FairFinGAN: Fairness-aware Synthetic Financial Data Generation

2026-03-05

AI Summary

This work addresses the risk of unfair outcomes in automated decision-making systems caused by biases inherent in financial data. To mitigate this issue, the authors propose a fairness-aware synthetic data generation framework that, for the first time, directly incorporates fairness constraints into the training process of a Wasserstein Generative Adversarial Network (WGAN). By integrating a fairness regularization term based on protected attributes and a classifier-guidance mechanism, the framework simultaneously preserves high-fidelity data characteristics and reduces bias. Experimental evaluations on five real-world financial datasets demonstrate that the proposed method significantly improves fairness metrics—such as demographic parity and equalized odds—while maintaining data utility for downstream tasks, thereby achieving a balanced trade-off between fairness and practical applicability.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• fairness
• synthetic data generation
• financial data
• bias mitigation
• protected attribute

Innovation

• FairFinGAN
• fairness-aware generation
• WGAN
• synthetic financial data
• bias mitigation