Lean Models and Reject Inference

Credit scoring models are normally built using only applicants who have been previously accepted for credit. Such non-random sample selection may produce bias in estimated model parameters and accordingly model predictions of repayment performance may not be optimal. Previous empirical research suggests that omission of rejected applicants has a detrimental impact on model estimation and prediction., This paper explores the extent to which the number of included variables influences the efficacy of a commonly used reject inference technique, reweighting. Analysis benefits from availability of a rare sample where virtually no applicant was denied credit. The general indication is that the efficacy of reject inference is little influenced by either model leanness or interaction between model leanness and the rejection rate that determined the sample. However, there remains some hint that very lean models may benefit from reject inference where modelling is conducted on data characterized by a very high rate of applicant rejection.