Marazqah Btoush Eyad Abdel Latif, Zhou Xujuan, Gururajan Raj, Chan Ka Ching, Genrich Rohan, Sankaran Prema
School of Business, University of Southern Queensland, Toowoomba, QLD, Australia.
School of Computing, SRM Institute of Science and Technology, Chennai, India.
PeerJ Comput Sci. 2023 Apr 17;9:e1278. doi: 10.7717/peerj-cs.1278. eCollection 2023.
The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented. Our review provides direction for choosing the most suitable techniques. This review also discusses the major problems, gaps, and limits in detecting cyber fraud in credit card and recommend research directions for the future. This comprehensive review enables researchers and banking industry to conduct innovation projects for cyber fraud detection.
网络攻击和犯罪的日益蔓延使网络安全成为银行业的首要任务。信用卡网络欺诈是全球范围内的重大安全风险。传统的异常检测和基于规则的技术是检测网络欺诈最常用的两种方法，然而，它们最耗时、资源密集且不准确。机器学习是在该领域日益流行并发挥重要作用的技术之一。本研究考察并综合了以往关于信用卡网络欺诈检测的研究。本综述特别侧重于探索机器学习/深度学习方法。在我们的综述中，我们识别出了2019年至2021年发表的181篇研究文章。为了研究人员的利益，本文介绍了机器学习/深度学习技术及其在信用卡网络欺诈检测中的相关性。我们的综述为选择最合适的技术提供了方向。本综述还讨论了信用卡网络欺诈检测中的主要问题、差距和局限性，并推荐了未来的研究方向。这一全面的综述使研究人员和银行业能够开展网络欺诈检测的创新项目。
