Graph Neural Networks for Financial Fraud Detection: A Review

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.

17 Pages, 2 Figures

References in corpus (8)

• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
• GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks
• Causal Attention for Interpretable and Generalizable Graph Classification
• Deep Learning applied to NLP
• Financial Fraud Detection using Quantum Graph Neural Networks
• Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching
• Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness

Cited by in corpus (4)

• MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU
• Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection
• Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection
• Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection