FairFinGAN: Fairness-aware Synthetic Financial Data Generation

• URL: http://arxiv.org/abs/2603.05327v1
• Date: Thu, 05 Mar 2026 16:09:19 GMT
• 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,
• Abstract summary: We propose FairFinGAN, a WGAN-based framework designed to generate synthetic financial data while mitigating bias with respect to the protected attribute.<n>We evaluate our proposed model on five real-world financial datasets and compare it with existing GAN-based data generation methods.<n> Experimental results show that our approach achieves superior fairness metrics without significant loss in data utility.
• Score: 0.3544442162078764
• License: http://creativecommons.org/licenses/by/4.0/
• 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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