Bound and collapse Bayesian reject inference for credit scoring

Reject inference is a method for inferring how a rejected credit applicant would have behaved had credit been granted. Credit‐quality data on rejected applicants are usually missing not at random (MNAR). In order to infer credit‐quality data MNAR, we propose a flexible method to generate the probability of missingness within a model‐based bound and collapse Bayesian technique. We tested the method's performance relative to traditional reject‐inference methods using real data. Results show that our method improves the classification power of credit scoring models under MNAR conditions.