Credit scoring and reject inference with mixture models

A.J. Feelders

Intelligent Systems in Accounting, Finance and Management, 2000, vol. 9, issue 1, 1-8

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.

Date: 2000
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