Machine learning-based profit modeling for credit card underwriting - implications for credit risk

Retail credit issuers traditionally assign credit based on cutoffs from risk-based models. However, in recent years, advances in technology such as AI/ML have given rise to more models that predict more complicated facets of customer behavior, such as projected NPV. These can be used to precisely target profitable but risky customers. Using a unique regulatory panel dataset of credit cards combining data from many major banks, I construct both traditional risk and ML-based profit models and find that profit score cutoffs generally target wealthy, high-spending, “revolving” customers, while risk score cutoffs target low-activity “transacting” customers. Conducting simulations using both types of cutoffs, I find that, absent risk guardrails, profit-based underwriting could potentially cause an increase in riskiness in card portfolios. However, this is highly portfolio dependent and may only occur in those that concentrate on “revolvers” in the lower end of the credit spectrum.