# Deep Generative Models for Reject Inference in Credit Scoring

arXiv: https://arxiv.org/abs/1904.11376 · HTML: https://mlsanity.com/paper/1904.11376/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Characterizing Fairness Over the Set of Good Models Under Selective Labels](https://mlsanity.com/paper/2101.00352/) (2021)
- [RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring](https://mlsanity.com/paper/2206.00568/) (2022)
- [Discriminative Multimodal Learning via Conditional Priors in Generative Models](https://mlsanity.com/paper/2110.04616/) (2021)
- [Towards a Better Microcredit Decision](https://mlsanity.com/paper/2209.07574/) (2022)
- [Best Practices for Responsible Machine Learning in Credit Scoring](https://mlsanity.com/paper/2409.20536/) (2024)
- [Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem](https://mlsanity.com/paper/2308.08051/) (2023)
- [The Impact of Pseudo-Science in Financial Loans Risk Prediction](https://mlsanity.com/paper/2507.16182/) (2025)
- [Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline](https://mlsanity.com/paper/2509.22381/) (2025)
- [The Illusion of Improvement: Reject Inference Strategies in Credit Scoring](https://mlsanity.com/paper/2606.18479/) (2026)

## Abstract (arXiv)

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.