Graph Neural Networks for Financial Fraud Detection: A Review

ArXiv ID: 2411.05815 “View on arXiv”

Authors: Unknown

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.

Keywords: Graph Neural Networks (GNNs), Financial Fraud Detection, Relational Patterns, Financial Networks, Fraud Management

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper presents a high-level review of Graph Neural Network methodologies, featuring mathematical definitions and structural concepts, but lacks specific implementation details, code, backtests, or empirical performance metrics.

  flowchart TD
    A["Research Goal<br/>To review GNN applications<br/>in financial fraud detection"] --> B["Methodology<br/>Structured review of 100+ studies"]
    
    B --> C["Key Input<br/>Relational financial network data<br/>(Transactions & Entities)"]
    
    C --> D["Computational Process<br/>GNN Frameworks<br/>(GCN, GAT, GraphSAGE, etc.)"]
    
    D --> E{"Key Findings & Outcomes"}
    
    E --> F["Superior Performance<br/>Captures complex relational patterns<br/>outperforming traditional methods"]
    
    E --> G["Research Gaps<br/>Identified challenges in<br/>real-world deployment"]
    
    E --> H["Future Directions<br/>Guidelines for enhanced<br/>integration strategies"]
    
    F --> I["Contribution<br/>Unified framework for<br/>financial fraud detection"]