Dynamic Valuation of Delinquent Credit-Card Accounts

This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent credit‐card accounts. Based on this model, we construct a dynamic collectability score (DCS) that estimates the account‐specific probability of collecting a given portion of the outstanding debt over any given time horizon. The model integrates a variety of information sources, including historical repayment data, account‐specific, and time‐varying macroeconomic covariates, as well as scheduled account‐treatment actions. Two model‐identification methods are examined, based on maximum‐likelihood estimation and the generalized method of moments. The latter allows for an operational‐statistics approach, combining model estimation and performance optimization by tailoring the estimation error to business‐relevant loss functions. The DCS framework is applied to a large set of account‐level repayment data. The improvements in classification and prediction performance compared to standard bank‐internal scoring methods are found to be significant. This paper was accepted by Noah Gans, stochastic models and simulation.