Graph Neural Networks for Financial Fraud Detection: A Review

TL;DR

Graph Neural Networks offer a powerful framework for financial fraud detection by modeling complex relational patterns in transaction networks. This paper provides a unified framework for classifying GNN methodologies, reviews over 100 studies, and discusses real-world deployment challenges and resources. It demonstrates that GNNs outperform traditional fraud detectors by automatically learning features and leveraging dynamic and multimodal data. The paper also outlines gaps and future directions to enhance scalability, interpretability, and cross-domain integration in financial systems.

Abstract

The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies.

Figures (2)

• Figure 1: An overview of the road map of this paper. We first introduce the mainly utilized graph neural networks (GNNs) for financial fraud detection. Secondly, we summarize the reasons for choosing GNNs. After that, we introduce the methods and special tricks in GNN architecture design for financial fraud detection tasks. Then we go through the real-world applications and future directions for GNN-based financial fraud detection.
• Figure 2: The tasks of GNNs at different levels. For node level, the label of each node is determined by its features and neighbors. For edge level, the label of each edge is determined by the features of source & target node. For graph level, the property is determined by the features of all nodes or edges.