Actionable Explainable AI for Credit Risk: A SAFE-AI Framework Integrating SHAP and Counterfactual Explanations with DiCE

Abstract

: The increasing use of machine learning models in credit risk assessment raises concerns about transparency, trust, and regulatory compliance, as many high-performing models behave as black boxes. This paper proposes an actionable explainable AI framework for credit risk that combines an eXtreme Gradient Boosting (XGBoost) classifier with Shapley Additive Explanations (SHAP) and Diverse Counterfactual Explanations (DiCE), organized under the Situation Awareness Framework for Explainable AI (SAFE-AI). Using the public HELOC dataset, we first train and tune an XGBoost model to predict default risk, then derive global and local explanations with SHAP, and finally generate counterfactual explanations with DiCE to indicate feasible changes capable of reversing unfavorable outcomes. Results show that the proposed framework provides competitive predictive performance while enhancing interpretability and actionable decision support in credit risk assessment.