Fairness Assessment for Artificial Intelligence in Financial Industry
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 1.
7 papers.
- 2021BeFair: Addressing Fairness in the Banking Sectorcited by 8
- 2022Towards Multi-Objective Statistically Fair Federated Learningcited by 8
- 2024Aequitas Flow: Streamlining Fair ML Experimentationcited by 4
- 2023Achieving Counterfactual Fairness with Imperfect Structural Causal Modelcited by 2
- 2025Data and AI governance: Promoting equity, ethics, and fairness in large language modelscited by 2
- 2023Democratize with Care: The need for fairness specific features in user-interface based open source AutoML toolscited by 0
- 2024Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairnesscited by 0
- 2025Data and AI governance: Promoting equity, ethics, and fairness in large language models5 Aug 2025
- 2024Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness24 May 2024
- 2024Aequitas Flow: Streamlining Fair ML Experimentation9 May 2024
- 2023Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools16 Dec 2023
- 2023Achieving Counterfactual Fairness with Imperfect Structural Causal Model26 Mar 2023
- 2022Towards Multi-Objective Statistically Fair Federated Learning24 Jan 2022
- 2021BeFair: Addressing Fairness in the Banking Sector3 Feb 2021