Deep Generative Models for Reject Inference in Credit Scoring
This addresses bias in credit scoring for financial institutions, but it is incremental as it builds on existing reject inference techniques with new models.
The paper tackles bias in credit scoring models by using deep generative models for reject inference, aiming to infer the creditworthiness of rejected applications, and shows that the proposed models outperform classical and alternative machine learning methods in experiments.
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.