Temporal Graph Networks for Graph Anomaly Detection in Financial Networks

ArXiv ID: 2404.00060 “View on arXiv”

Authors: Unknown

Abstract

This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We present a comprehensive framework that leverages TGN, capable of capturing dynamic changes in edges within financial networks, for fraud detection. Our study compares TGN’s performance against static Graph Neural Network (GNN) baselines, as well as cutting-edge hypergraph neural network baselines using DGraph dataset for a realistic financial context. Our results demonstrate that TGN significantly outperforms other models in terms of AUC metrics. This superior performance underlines TGN’s potential as an effective tool for detecting financial fraud, showcasing its ability to adapt to the dynamic and complex nature of modern financial systems. We also experimented with various graph embedding modules within the TGN framework and compared the effectiveness of each module. In conclusion, we demonstrated that, even with variations within TGN, it is possible to achieve good performance in the anomaly detection task.

Keywords: Financial Anomaly Detection, Temporal Graph Networks, Fraud Detection, Graph Neural Networks, Dynamic Edges, Fintech/Payments

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced temporal graph neural network mathematics with custom embedding functions and attention mechanisms, while demonstrating strong empirical rigor through backtesting on a large-scale real-world financial dataset (DGraph) and comparing multiple baselines with AUC metrics.

  flowchart TD
    A["Research Goal:<br>Analyze TGN for Financial Anomaly Detection"] --> B["Dataset & Input<br>DGraph Financial Dataset<br>Dynamic Edges"]
    B --> C["Methodology: Model Comparison<br>TGN vs. Static GNNs<br>vs. Hypergraph NNs"]
    C --> D["Computational Process<br>Temporal Training<br>Embedding Module Analysis"]
    D --> E["Key Findings & Outcomes<br>TGN significantly outperforms baselines<br>in AUC metrics<br>Proves adaptability to dynamic<br>financial systems"]