Forecasting and Stress Testing Credit Card Default Using Dynamic Models

Typically models of credit card default are built on static data, often collected at time of application. We consider alternative models that also include behavioural data about credit card holders and macroeconomic conditions across the credit card lifetime, using a discrete survival analysis framework. We find that dynamic models that include these behavioural and macroeconomic variables give statistically significant improvements in model fit which translates into better forecasts of default at both account and portfolio level when applied to an out-of-sample data set. Additionally, by simulating extreme economic conditions, we show how these models can be used to stress test credit card portfolios.