Bound and collapse Bayesian reject inference for credit scoring

G G Chen and Thomas Astebro
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G G Chen: MSCI (Hong Kong) Inc, Hong Kong

Journal of the Operational Research Society, 2012, vol. 63, issue 10, 1374-1387

Abstract: 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.

Date: 2012
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Working Paper: Bound and Collapse Bayesian Reject Inference for Credit Scoring (2010)
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