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Bound and collapse Bayesian reject inference for credit scoring

Author

Listed:
  • G G Chen

    (MSCI (Hong Kong) Inc, Hong Kong)

  • T Åstebro

    (HEC Paris, Jouy-en-Josas, France)

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.

Suggested Citation

  • G G Chen & T Åstebro, 2012. "Bound and collapse Bayesian reject inference for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(10), pages 1374-1387, October.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:10:p:1374-1387
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    Cited by:

    1. Marra Giampiero & Radice Rosalba, 2017. "A joint regression modeling framework for analyzing bivariate binary data in R," Dependence Modeling, De Gruyter, vol. 5(1), pages 268-294, December.
    2. Zhiyong Li & Xinyi Hu & Ke Li & Fanyin Zhou & Feng Shen, 2020. "Inferring the outcomes of rejected loans: an application of semisupervised clustering," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 631-654, February.
    3. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    4. Mengnan Song & Jiasong Wang & Suisui Su, 2022. "Towards a Better Microcredit Decision," Papers 2209.07574, arXiv.org.
    5. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.
    6. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    7. Rogelio A. Mancisidor & Michael Kampffmeyer & Kjersti Aas & Robert Jenssen, 2019. "Deep Generative Models for Reject Inference in Credit Scoring," Papers 1904.11376, arXiv.org, revised Sep 2021.

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