Bias Detection and Fairness in Large Language Models for Financial Services

Authors

  • Rahul Vats Maharishi University of Management, Fairfield IA, USA Author
  • Shekhar Agrawal University of Cincinnati, USA Author
  • Srinivasa Sunil Chippada University of Arizona, USA Author

DOI:

https://doi.org/10.32628/CSEIT25112461

Keywords:

Algorithmic fairness, financial bias mitigation, explainable AI, adversarial testing, regulatory compliance

Abstract

This article addresses the critical issue of algorithmic bias and fairness in Large Language Models (LLMs) deployed across financial services. As these powerful AI systems increasingly influence decision-making in credit scoring, loan approvals, fraud detection, and risk assessments, they risk perpetuating or amplifying existing societal biases. The article introduces the Bias Detection and Fairness Evaluation (BDFE) Framework, a comprehensive methodology integrating adversarial testing, fairness-aware model training, and explainable AI to identify and mitigate biases in financial applications. Through real-world case studies involving credit underwriting during a major merger, international fraud detection systems, and insurance claim processing, it demonstrates how the framework significantly reduces bias while maintaining model accuracy and regulatory compliance. The article reveals that fairness-enhanced models deliver substantial business benefits including expanded market reach, reduced regulatory risk, and improved customer trust. It provides practical guidance for financial institutions navigating the complex intersection of AI innovation, ethical considerations, and regulatory requirements in an increasingly AI-driven industry.

Downloads

Download data is not yet available.

References

Aakriti Bajracharya et al., "Recent Advances in Algorithmic Biases and Fairness in Financial Services: A Survey," Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (pp.809-822), 2022. [Online]. Available: https://www.researchgate.net/publication/364505799_Recent_Advances_in_Algorithmic_Biases_and_Fairness_in_Financial_Services_A_Survey

Oluwatofunnmi O. Oguntibeju, "Mitigating Artificial Intelligence Bias in Financial Systems: A Comparative Analysis of Debiasing Techniques," Asian Journal of Research in Computer Science, 2024. [Online]. Available: https://journalajrcos.com/index.php/AJRCOS/article/view/536

Sonja Kelly, "Algorithmic Bias, Financial Inclusion, and Gender," Women's World Banking, 2024. [Online]. Available: https://www.womensworldbanking.org/insights/algorithmic-bias-financial-inclusion-and-gender/

Pavan Rupanguntla et al., "Bias Testing for Fair and Ethical Machine Learning Models in Consumer Finance," Research Gate Publication, 2025. [Online]. Available: https://www.researchgate.net/publication/389085465_BIAS_TESTING_FOR_FAIR_AND_ETHICAL_MACHINE_LEARNING_MODELS_IN_CONSUMER_FINANCE

Oksana Zdrok, "Fairness Metrics in AI: Your Step-by-Step Guide to Equitable Systems," Shelf.io, 2024. [Online]. Available: https://shelf.io/blog/fairness-metrics-in-ai/

Alexey Surkov et al., "Unleashing the power of machine learning models in banking through explainable artificial intelligence (XAI)," Deloitte Insights, 2022. [Online]. Available: https://www2.deloitte.com/us/en/insights/industry/financial-services/explainable-ai-in-banking.html

Sanjiv Das, et al., "Algorithmic Fairness," Berkeley Haas Faculty Research Paper, 2022. [Online]. Available: https://faculty.haas.berkeley.edu/stanton/pdf/ARfairness.pdf

Lokesh Ballenahalli, "Mitigating Bias in LLM Models," Enkefalos Research. [Online]. Available: https://www.enkefalos.com/blog/large-language-models/mitigating-bias-in-llm-models/

Alexander Savinskiy, "Mergers and acquisitions of fintechs: market overview and Money Lion case," E3S Web of Conferences, vol. 392, 2023. [Online]. Available: https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/57/e3sconf_ebwff2023_08005.pdf

Phil Britt, "5 AI Case Studies in Finance," VKTR Digital, 2024. [Online]. Available: https://www.vktr.com/ai-disruption/5-ai-case-studies-in-finance/

BBVA, "Algorithmic fairness as a key to creating responsible artificial intelligence," BBVA, 2024. [Online]. Available: https://www.bbva.com/en/innovation/algorithmic-fairness-as-a-key-to-creating-responsible-artificial-intelligence/

HyScaler, "The ROI of AI: How Businesses Can Measure the Value of AI Investments," 2024. [Online]. Available: https://hyscaler.com/insights/ai-investment-roi-of-ai-business-guide/

Nicola Morini Bianzino, "How to navigate global trends in Artificial Intelligence regulation," EY, 2024. [Online]. Available: https://www.ey.com/en_in/insights/ai/how-to-navigate-global-trends-in-artificial-intelligence-regulation

Ray Eitel-Porter et al., "Responsible AI: From Principles to Practice," Accenture. [Online]. Available: https://www.accenture.com/content/dam/accenture/final/a-com-migration/pdf/pdf-149/accenture-responsible-ai-final.pdf

Shekhar Agrawal, "Enterprise-Scale Bias Mitigation: A Real-time Framework for Large Language Models." International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 8(1), 2410-2422, 2025. Available: https://iaeme.com/Home/article_id/IJRCAIT_08_01_175

Downloads

Published

16-03-2025

Issue

Section

Research Articles