Bound and collapse Bayesian reject inference for credit scoring
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- Chen, G. & Åstebro, T. J Oper Res Soc (2012) 63: 1374. doi:10.1057/jors.2011.149
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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.