Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring.
Banks decide whether to grant credit to new applications as well as how to deal with existing customers, e.g. deciding whether credit limits should be increased and determining which marketing campaign is most appropriate. The tools that help banks with the first problem are called credit scoring models, while behavioral scoring models are used to handle exiting customers @cite_17 . Both type of models estimate the ability that a borrower will be unable to meet its debt obligations, which is referred to as default probability. This research focuses on reject inference to improve the classification accuracy of credit scoring models by utilizing the rejected applications. In Table ), we present an updated research overview on reject inference in credit scoring extending the one presented in @cite_12 .
@cite_12 @cite_17
2296034778 1980770954
Semi-supervised Support Vector Machines for reject inference are proposed.The method uses information of both the accepted and rejected applicants.The method deals with labelled and unlabelled classes of the outcome.The model is tested on real consumer loans with a low acceptance rate.Predictive accuracy is improved by the new model compared to traditional methods. Abstract Credit scoring and behavioural scoring are the techniques that help organisations decide whether or not to grant credit to consumers who apply to them. This article surveys the techniques used — both statistical and operational research based — to support these decisions. It also discusses the need to incorporate economic conditions into the scoring systems and the way the systems could change from estimating the probability of a consumer defaulting to estimating the profit a consumer will bring to the lending organisation — two of the major developments being attempted in the area. It points out how successful has been this under-researched area of forecasting financial risk.