BIAS TESTING FOR FAIR AND ETHICAL MACHINE LEARNING MODELS IN CONSUMER FINANCE

Abstract

Machine learning models have become increasingly prevalent in consumer finance, revolutionizing credit decisioning while raising significant concerns about fairness and transparency. This article presents a comprehensive framework for bias testing in machine learning models within the financial services sector, addressing both regulatory compliance and ethical considerations. The methodologies for identifying and mitigating discriminatory patterns, with particular emphasis on proxy variable detection and disparate impact analysis. The framework encompasses continuous monitoring systems, statistical validation approaches, and governance protocols designed to ensure sustained model fairness. Effective bias testing requires a multi-faceted approach combining technical rigor with domain expertise in financial services. The proposed methodology provides practitioners with actionable insights for implementing robust bias testing procedures while maintaining model performance. Furthermore, this article discusses practical challenges and solutions in stakeholder communication and regulatory documentation, offering a balanced perspective on the trade-offs between model complexity and interpretability. This article contributes to the growing body of literature on responsible AI in finance, providing a structured approach to bias testing that aligns with both business objectives and ethical principles.