Graph Neural Networks for Financial Transaction Fraud Detection and Risk Analysis
Keywords:
graph neural networks, fraud detection, transaction risk, graph representation learning, fairness, governanceAbstract
Financial transaction fraud detection has become a systemic challenge that extends beyond isolated account anomalies to coordinated networks of accounts, devices, merchants, and intermediaries. Traditional feature-based classifiers often fail to capture relational dependencies that indicate organized fraud, money laundering, or synthetic identity operations. Graph neural networks offer a promising architectural response by learning representations that propagate risk signals across transaction topologies. This paper presents a system-level examination of graph neural networks for financial transaction fraud detection and risk analysis. It analyzes the structural trade-offs between homogeneous and heterogeneous graph models, transductive and inductive inference, shallow and deep message passing, and edge-level versus node-level risk modeling. The discussion emphasizes that graph construction, temporal dynamics, and deployment constraints are as important as model architecture in production environments. The paper further examines governance issues including explainability, fairness, adversarial robustness, model risk management, and regulatory accountability. It explores infrastructure requirements for streaming graph updates, low-latency inference, reproducibility, and sustainability. Cross-domain comparisons with payment networks, telecommunications fraud, and insurance claim systems are used to illustrate recurring design tensions. The analysis concludes that graph neural networks can substantially improve detection of relational fraud, but only when embedded within a broader socio-technical architecture that includes rule-based safeguards, investigator feedback loops, fairness audits, and policy-aware data sharing mechanisms.
References
1. Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, 29(3), 626–688.
2. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations.
3. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems.
4. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. International Conference on Learning Representations.
5. Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., & Song, L. (2018). Heterogeneous graph neural networks for malicious account detection. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2077–2085.
6. Weber, M., Domeniconi, G., Chen, J., Weidele, D. K. I., Bellei, C., Robinson, T., & Leiserson, C. E. (2019). Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2610–2620.
7. Pourhabibi, T., Ong, K.-L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection algorithms. Decision Support Systems, 133, 113303.
8. Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How powerful are graph neural networks? International Conference on Learning Representations.
9. Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., & Sun, X. (2020). Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. Proceedings of the AAAI Conference on Artificial Intelligence, 34(4), 3438–3445.
10. Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019). Heterogeneous graph attention network. Proceedings of the World Wide Web Conference, 2022–2032.
11. Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009). The graph neural network model. IEEE Transactions on Neural Networks, 20(1), 61–80.
12. Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019). Graph convolutional networks: A comprehensive review. Computational Social Networks, 6(1), 11.
13. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018). Graph convolutional neural networks for web-scale recommender systems. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 974–983.
14. Li, Q., Han, Z., & Wu, X.-M. (2018). Deeper insights into graph convolutional networks for semi-supervised learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 3538–3545.
15. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
16. Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. MIT Press.
17. Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative study. Proceedings of the IEEE International Conference on Computing, Networking and Informatics, 1–6.
18. Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10), 4915–4928.
19. Shchur, O., Mumme, M., Bojchevski, A., & Günnemann, S. (2018). Pitfalls of graph neural network evaluation. NeurIPS Workshop on Relational Representation Learning.
20. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 214–226.
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