Credit card fraud detection in the era of disruptive technologies: A systematic review

• Published in: Journal of King Saud University. Computer and information sciences 2023-01, Vol.35 (1), p.145-174
• Main Authors: Cherif, Asma, Badhib, Arwa, Ammar, Heyfa, Alshehri, Suhair, Kalkatawi, Manal, Imine, Abdessamad
• Format: Article
• Language: English
• Subjects: Class imbalance; Credit card fraud detection; Deep learning; Machine learning
• ISSN: 1319-1578; 2213-1248
• DOI: 10.1016/j.jksuci.2022.11.008

Credit card fraud is becoming a serious and growing problem as a result of the emergence of innovative technologies and communication methods, such as contactless payment. In this article, we present an in-depth review of cutting-edge research on detecting and predicting fraudulent credit card transactions conducted from 2015 to 2021 inclusive. The selection of 40 relevant articles is reviewed and categorized according to the topics covered (class imbalance problem, feature engineering, etc.) and the machine learning technology used (modelling traditional and deep learning). Our study shows a limited investigation to date into deep learning, revealing that more research is required to address the challenges associated with detecting credit card fraud through the use of new technologies such as big data analytics, large-scale machine learning and cloud computing. Raising current research issues and highlighting future research directions, our study provides a useful source to guide academic and industrial researchers in evaluating financial fraud detection systems and designing robust solutions.