Securing online transactions: Unveiling anomalies through graph-based machine learning in fraud detection

Abstract

 In an era where online transactions have become the norm, the battle against fraudulent activities looms large. This paper delves into the application of graph-based machine learning techniques, particularly Graph Neural Networks (GNN), in the realm of online transaction fraud detection. By leveraging the inherent network structure of transaction data, GNNs offer a powerful framework for uncovering complex patterns and anomalies indicative of fraudulent activities. In this study, we present the results obtained through the utilization of GNNs for online fraud detection, showcasing their efficacy in accurately identifying fraudulent transactions while minimizing false positives. Additionally, we discuss the implications and significance of employing graph-based machine learning techniques in enhancing fraud detection systems, emphasizing their ability to adapt to evolving fraud schemes and provide actionable insights for fraud mitigation strategies.