Credit risk modeling is a cornerstone of financial stability, enabling institutions to assess the likelihood of loan defaults and make informed lending decisions. Traditional credit scoring methods, while foundational, often rely on rigid statistical models with limited flexibility to capture complex borrower behaviors and dynamic market conditions. The advent of machine learning (ML) has significantly enhanced predictive accuracy in credit risk assessment; however, the opaque nature of many ML models poses critical challenges in terms of transparency, regulatory compliance, and stakeholder trust. Explainable Artificial Intelligence (XAI) offers a transformative solution by bridging the gap between advanced predictive modeling and the need for interpretability in financial decision-making. This explores predictive approaches that integrate XAI into credit risk modeling to enhance the identification of default risk drivers and reduce loan default rates. It examines key XAI methodologies, such as SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and interpretable ensemble techniques, which allow financial institutions to understand and validate model outcomes effectively. Furthermore, this discusses practical applications of XAI-enhanced credit models in real-world banking and fintech environments, demonstrating how explainability tools improve risk segmentation, model governance, and strategic loan portfolio management. Challenges related to data biases, trade-offs between model complexity and interpretability, and operational integration of XAI solutions are critically analyzed. This also outlines emerging trends, including inherently interpretable models, real-time explainability, and AI governance frameworks, which are poised to redefine credit risk assessment practices. By adopting Explainable AI-driven credit risk models, financial institutions can achieve a balance between predictive accuracy and transparency, fostering more responsible lending practices and significantly mitigating the risk of loan defaults.
Ajayi et al. (Fri,) studied this question.