Credit Card Fraud Detection Using Machine Learning Models: A Study Review
DOI:
https://doi.org/10.59222/ustjet.4.1.5Keywords:
Machine learning, credit card fraud detection, fraudsters methods, financial fraudAbstract
Advances in technology have led to the emergence of digital payments. As credit card transactions have become the most common payment method and increasingly sophisticated methods have been adopted by fraudsters, detecting fraudulent transactions has become crucial due to the financial losses incurred by both cardholders and financial institutions. This study aims to review existing research on Credit Card Fraud Detection (CCFD) to provide future researchers with insights into the latest Machine Learning (ML) models applied in this field. This study adopts a critical literature review approach to explore and analyze current studies. With the rapid advancements in CCFD using ML models, which have addressed numerous challenges in this domain, it has become increasingly difficult to identify the techniques that contribute most significantly to its development, as well as the research gaps that require further research. Therefore, this review identifies 20 research articles published between 2022 and 2025. The focus on studies from the past four years ensures coverage of up-to-date developments and the latest technologies. The findings of this review highlight the most effective ML models and CCFD datasets, identify key research gaps, and outline the key evaluation metrics utilized in the field of CCFD, thereby supporting future studies.
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