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arXiv · 2603.05327

FairFinGAN: Fairness-aware Synthetic Financial Data Generation

Tai Le Quy·Dung Nguyen Tuan·Trung Nguyen Thanh·Duy Tran Cong·Huyen Giang Thi Thu·Frank Hopfgartner
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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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Tai Le Quy, Dung Nguyen Tuan, Trung Nguyen Thanh, Duy Tran Cong, Huyen Giang Thi Thu, Frank Hopfgartner. 2026-03-05. FairFinGAN: Fairness-aware Synthetic Financial Data Generation. https://doi.org/10.1007/978-981-92-1947-6_16

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