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Bound and Collapse Bayesian Reject Inference for Credit Scoring

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.
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https://hal-hec.archives-ouvertes.fr/hal-00655036
Contributor : Antoine Haldemann <>
Submitted on : Sunday, December 25, 2011 - 6:25:35 PM
Last modification on : Thursday, January 11, 2018 - 6:19:31 AM

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  • HAL Id : hal-00655036, version 1

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Thomas B. Astebro, Gongyue Chen. Bound and Collapse Bayesian Reject Inference for Credit Scoring. 2010. ⟨hal-00655036⟩

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