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FairFinGAN: Fairness-aware Synthetic Financial Data Generation

arXiv 2603.05327 ↗First posted 5 Mar 2026cs.LGcited by 0

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Abstract (arXiv)

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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References · 14

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2019Modeling Tabular data using Conditional GANcited by 695
  4. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  5. 2018Data Synthesis based on Generative Adversarial Networkscited by 184
  6. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  7. 2021A survey on datasets for fairness-aware machine learningcited by 157
  8. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  9. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  10. 2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
  11. 2022PreFair: Privately Generating Justifiably Fair Synthetic Datacited by 13
  12. 2023Strong statistical parity through fair synthetic datacited by 6
2 more
  1. 2022Evaluation of group fairness measures in student performance prediction problemscited by 5
  2. 2024Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal Techniquescited by 1

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