[2603.05327] FairFinGAN: Fairness-aware Synthetic Financial Data Generation

Computer Science > Machine Learning arXiv:2603.05327 (cs) [Submitted on 5 Mar 2026] Title:FairFinGAN: Fairness-aware Synthetic Financial Data Generation Authors:Tai Le Quy, Dung Nguyen Tuan, Trung Nguyen Thanh, Duy Tran Cong, Huyen Giang Thi Thu, Frank Hopfgartner View a PDF of the paper titled FairFinGAN: Fairness-aware Synthetic Financial Data Generation, by Tai Le Quy and 5 other authors View PDF HTML (experimental) 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. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.05327 [cs.LG]   (or arXiv:2603.05327v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.05327 Focus to learn more arXiv-issued DOI via DataCite (pending regi...