Sample selection bias in acquisition credit scoring models: an evaluation of the supplemental-data approach

Irina Barakova, Dennis Glennon and Ajay Palvia

Abstract

ABSTRACT

Models evaluating credit applicants rely on payment performance data, which is only available for accepted applicants. This sampling limitation could lead to biased parameter estimates. We use a nationally representative sample of credit bureau records to examine sample selection bias in account acquisition scoring models and to evaluate the effectiveness of the industry practice of using proxy payment performance for rejected applicants. Our results show that ignoring the rejected applicants significantly affects forecast accuracy of credit scores, while it has little effect on their discriminatory power. Finally, we document that validating scores only on accepted applicants can be misleading.