Bias Mitigation in AI-Driven Banking: Legal, Ethical, and Regulatory Perspectives

AI-based decision-making in banking, particularly in lending, credit scoring, and fraud detection, raises concerns about systemic bias, fairness, and discrimination. This paper critically examines the intersection of algorithmic bias and U.S. anti-discrimination laws (Equal Credit Opportunity Act, Fair Housing Act). It proposes a hybrid framework that combines technical bias mitigation strategies with legal and ethical oversight. Through qualitative analysis of existing litigation and quantitative fairness audits of selected models, the paper identifies practical solutions to enhance equity in banking AI systems.