A Systematic Literature Review on Supervised Machine Learning Techniques for Financial Fraud Detection
TL;DR
The findings indicated that financial fraud detection relied more on supervised learning which was the method of choice, especially in cases where labelled data of fraudulent and legitimate transactions were known.
Abstract
Financial transaction fraud is a significant problem in the fields of digital banking, credit card transactions, online payment, and mobile financial services. This study systematically reviewed the existing literature on supervised machine learning techniques used for financial fraud detection. The review included peer-reviewed papers published between 2020 and 2026 and followed the PRISMA framework, with Parsifal used to assist the search management, screening, eligibility assessment, and data extraction. A total of 503 records were obtained from IEEE Xplore, Scopus, Web of Science, ScienceDirect, and ACM Digital Library. After removing 129 duplicates, screening 374 records, and assessing 107 full-text articles, a total of 55 studies were accepted in the final review. The findings indicated that financial fraud detection relied more on supervised learning which was the method of choice, especially in cases where labelled data of fraudulent and legitimate transactions were known. Credit/debit card fraud was the most common form of fraud, contributing to 40 studies (72.7%) of the reviewed literature. Methodologically, ensemble, boosting, and hybrid classifiers were the most common methods, occurring in 17 studies (30.9%), followed by deep/hybrid representation learning in 12 studies (21.8%) and classical supervised machine learning comparisons in 10 studies (18.2%). The review further indicated that the model performance not only depends on the algorithm choice but also on preprocessing, feature selection, class imbalance handling, and evaluation metrics. The most commonly reported metric was accuracy, which occurred in 52 studies (94.5%), whereas recall/sensitivity was present in 50 studies (90.9%), precision was in 48 studies (87.3%), F1-score was in 47 studies (85.5%), ROC-AUC/AUC was in 42 studies (76.4%), and confusion matrix was in 38 studies (69.1%). The study ends by recommending that effective supervised fraud detection is a complete methodological pipeline and not just isolated algorithm comparison.