FraudGraph-X: A Multimodal Graph Neural Framework for Behavioral Fraud Detection

Abstract

The complexity and volume of transactional data has expanded due to the rapid growth of digital financial services, which has opened the door to new types of sophisticated fraud. The ever-changing nature of fraud trends and the complexity of entity interactions make rule-based or transactional fraud detection systems inadequate. Using relational structure learning and transactional behaviour profiling, FraudGraph-X is a multimodal graph neural framework for behavioural fraud detection. In the financial ecosystem model offered by the framework, users, accounts, devices, and merchants are nodes in a graph. Graph neural networks analyse transactional histories, patterns of activity across time, and ambient metadata to identify group fraud tendencies and high-order dependencies. Unlike conventional methods, FraudGraph-X is able to learn from structural links and behavioural indicators, allowing it to detect complex and organised fraud. Based on thorough experimental testing on real-world and benchmark financial datasets, the system outperforms non-graph deep learning approaches, conventional machine learning, and the detection accuracy of changing fraud tactics. It also has lower false positive rates. Using multimodal graph learning, FraudGraph-X can discover intricate fraud patterns; it is a smart and scalable solution for fraud detection systems of the future.