A systematic review of literature on credit card cyber fraud detection using machine and deep learning

Abstract

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.

UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection

“…Selected Algorithms Efficient Algorithm Accuracy Kibria and Sevkli [17] LR, Deep Learning, and SVM Deep Learning 87.10% Naveen and Diwan [30] LR, QDA and SVM LR 99.38% Shaji et al [26] ANN and SVM, Both 88.00% Sinayobye et al [31] KNN, DT, RF, SVM, LR KNN 82.60% Btoush et al [32] Deep Learning DL 95.76% Taha et al [33] Optimized Light Gradient Boosting Machine OLGBM 98.40%…”