Fraud Detection

Financial fraud detection is a major challenge in modern digital financial systems due to the increasing volume and complexity of transactions. Traditional machine learning approaches treat transactions as independent entities, making them ineffective in identifying coordinated fraud patterns. This research proposes a Graph Neural Network (GNN)-based fraud detection system that models financial transactions as a graph, where users, accounts, and merchants are represented as nodes and transactions as edges. The system utilizes advanced GNN architectures such as GraphSAGE and Graph Attention Networks (GAT) to capture both structural and feature-based relationships. To improve detection performance, the system incorporates feature engineering and graph-based learning techniques. Experimental results demonstrate improved performance in terms of accuracy, precision, recall, and F1-score, along with better fraud detection capability compared to traditional methods. The proposed system provides a scalable, efficient, and real-time solution for detecting financial fraud in large-scale transaction networks.