Graph-based Representation Learning for Financial Fraud and Anomaly Transaction Detection

Abstract

Financial fraud detection has emerged as a critical challenge in modern banking systems, with fraudulent transactions causing billions in annual losses worldwide. Traditional rule-based and statistical methods struggle to adapt to sophisticated fraud patterns and evolving attack vectors. This paper proposes a graph-based representation learning approach leveraging Graph Neural Networks to capture complex relational patterns in financial transaction networks. The methodology constructs heterogeneous transaction graphs encoding structural and temporal information, enabling detection of both known fraud patterns and novel anomalies. Experimental evaluations on real-world datasets demonstrate superior performance compared to traditional machine learning and deep learning baselines, with F1-scores reaching 0.947 and AUC-ROC values exceeding 0.985. The results confirm the effectiveness of graph-based representation learning for addressing imbalanced fraud detection while maintaining low false positive rates.