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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 © 1999 John Wiley & Sons, Ltd.

Suggested Citation

  • A.J. Feelders, 1999. "Credit scoring and reject inference with mixture models," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 8(4), pages 271-279, December.
    • Handle: RePEc:wly:isacfm:v:8:y:1999:i:4:p:271-279
    DOI: 10.1002/(SICI)1099-1174(199912)8:43.0.CO;2-P
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    References listed on IDEAS

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    1. 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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