Real-Time Credit Card Fraud Detection Using Graph Attention Networks with Cost-Sensitive Learning

Due to the dynamic expansion and greater efficiency of online payment systems, financial fraud has also become more prevalent and increasingly tech-savvy. For efficient and effective detection, rule-based and table-based cross-learning algorithms often do not account for the relationships and context involved in modern financial fraud. To tackle these problems, a novel real-time fraud detection system is presented that leverages both Graph Neural Networks and the edge perspective. During the system, the data is represented as a graph, leading to better understanding through relation-based learning. To recognize especially influential patterns, a tuned-up variant, the Graph Attention Network, is considered, targeting the discovery of fraudulent patterns through attention-based message transmission between connected entities. To better address class imbalance, a combined performance measure is used that combines the Synthetic Minority Oversampling Technique and Cost Sensitive Learning. During the experiments, a dataset was used comprising 284,807 samples, of which 0.172 % were fraudulent credit card transaction history cases. It is important to note that the proposed system for detection purposes will deliver highly efficient results, with a rapid response time, achieving 99.89 % accuracy.