FairFinGAN: Fairness-aware Synthetic Financial Data Generation
cs.LG
Submitted: 2026-03-05
Updated: 2026-03-05
Comments: Accepted to Special Session: Data Science: Foundations and Applications (DSFA), PAKDD 2026
DOI: 10.1007/978-981-92-1947-6_16
Code: https://github.com/tailequy/FairFinGAN
License: http://creativecommons.org/licenses/by/4.0/
The gist: Financial datasets often suffer from bias that can lead to unfair decision-making in automated systems.
Terminology
Abstract
Financial datasets often suffer from bias that can lead to unfair decision-making in automated systems. In this work, we propose FairFinGAN, a WGAN-based framework designed to generate synthetic financial data while mitigating bias with respect to the protected attribute. Our approach incorporates fairness constraints directly into the training process through a classifier, ensuring that the synthetic data is both fair and preserves utility for downstream predictive tasks. We evaluate our proposed model on five real-world financial datasets and compare it with existing GAN-based data generation methods. Experimental results show that our approach achieves superior fairness metrics without significant loss in data utility, demonstrating its potential as a tool for bias-aware data generation in financial applications.
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