An Analysis of the Supervised Learning Approach for Online Fraud Detection

Abstract

Illegal online financial transactions are now more sophisticated and global in scope, which costs both parties—customers and businesses. For fraud prevention and detection in the online setting, many different strategies have been proposed. While all of these techniques aim to detect and stop fraudulent online transactions, they differ in terms of their features, advantages, and disadvantages. This study assesses the current fraud detection research in this area to detect the employed algorithms and assessing in accordance with predetermined standards. The systematic quantitative literature review methodology was used to assess the research studies in the subject of online fraud detection. A hierarchical typology is created based on the supervised learning methods in scientific articles and their properties. Therefore, by integrating three selection criteria—accuracy, coverage, and costs—our research presents the best methods for identifying fraud in a novel approach. Index Terms : Detection, Online fraud, Online transaction, Supervised Learning Algorithm.