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Using data a key element of today's efforts
This is bonus coverage from “Adventures in Collections” from the December 2014 issue of Credit Union Management magazine.
People who expect collectors to be rough-and-ready, forceful people may be surprised to discover that today’s collectors are more likely to be geeks than cops.
“They are experts at using big data and predictive models to determine scientifically where collection efforts will be most productive,” says Eric Snyder, chief business development officer at Akcelerant Software, Malvern, Pa., which supplies CUs with collections software.
By any name, collections used to be simpler. “We have bigger fraud to deal with now,” notes David Tuyo, II, CCE, EVP/chief financial officer of $525 million Power Financial Credit Union, Pembroke Pines, Fla. “Twenty years ago, we were more SEG focused and members enjoyed a greater understanding of what a financial cooperative represents. When we became community chartered CUs, we had to learn to assess risk and collect data in different ways. It’s more complex now, but also more fun.
“We are becoming increasingly more creative in leveraging data and resources to find solutions for our members while at the same time weeding out the abusers,” Tuyo continues. “You have to determine whether the situation causing the delinquency is temporary or permanent, and whether the borrower even wants a solution. We’re still working through a lot of ‘strategic’ defaulters in South Florida; some are older than five years.”
Here’s an example of data in action for collections purposes. A member of $1.1 billion Arizona Federal Credit Union, Phoenix, stopped making payments on an aging automobile. When the CU refused to accept a low offer to release the title, contact was broken off, recalls Eric Givens, the CU’s director of risk mitigation.
But to try to effect a short sale, the owner had taken pictures of the alleged “wreck” and sent them to the $1.1 billion CU, which showed the photos to its in-house skip-tracer, who thought he recognized a fence in the background of the photo. Using Google maps (first aerial view, then street view), the skip-tracer located the car, and the CU sent a repo firm to pick it up. They sold it for about 55 percent of the outstanding loan value.
Richard H. Gamble is a freelance writer based in Colorado.
