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New model predicts delinquent card repayment

50% more accurate, claim university developers

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New model predicts delinquent card repayment

University researchers claim to have identified a way to accurately predict which delinquent credit card accounts will repay an outstanding balance. This could help decisionmakers choose among methods of recovering past dues and chargeoffs.

The method is called the Dynamic Collectability Score, which ranks delinquent account holders based on such factors as size of outstanding balance; mortgage status; past payment history; credit score; and external factors such as the performance of the stock market and the national unemployment rate.

As designed, the score continually adjusts as these variables change to provide a real-time prediction that developers say is up to 50% more accurate than banks’ current scoring systems.

The new approach was developed at the McCombs School of Business at the University of Texas at Austin by Assistant Professor Naveed Chehrazi and his co-author Thomas Weber from École Polytechnique Fédérale de Lausanne in Switzerland, working with an unnamed credit card issuer.

“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 that the bank receives—is factored in to revise, up or down, that person’s likelihood of future payment.” That information, in turn, improves the scoring system’s accuracy. No other current scoring system is capable of this, Chehrazi explains.

How new methodology could help banks

Banks can calculate the likelihood that a credit card account will go into default. However, once an account is delinquent, they only have a weak guess about who is most likely to pay back the debt.

Once an account is significantly past due, banks may involve a third-party collection agency to attempt to recover payment, but that strategy can be costly. Banks may sell off the debt for pennies on the dollar or pay hefty commission rates to recover only a small portion of the debt owed.

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 account.

Aid in setting capital levels

The Dynamic Collectability Score also helps banks better determine their credit risk capital requirements, or the amount of money they need to have in reserve to cover future unpaid accounts, known as Loss Given Default.

Current estimation methods can result in projections 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.

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

Chehrazi and his co-author are in the process of expanding the Dynamic Collectability Score further so that banks can also calculate the optimal rate to pay collection agencies depending on each account’s repayment score.

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