Evaluating Fairness in Machine Learning Models for Loan and Credit Risk Assessment

Abstract

The integration of machine learning into loan and credit risk assessment has improved predictive accuracy and operational efficiency, yet it has simultaneously amplified concerns about fairness and bias in financial decision-making. Algorithms trained on historical financial and demographic data can inadvertently replicate or even intensify structural inequalities, leading to discriminatory lending practices that disadvantage vulnerable groups [1], [7], [13]. Addressing these challenges requires systematic evaluation of fairness, incorporating both technical and socio-economic perspectives.

Recent studies have demonstrated that enforcing fairness constraints may reduce short-term accuracy or profitability but enhance long-term trust, regulatory compliance, and financial inclusion [2], [6], [9]. Approaches range from bias detection techniques, residual unfairness assessments, and counterfactual risk analysis [11], [12], to fairness-aware algorithms and toolkits such as Fairlearn, which enable transparent evaluation and mitigation of model disparities [5]. At the same time, context-conscious frameworks highlight that fairness is not a universal metric but must be adapted to specific legal, cultural, and market environments [7], [10].

This paper evaluates fairness in machine learning models for credit scoring and loan risk prediction by synthesizing advances in fairness metrics, model interpretability, and risk-adjusted performance analysis. Building on prior research in explainability, consumer lending, and regulatory perspectives [4], [10], the study argues that fairness must be treated as both a technical criterion and a socio-economic imperative. The findings emphasize that equitable credit assessment requires balancing predictive performance with ethical responsibility, ensuring not only accurate risk estimation but also inclusive access to financial resources.