Credit Scoring Models Getting Analytical Adjustments

Ivan Schneider
November 2, 2004

September 11 changed everything -- including banks' loan portfolios.

"After 9/11, the whole economy changed," says Christine Pratt, analyst at TowerGroup (Needham, Mass.). "The mix in the portfolios changed, the borrowers changed and the risk in the borrowers changed."

While some borrowers battened down the hatches, others took on greater risks. But banks' risk models had been calibrated during better economic times, and they did not instantly adapt to the new reality. "People began to realize that the scoring models didn't work as well as they thought they did," says Pratt. "There has been much more of an impetus to make sure that these models are constantly tested, refreshed and refined now."

Part of the solution has come through better capturing of information that the firm knows about its customers. "I've seen a nice uptick in the number of organizations that are looking for new collections and recovery systems, or are looking for tools to help them segment their servicing portfolios, so that they can get a better handle on what's going on," says Pratt. "People have started to say, 'We've had this same collections and recovery system for years now -- it's about time we invested in some new systems."

For instance, Intelligent Results (Bellevue, Wash.), offers a solution that incorporates unstructured data gleaned from collections agents. Banks can use this information to decide whether to sell the loan to a collection agency or hold it in the hope that it turns back into a conforming loan. "Until now, most of that information has been resident in the collectors' notes," says Pratt.

A good collections and recovery system "can actually predict the right treatment for each customer," notes Pratt. The same way that a CRM system can predict which offer might be appropriate for a given customer, collections and recovery systems can incorporate analytics to determine whether it's worthwhile to work out a payment arrangement with a customer, and at what terms.

Also, at least one major credit organization has begun to use analytics to figure out which customers are in danger of struggling to make payments. "They've used that information to start a proactive customer approach," relates Pratt.




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