Integrating Explainable AI into Credit Card Fraud Detection for Transparent Decision-Making

Abstract

Credit card fraud detection systems are increasingly powered by sophisticated machine learning models because of their better capacity to detect faint patterns and anomalies in huge transactional data. Yet, most of these models—especially ensemble methods and deep learning structures—function as "black boxes," providing little or no insight into how decisions are reached. This lack of transparency poses huge challenges in the financial sector, where trust, accountability, and regulatory compliance are of utmost importance. To tackle these challenges, this paper suggests an integrated fraud detection system that utilizes Explainable Artificial Intelligence (XAI) methods, specifically SHAP (Shapley Additive explanations) and LIME (Local Interpretable Model-Agnostic Explanations). These approaches offer both global and local explanations of model behavior, enabling stakeholders to comprehend the contribution of each feature to a particular transaction's classification. For example, SHAP provides a theoretically grounded, game-theory-based feature importance attribution, whereas LIME creates local surrogate models that mimic the decision boundary near a specific instance. By integrating XAI into the fraud detection pipeline, the proposed system not only retains high predictive accuracy but also improves interpretability and user trust. This transparency also enables analysts to make better-informed decisions, enables compliance with regulatory models like GDPR, and enables end-users to understand why a specific transaction was alerted. The outcome is an accurate, explainable, responsible, and human-centric fraud detection model that opens doors for secure and transparent AI solutions in the financial sector.