Current Research Landscape of Machine Learning Algorithms in Online Identity Fraud Prediction and Detection

Abstract

Online fraud is an ever-growing problem that dates back to the beginning of e-commerce. An online fraudster can utilize many attack vectors and planes; however, identity fraud is one of the most common and detrimental to the victims. This paper will look at the current research landscape of the different machine learning algorithms and approaches used to help predict and detect online identity fraud. By adopting systematic review and meta-analysis protocols, this paper summarizes the types of machine learning algorithms used in online fraud detection and prevention, and highlights the reported effectiveness of these methods through performance measurement indicator analysis. Last, the researchers present the limitations and future research directions based on the results of this study