Bound and Collapse Bayesian Reject Inference for Credit Scoring

Thomas Astebro and Gongyue Chen
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Gongyue Chen: Department of Management Sciences - University of Waterloo [Waterloo]

Working Papers from HAL

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.

Keywords: Credit scoring; reject inference; missing not at random; Bayesian inference (search for similar items in EconPapers)
Date: 2010
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Published in 2010

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Journal Article: Bound and collapse Bayesian reject inference for credit scoring (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-00655036

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