{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T05:08:45Z","timestamp":1784956125980,"version":"3.55.0"},"reference-count":86,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan","award":["AP25794007"],"award-info":[{"award-number":["AP25794007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/computers14100437","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:12:29Z","timestamp":1760523149000},"page":"437","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Systematic Review of Machine Learning in Credit Card Fraud Detection Under Original Class Imbalance"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8134-0466","authenticated-orcid":false,"given":"Nazerke","family":"Baisholan","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"},{"name":"Software Engineering Department, International Engineering and Technological University, Almaty 050060, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Eric","family":"Dietz","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4992-0564","authenticated-orcid":false,"given":"Sergiy","family":"Gnatyuk","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Technology, State University \u201cKyiv Aviation Institute\u201d, 03058 Kyiv, Ukraine"},{"name":"State Scientific and Research Institute of Cybersecurity Technologies and Information Protection, 03142 Kyiv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1470-3706","authenticated-orcid":false,"given":"Mussa","family":"Turdalyuly","sequence":"additional","affiliation":[{"name":"Software Engineering Department, International Engineering and Technological University, Almaty 050060, Kazakhstan"},{"name":"School of Digital Technologies, Narxoz University, Almaty 050035, Kazakhstan"},{"name":"School of Engineering and Information Technologies, Eurasian Technological University, Almaty 050012, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric T.","family":"Matson","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7375-5998","authenticated-orcid":false,"given":"Karlygash","family":"Baisholanova","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116429","DOI":"10.1016\/j.eswa.2021.116429","article-title":"Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances","volume":"193","author":"Hilal","year":"2022","journal-title":"Expert Syst. 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