# Fairness Assessment for Artificial Intelligence in Financial Industry

arXiv: https://arxiv.org/abs/1912.07211 · HTML: https://mlsanity.com/paper/1912.07211/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [BeFair: Addressing Fairness in the Banking Sector](https://mlsanity.com/paper/2102.02137/) (2021)
- [Towards Multi-Objective Statistically Fair Federated Learning](https://mlsanity.com/paper/2201.09917/) (2022)
- [Aequitas Flow: Streamlining Fair ML Experimentation](https://mlsanity.com/paper/2405.05809/) (2024)
- [Achieving Counterfactual Fairness with Imperfect Structural Causal Model](https://mlsanity.com/paper/2303.14665/) (2023)
- [Data and AI governance: Promoting equity, ethics, and fairness in large language models](https://mlsanity.com/paper/2508.03970/) (2025)
- [Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools](https://mlsanity.com/paper/2312.12460/) (2023)
- [Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness](https://mlsanity.com/paper/2407.03133/) (2024)

## Abstract (arXiv)

Artificial Intelligence (AI) is an important driving force for the development and transformation of the financial industry. However, with the fast-evolving AI technology and application, unintentional bias, insufficient model validation, immature contingency plan and other underestimated threats may expose the company to operational and reputational risks. In this paper, we focus on fairness evaluation, one of the key components of AI Governance, through a quantitative lens. Statistical methods are reviewed for imbalanced data treatment and bias mitigation. These methods and fairness evaluation metrics are then applied to a credit card default payment example.