Paper Number
1382
Paper Type
Complete
Description
We propose an adversarial deep learning model for credit risk modeling. We make use of sophisticated machine learning model’s ability to triangulate (i.e., infer the sensitive group affiliation by using only permissible features), which is often deemed “troublesome” in fair machine learning research, in a positive way to increase both borrower welfare and lender profits while improving fairness. We train and test our model on a dataset from a real-world microloan company. Our model significantly outperforms regular deep neural networks without adversaries and the most popular credit risk model XGBoost, in terms of both improving borrowers’ welfare and lenders’ profits. Our empirical findings also suggest that the traditional AUC metric cannot reflect a model's performance on the borrowers’ welfare and lenders’ profits. Our framework is ready to be customized for other microloan firms, and can be easily adapted to many other decision-making scenarios.
Recommended Citation
Hu, Xiyang; Huang, Yan; Li, Beibei; and Lu, Tian, "Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit" (2022). ICIS 2022 Proceedings. 4.
https://aisel.aisnet.org/icis2022/ai_business/ai_business/4
Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit
We propose an adversarial deep learning model for credit risk modeling. We make use of sophisticated machine learning model’s ability to triangulate (i.e., infer the sensitive group affiliation by using only permissible features), which is often deemed “troublesome” in fair machine learning research, in a positive way to increase both borrower welfare and lender profits while improving fairness. We train and test our model on a dataset from a real-world microloan company. Our model significantly outperforms regular deep neural networks without adversaries and the most popular credit risk model XGBoost, in terms of both improving borrowers’ welfare and lenders’ profits. Our empirical findings also suggest that the traditional AUC metric cannot reflect a model's performance on the borrowers’ welfare and lenders’ profits. Our framework is ready to be customized for other microloan firms, and can be easily adapted to many other decision-making scenarios.
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