proprietary] data for banks to predict credit deterioration sooner,” he says.
An Ounce Of Prevention
Definitions of even basic items such as nonperformance criteria can lead to disparities. The lack of uniform semantics is not something that regulation would necessarily be able to fix, O’Connell says; financial institutions should come to an internal consensus on what uniform standard to adopt.
The compartmentalization of knowledge, combined with fragmented data sets that may exist on several different platforms, can make it difficult to aggregate information into a meaningful evaluation tool.
Large public banks have extremely granular footnotes, O’Connell says; based on what the bankers he polled told him, he was able to evaluate the amount of annual writeoffs of the commercial lending portfolios of three large banks to figure out how much lower their losses would have been – a 10 percent reduction on chargeoffs to net income.
“Loan workouts provide a particularly rich data set to examine,” O’Connell says – “which go to workout, which succeed and which fail, [providing a way] to identify best and worst practices.”
Email: coneill@thewarrengroup.com
