A Systematic Review of Machine Learning in Credit Card Fraud Detection Under Original Class Imbalance
Baisholan N. Dietz J.E. Gnatyuk S. Turdalyuly M. Matson E.T. Baisholanova K.
October 2025Multidisciplinary Digital Publishing Institute (MDPI)
Computers
2025#14Issue 10
Credit card fraud remains a significant concern for financial institutions due to its low prevalence, evolving tactics, and the operational demand for timely, accurate detection. Machine learning (ML) has emerged as a core approach, capable of processing large-scale transactional data and adapting to new fraud patterns. However, much of the literature modifies the natural class distribution through resampling, potentially inflating reported performance and limiting real-world applicability. This systematic literature review examines only studies that preserve the original class imbalance during both training and evaluation. Following PRISMA 2020 guidelines, strict inclusion and exclusion criteria were applied to ensure methodological rigor and relevance. Four research questions guided the analysis, focusing on dataset usage, ML algorithm adoption, evaluation metric selection, and the integration of explainable artificial intelligence (XAI). The synthesis reveals dominant reliance on a small set of benchmark datasets, a preference for tree-based ensemble methods, limited use of AUC-PR despite its suitability for skewed data, and rare implementation of operational explainability, most notably through SHAP. The findings highlight the need for semantics-preserving benchmarks, cost-aware evaluation frameworks, and analyst-oriented interpretability tools, offering a research agenda to improve reproducibility and enable effective, transparent fraud detection under real-world imbalance conditions.
credit card fraud detection , financial fraud , fraud evaluation metrics , imbalanced classification , Kitchenham approach , machine learning , SHAP , systematic literature review , XAI
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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
State Scientific and Research Institute of Cybersecurity Technologies and Information Protection, Kyiv, 03142, Ukraine
School of Digital Technologies, Narxoz University, Almaty, 050035, Kazakhstan
School of Engineering and Information Technologies, Eurasian Technological University, Almaty, 050012, Kazakhstan
Faculty of Information Technology
Software Engineering Department
Department of Computer and Information Technology
Faculty of Computer Science and Technology
State Scientific and Research Institute of Cybersecurity Technologies and Information Protection
School of Digital Technologies
School of Engineering and Information Technologies
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