FraudXAI-Net: An Explainability-Driven Intelligent Fraud Detection Framework

Abstract

Financial fraud has become a major problem in modern digital transactions. Traditional fraud detection systems mainly focus on prediction accuracy, but they do not clearly explain why a transaction is marked as fraudulent. This creates trust and transparency issues in banking and financial applications. To solve this problem, this research paper proposes FraudXAI-Net, an explainability-driven intelligent fraud detection framework that combines machine learning and Explainable Artificial Intelligence (XAI). The proposed framework performs data preprocessing, feature scaling, class imbalance handling, model training, fraud prediction, and explainability analysis. Multiple machine learning models such as Logistic Regression, Random Forest, and XGBoost are used for fraud classification. SHAP (Shapley Additive Explanations) is applied to identify the contribution of each feature in prediction results. The framework improves both fraud detection performance and model interpretability. Experimental results show that the XGBoost model achieved the best performance with high accuracy, precision, recall, and F1-score. The SHAP-based explainability method successfully highlighted the important features responsible for fraudulent transactions. The proposed FraudXAI-Net framework can help financial institutions build more reliable, transparent, and intelligent fraud detection systems.