FraudX AI: An Interpretable Machine Learning Framework for Credit Card Fraud Detection on Imbalanced Datasets
Baisholan N. Dietz J.E. Gnatyuk S. Turdalyuly M. Matson E.T. Baisholanova K.
April 2025Multidisciplinary Digital Publishing Institute (MDPI)
Computers
2025#14Issue 4
Credit card fraud detection is a critical research area due to the significant financial losses and security risks associated with fraudulent activities. This study presents FraudX AI, an ensemble-based framework addressing the challenges in fraud detection, including imbalanced datasets, interpretability, and scalability. FraudX AI combines random forest and XGBoost as baseline models, integrating their results by averaging probabilities and optimizing thresholds to improve detection performance. The framework was evaluated on the European credit card dataset, maintaining its natural imbalance to reflect real-world conditions. FraudX AI achieved a recall value of 95% and an AUC-PR of 97%, effectively detecting rare fraudulent transactions and minimizing false positives. SHAP (Shapley additive explanations) was applied to interpret model predictions, providing insights into the importance of features in driving decisions. This interpretability enhances usability by offering helpful information to domain experts. Comparative evaluations of eight baseline models, including logistic regression and gradient boosting, as well as existing studies, showed that FraudX AI consistently outperformed these approaches on key metrics. By addressing technical and practical challenges, FraudX AI advances fraud detection systems with its robust performance on imbalanced datasets and its focus on interpretability, offering a scalable and trusted solution for real-world financial applications.
anomaly detection , AUC-PR , credit card fraud detection , ensemble models , imbalanced datasets , machine learning , SHAP
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Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Software Engineering Department, International Engineering and Technological University, Almaty, 050060, Kazakhstan
Department of Computer and Information Technology, Purdue University, West Lafayette, 47907, IN, United States
Faculty of Computer Science and Technology, State University “Kyiv Aviation Institute”, Kyiv, 03058, Ukraine
School of Digital Technologies, Narxoz University, Almaty, 050035, Kazakhstan
Faculty of Information Technology
Software Engineering Department
Department of Computer and Information Technology
Faculty of Computer Science and Technology
School of Digital Technologies
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