Deep generative models for reject inference in credit scoring
نویسندگان
چکیده
منابع مشابه
Credit scoring and reject inference with mixture models
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 inc...
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Reject inference is the process of estimating the risk of defaulting for loan applicants that are rejected under the current acceptance policy. In this survey article we show how the problem of reject inference can be viewed as one of statistical inference with incomplete data. We use a well known classification of missing data mechanisms into ignorable and nonignorable to organize the discussi...
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We use data with complete information on both rejected and accepted bank loan applicants to estimate the value of sample bias correction using Heckman’s two-stage model with partial observability. In the credit scoring domain such correction is called reject inference. We validate the model performances with and without the correction of sample bias by various measurements. Results show that it...
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Many business elements are used to develop credit scorecards. Reject inference, related to the issue of sample bias, is one of the key processes required to build relevant application scorecards and is vital in creating successful scorecards. Reject inference is used to assign a target class (that is, a good or bad designation) to applications that were rejected by the financial institution and...
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We generalize an empirical likelihood approach to missing data to the case of consumer credit scoring and provide a Hausman test for nonignorability of the missings. An application to recent consumer credit data shows that our model yields parameter estimates which are significantly different (both statistically and economically) from the case where customers who were refused credit are ignored.
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ژورنال
عنوان ژورنال: Knowledge-Based Systems
سال: 2020
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2020.105758