Temporal Graph Networks for Graph Anomaly Detection in Financial Networks (2404.00060v1)
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.
- Graph based anomaly detection and description: a survey. Data mining and knowledge discovery, 29: 626–688.
- Realistic Synthetic Financial Transactions for Anti-Money Laundering Models. arXiv preprint arXiv:2306.16424.
- Hypergraph convolution and hypergraph attention. Pattern Recognition, 110: 107637.
- You are allset: A multiset function framework for hypergraph neural networks. arXiv preprint arXiv:2106.13264.
- Hnhn: Hypergraph networks with hyperedge neurons. arXiv preprint arXiv:2006.12278.
- Hypergraph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 33, 3558–3565.
- Inductive representation learning on large graphs. Advances in neural information processing systems, 30.
- Dgraph: A large-scale financial dataset for graph anomaly detection. Advances in Neural Information Processing Systems, 35: 22765–22777.
- The adoption of mobile payment services for “Fintech”. International Journal of Applied Engineering Research, 11(2): 1058–1061.
- Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
- Set transformer: A framework for attention-based permutation-invariant neural networks. In International conference on machine learning, 3744–3753. PMLR.
- The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision support systems, 50(3): 559–569.
- Temporal Graph Networks for Deep Learning on Dynamic Graphs. arXiv:2006.10637.
- Graph attention networks. arXiv preprint arXiv:1710.10903.
- Intelligent financial fraud detection: a comprehensive review. Computers & security, 57: 47–66.
- Yejin Kim (35 papers)
- Youngbin Lee (9 papers)
- Minyoung Choe (9 papers)
- Sungju Oh (1 paper)
- Yongjae Lee (28 papers)