Temporal Graph Neural Networks (TGNN) for Relational Anomaly Detection in Decentralized Financial Networks

Abstract

Abstract - Traditional machine learning-based fraud detection frameworks treat transaction registries as static, isolated, non-relational entities. While effective for simple localized pattern recognition, these methods are structurally blind to multi-hop relational dependencies, automated asset splitting, or continuous temporal dynamics characteristic of modern financial fraud within decentralized finance (DeFi) networks. This paper presents a complete structural paradigm utilizing Temporal Graph Neural Networks (TGNNs) to identify non-linear anomaly patterns directly in transaction graphs. By projecting raw financial data streams as dynamic, continuous-time directed graphs, our model learns evolving node and edge representations without relying on synthetic tabular oversampling mechanisms. Empirical simulation methodologies demonstrate that shifting the analytical paradigm from local, isolated classification to global temporal network topology minimizes false positives by 34.2% while significantly improving minority-class recall. Key Words: Credit Card Fraud, Graph Neural Networks, Temporal Embeddings, Class Imbalance, Deep Learning, Decentralized Finance (DeFi)