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Credit scoring and reject inference with mixture models

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  • A.J. Feelders

Abstract

Reject inference is the process of estimating the risk of defaulting for loan applicants that are rejected under the current acceptance policy. We propose a new reject inference method based on mixture modeling, that allows the meaningful inclusion of the rejects in the estimation process. We describe how such a model can be estimated using the EM‐algorithm. An experimental study shows that inclusion of the rejects can lead to a substantial improvement of the resulting classification rule. Copyright © 2000 John Wiley & Sons, Ltd.

Suggested Citation

  • A.J. Feelders, 2000. "Credit scoring and reject inference with mixture models," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(1), pages 1-8, March.
    • Handle: RePEc:wly:isacfm:v:9:y:2000:i:1:p:1-8
    DOI: 10.1002/(SICI)1099-1174(200003)9:13.0.CO;2-#
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    References listed on IDEAS

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    1. William H. Greene, 1992. "A Statistical Model for Credit Scoring," Working Papers 92-29, New York University, Leonard N. Stern School of Business, Department of Economics.
    2. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
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    Cited by:

    1. Mengnan Song & Jiasong Wang & Suisui Su, 2022. "Towards a Better Microcredit Decision," Papers 2209.07574, arXiv.org.
    2. Salihu, Armend & Shehu, Visar, 2020. "A Review of Algorithms for Credit Risk Analysis," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, pages 134-146, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    3. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.
    4. Qiang Liu & Yingtao Luo & Shu Wu & Zhen Zhang & Xiangnan Yue & Hong Jin & Liang Wang, 2022. "RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring," Papers 2206.00568, arXiv.org.

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