Explainable AI in Credit Risk Models: Enhancing Transparency and Trust in Financial Decision-Making

Abstract

The integration of Artificial Intelligence (AI) in credit risk modeling has transformed financial decision-making by improving prediction accuracy and efficiency. However, the widespread use of opaque, black-box models has raised concerns about transparency, accountability, and fairness, particularly in contexts where credit access directly impacts financial inclusion and social equity. This study explores the role of Explainable AI (XAI) in enhancing the interpretability of credit risk models, thereby fostering greater trust among stakeholders, including regulators, financial institutions, and consumers. Drawing on recent advances in model-agnostic and model-specific explainability techniques, the research examines how tools such as SHAP, LIME, and counterfactual explanations can be applied to credit scoring systems without compromising predictive performance. The study further analyzes the implications of explainable models for regulatory compliance, bias detection, and ethical lending practices. By synthesizing insights from the intersection of machine learning, finance, and responsible AI, this research highlights the potential of XAI to bridge the gap between algorithmic efficiency and human understanding. Ultimately, the findings underscore that integrating explainability into credit risk assessment is not only a technical enhancement but also a critical step toward responsible innovation, improved stakeholder confidence, and sustainable financial decision-making.