Graph Based Fraud Detection

Graph-based fraud detection leverages the relational structure of data (e.g., financial transactions, social media interactions) to identify fraudulent activities, aiming to improve accuracy and interpretability of detection models. Current research focuses on addressing limitations of existing Graph Neural Networks (GNNs), such as the assumption of homophily (similar nodes having similar labels) in inherently heterophilic fraud graphs, by developing novel GNN architectures and incorporating techniques like attention mechanisms, spectral analysis, and federated learning to handle data privacy and heterogeneity across institutions. These advancements are significantly impacting various sectors, including finance and e-commerce, by enabling more accurate, efficient, and explainable fraud detection systems.