FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection

Abstract

Fraud detection plays a crucial role in the financial industry, preventing significant financial losses. Traditional rule-based systems and manual audits often struggle with the evolving nature of fraud schemes and the vast volume of transactions. Recent advances in machine learning, particularly graph neural networks (GNNs), have shown promise in addressing these challenges. However, GNNs still face limitations in learning intricate patterns, effectively utilizing edge attributes, and maintaining efficiency on large financial graphs. To address these limitations, we introduce FraudGT, a simple, effective, and efficient graph transformer (GT) model specifically designed for fraud detection in financial transaction graphs. FraudGT leverages edge-based message passing gates and an edge attribute-based attention bias to enhance its ability to discern important transactional features and differentiate between normal and fraudulent transactions. Our model achieves state-of-the-art performance in detecting fraudulent activities while demonstrating high throughput and significantly lower latency compared to existing methods. We validate the effectiveness of FraudGT through extensive experiments on multiple large-scale financial datasets. FraudGT consistently outperforms other models, achieving 8–17% higher F1 scores, while delivering an average of 2.4× greater throughput and reduced latency.