FairFinGAN: Fairness-aware Synthetic Financial Data Generation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914372049174528 |
|---|---|
| author | Quy, Tai Le Tuan, Dung Nguyen Thanh, Trung Nguyen Cong, Duy Tran Thu, Huyen Giang Thi Hopfgartner, Frank |
| author_facet | Quy, Tai Le Tuan, Dung Nguyen Thanh, Trung Nguyen Cong, Duy Tran Thu, Huyen Giang Thi Hopfgartner, Frank |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05327 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FairFinGAN: Fairness-aware Synthetic Financial Data Generation Quy, Tai Le Tuan, Dung Nguyen Thanh, Trung Nguyen Cong, Duy Tran Thu, Huyen Giang Thi Hopfgartner, Frank Machine Learning 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. |
| title | FairFinGAN: Fairness-aware Synthetic Financial Data Generation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.05327 |