Bound and Collapse Bayesian Reject Inference for Credit Scoring
33 Pages Posted: 22 Aug 2004 Last revised: 3 Mar 2013
Date Written: July 1, 2010
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.
Keywords: Credit scoring, reject inference, missing not at random, Bayesian inference
JEL Classification: C12, C15, C52
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