Bias Detection and Fairness in Large Language Models for Financial Services
Year: 2025
Type: article
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.
Authors: Rahul Vats, Shekhar Agrawal, Srinivasa Sunil Chippada
Institutions: University of Cincinnati, University of Arizona
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4408573369