Interpretable Machine Learning Models for Credit Risk Assessment in Financial Institutions
Ugochukwu Ukeje1*
Abstract
Credit risk assessment remains a cornerstone of decision making in the financial sector, directly influencing loan approvals, capital allocation, and systemic risk management. As machine learning models increasingly replace traditional techniques like logistic regression to enhance predictive accuracy, concerns over their interpretability have grown, especially given the opaque nature of black box algorithms such as XGBoost and deep neural networks. In high stakes domains like finance, this lack of transparency raises significant regulatory and ethical challenges, particularly under frameworks that demand explainable and non discriminatory decision making. This paper critically examines the landscape of interpretable machine learning (IML) models applied to credit risk assessment, with a focus on both intrinsically interpretable methods such as decision trees and generalized additive models and post hoc explanation techniques, including SHAP, LIME, and counterfactual reasoning. Through a structured taxonomy and comparative analysis, the study evaluates how these models address the trade offs between predictive performance, interpretability, and fairness. Key findings highlight the limitations of current IML approaches in handling bias, the lack of standardized interpretability metrics, and the need for hybrid frameworks that combine model transparency with high accuracy. The paper concludes by outlining future research directions, including causal inference, privacy preserving AI, and interdisciplinary collaboration, as essential to building trustworthy and accountable financial systems.
Keywords: Credit Risk, Interpretable Machine Learning, Explainable AI, Fairness, Financial Institutions, SHAP, LIME