EXPLAINABLE AI FOR CREDIT RISK MANAGEMENT IN REGULATED FINANCIAL ENVIRONMENTS: A TRANSPARENT AND AUDITABLE DECISION FRAMEWORK

As artificial intelligence becomes popular for credit risk, the issues of transparency, explainability, and regulatory compliance raise significant challenges. Even though sophisticated predictive techniques can improve risk assessment accuracy, the black-box nature of the decision process associated with those techniques is frequently at odds with the regulated financial industry's reliance on explainability, accountability, and auditability. This study introduces an explainable artificial intelligence (XAI)–driven framework for credit risk management to support transparent, regulator-aligned decision-making. The proposed method provides both global and local interpretability, allowing stakeholders to reason about how important financial and credit factors drive risk at both levels. On the model side, explainability techniques integrated into the model itself yield human-interpretable explanations for model predictions, which ultimately support internal risk governance, compliance audits, and fair lending practices. It provides an explainable decision logic framework that meets regulatory expectations for model transparency while helping avoid the risks of poorly deploying a black-box AI model. These results highlight that risk assessment methods, while remaining consistent with regulatory principles, can provide reliable and consistent credit risk insights when they also enforce explainability guidelines. The framework provides traceable, interpretable, and defensible credit decisions that instil trust among financial institutions, regulators,  and customers. To sum up, this work improves upon the existing state of the art in accountable financial analytics by exemplifying an explainability-based paradigm of credit risk. This sets the stage for trust and opens new conversations that explainability itself is a foundational component of both compliance and AI solution trust in regulated financial institutions.