Letting Banks Know Who’s Got the Power to Pay

Each year in the U.S., more than $15 billion of overdue credit card debt goes into collections. But it doesn’t have to.

Based on the research of Naveed Chehrazi

Banks can calculate the likelihood that a credit card account will go into default, but once delinquent, they have only a poor guess of who is most likely to pay back the debt. Once an account is significantly past due, banks often involve a third-party collection agency to attempt to recover payment.

That move, however, can be costly. Banks either sell off the debt for pennies on the dollar or pay hefty commission rates to recover only a small portion of the debt owed.

“The credit card collection problem is very complicated,” says McCombs Assistant Professor Naveed Chehrazi, “and current bank-internal scoring systems are surprisingly poor in predicting repayment behavior, given the amounts that are at stake.”

Banks need to know which accounts are worth spending money on — whether sending them to a collection agency, filing a lawsuit, or taking no action whatsoever — based on the likelihood of repayment and the amount they can expect to recover. Having that information would influence the strategy a credit card issuer follows for each individual account. Until now, that model didn’t exist.

Chehrazi and colleague Thomas Weber from École Polytechnique Fédérale de Lausanne in Switzerland worked directly with a major credit card issuer to develop the Dynamic Collectability Score. It ranks delinquent account holders based on factors such as size of outstanding balance, mortgage status, past payment history, and credit score as well as external factors such as stock market performance and current unemployment rates. Even an indication that a cardholder is willing to pay but simply unable to changes how an account is ranked and what a bank’s next steps should be.

More important, the DCS continually adjusts as these variables change to provide a real-time prediction that is up to 50 percent more accurate than banks’ current scoring systems.

“Any new piece of information that comes in is going to change the prediction of the model,” Chehrazi says. “Each action that’s taken — from a collection phone call that goes unanswered to a partial payment the bank receives — is factored in to revise, up or down, that person’s likelihood of future repayment.” That information, in turn, improves the scoring system’s accuracy. No other current scoring system is capable of this, he says.

Practical Implications

Banks need accurate repayment forecasts, and the DCS gives them the probability of receiving repayment, both individually and as an aggregate portfolio of accounts, over a defined period of time. Moreover, the DCS can predict how much money above a certain threshold a bank can expect to receive, which helps determine how to proceed through the collection process.

By continually re-evaluating an account holder’s delinquency status, banks can modify their approach. They can use the DCS as a tool to optimize commission rates paid to outside agencies (too high, and the bank takes a hit; too low, and the collection agency may not prioritize the account) or to determine the best settlement amount to offer a credit card holder instead of allowing an account to languish in collections indefinitely.

Just as important, the DCS helps banks better determine their credit risk capital requirements — the amount of money they need to have in reserve to cover future unpaid accounts, known as Loss Given Default. Current methods used to assess that amount are static and can produce estimates that are either too low or too high, costing banks potentially a significant sum either through uncovered credit risk or increased cost of capital. Using Chehrazi and Weber’s Dynamic Collectability Score could ensure banks are adequately meeting their capital risk requirements established under the Basel II Accord.

For more about this research, read the UT News Wire press release.

“Dynamic Valuation of Delinquent Credit-Card Accounts” was published in the September 2015 issue of Management Science.

Story by Adrienne Dawson