An Explainable XGBoost-Based Model for Credit Card Fraud Detection in Highly Imbalanced Transaction Data

Credit card fraud detection remains a challenging classification problem due to the severe imbalance between legitimate and fraudulent transactions, where conventional accuracy can provide a misleading assessment of model effectiveness. This study proposes an explainable machine learning model for credit card fraud detection by comparatively evaluating Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost against a Dummy Classifier baseline. Model performance was assessed using balanced accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, confusion matrices, prediction errors, and computational training time. McNemar’s test was further employed to statistically assess differences in paired model predictions, while SHapley Additive exPlanations (SHAP) provided global and local interpretability of the best-performing model. Experimental results identified XGBoost as the strongest overall classifier, achieving a PR-AUC of 0.8231, ROC-AUC of 0.9752, fraud precision of 0.9259, recall of 0.7895, and F1-score of 0.8523. XGBoost additionally produced the fewest prediction errors and the shortest training time among the evaluated gradient-boosting models. McNemar’s test indicated a statistically significant difference in paired model predictions (p = 1.29 × 10⁻¹¹). SHAP analysis identified V14, V4, V12, V10, and V11 as the most influential features, while transaction-level explanations revealed how individual feature values contributed toward or against fraud predictions. The findings demonstrate that combining gradient boosting, imbalance-sensitive evaluation, statistical validation, and explainable AI can provide an accurate, computationally efficient, and interpretable approach to credit card fraud detection.