Fraud Detection in Financial Transaction Using GNN

As financial transactions increasingly shift to digital platforms, fraud has become a growing concern, impacting individuals and institutions alike. Conventional fraud detection systems, relying on rule-based or statistical approaches, often fall short of adapting to the sophisticated tactics fraudsters employ. This paper introduces a Graph Neural Network (GNN) model for detecting fraudulent transactions by capturing the intricate relationships between users, accounts, and transactional patterns. Unlike traditional machine learning algorithms, GNNs are inherently suited to handle graph-structured data, where entities are connected through various types of interactions that can reveal suspicious behavior. Transactions in fraud approach are presented as a graph where the nodes represent users or accounts, and edges represent the transactions themselves, encoded in the features of transaction amount, frequency, location, and time of day. This graph-based structure enables the GNN to take advantage of both local and global dependencies, thereby detecting otherwise hard-to-detect abnormal transaction patterns. For more accurate detection, we include additional features that capture certain behaviors that are specific indicators of fraud, such as repeated transactions within short intervals or location-based anomalies. proposed model is evaluated on a structured financial transaction dataset, and the results indicate significant improvements in accuracy, recall, and precision over traditional models, underscoring the efficacy of GNNs in fraud detection.