Graph Neural Networks for Financial Fraud and Anomaly Detection

Abstract

Financial fraud now spreads through coordinated accounts whose risk is visible mainly in how they connect rather than in any single record. Tree based classifiers that score transactions in isolation miss this relational signal. This paper reviews and consolidates how graph neural networks (GNNs) cast fraud and anomaly detection as node and edge classification over transaction graphs, where message passing propagates evidence among accounts, devices, and merchants. We describe the main architectures used in practice, namely graph convolutional networks, GraphSAGE, and graph attention networks, and explain how each aggregates neighbourhood information. Two problems dominate the fraud setting: extreme class imbalance, since genuine fraud is rare, and camouflage, where fraudsters attach to honest neighbours to dilute their signature. We summarise label aware and neighbour selecting designs such as CARE-GNN and PC-GNN that address both. Heterogeneous and temporal graph variants are discussed for settings with several node types and evolving relations, alongside sampling methods that keep training tractable on large graphs. Using illustrative experiments patterned on public benchmarks (Elliptic, Yelp and Amazon review fraud, and IEEE-CIS), we report AUC, AUPRC, F1, and recall at k, and show that imbalance aware GNNs improve minority recall over a gradient boosted baseline. We close with notes on explainability and the engineering needed for production scoring.