Article1999

Credit scoring and reject inference with mixture models

61 citations0/0 files storedIntelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management

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Financial Distress and Bankruptcy Prediction Imbalanced Data Classification Techniques Bayesian Methods and Mixture Models

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.

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1989An econometric analysis of the bank credit scoring problem 1978Credit Scoring and the Equal Credit Opportunity Act

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2020Deep generative models for reject inference in credit scoring 2018A rejection inference technique based on contrastive pessimistic likelihood estimation for P2P lending 2019A Novel Reject Inference Model Using Outlier Detection and Gradient Boosting Technique in Peer-to-Peer Lending 2005Reject inference in credit operations based on survival analysis 2010Mining the Banking Customer Behavior Using Clustering and Association Rules Methods 2009Reject inference in survival analysis by augmentation 2005Improving the Comparability of Insolvency Predictions 2013Selection bias in credit scorecard evaluation

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