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Anti-Money Laundering Tracker Using Graph-AI
Authors
1 2 3 4 Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India. 5 Assistant Professor, Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India.
Article Information
- Published In
- Volume 6, Issue 6 (2026)
- Published Online
- November-December 2026
- Pages
- 44-49
- Article Type
- Original Article
References
1. Apache Software Foundation. (n.d.). Apache Kafka documentation. https://kafka.apache.org/documentation/
2. Islam, S., Gupta, G. R., Chakraborty, A., Singh, S., Soni, A., et al. (2025). Detecting fraudulent transactions for different patterns in financial networks using Layer Weighted GCN. Human-Centric Intelligent Systems, 5, 181–195.
3. Neo4j. (n.d.). Cypher query language manual. https://neo4j.com/docs/cypher-manual/current/
4. Neo4j. (n.d.). Neo4j graph database documentation. https://neo4j.com/docs/
5. Neo4j. (n.d.). Neo4j Graph Data Science library manual. https://neo4j.com/docs/graph-data-science/current/
6. PyTorch Geometric. (n.d.). PyTorch Geometric documentation. https://pytorch-geometric.readthedocs.io/
7. Shi, Y., & Tang, M. (2026). Uncertainty-aware spatio-temporal contrastive graph neural networks for cyber financial fraud detection and risk management. Scientific Reports, 16, Article 25068.
8. Suzumura, T., & Kanezashi, H. (2021). Anti-money laundering datasets: InPlusLab Anti-Money Laundering Data. IBM AMLSim project. https://github.com/IBM/AMLSim
9. Tong, G., & Shen, J. (2023). Financial transaction fraud detector based on imbalance learning and graph neural network. Applied Soft Computing, 149, Article 110984.
10. Weber, M., Chen, J., Suzumura, T., Pareja, A., Ma, M., Kanezashi, H., Kaler, T., Leiserson, C. E., & Schardl, T. B. (2018). Scalable graph learning for anti-money laundering: A first look. arXiv:1812.00076. https://arxiv.org/abs/1812.00076
11. 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. KDD ’19 Workshop on Anomaly Detection in Finance.
2. Islam, S., Gupta, G. R., Chakraborty, A., Singh, S., Soni, A., et al. (2025). Detecting fraudulent transactions for different patterns in financial networks using Layer Weighted GCN. Human-Centric Intelligent Systems, 5, 181–195.
3. Neo4j. (n.d.). Cypher query language manual. https://neo4j.com/docs/cypher-manual/current/
4. Neo4j. (n.d.). Neo4j graph database documentation. https://neo4j.com/docs/
5. Neo4j. (n.d.). Neo4j Graph Data Science library manual. https://neo4j.com/docs/graph-data-science/current/
6. PyTorch Geometric. (n.d.). PyTorch Geometric documentation. https://pytorch-geometric.readthedocs.io/
7. Shi, Y., & Tang, M. (2026). Uncertainty-aware spatio-temporal contrastive graph neural networks for cyber financial fraud detection and risk management. Scientific Reports, 16, Article 25068.
8. Suzumura, T., & Kanezashi, H. (2021). Anti-money laundering datasets: InPlusLab Anti-Money Laundering Data. IBM AMLSim project. https://github.com/IBM/AMLSim
9. Tong, G., & Shen, J. (2023). Financial transaction fraud detector based on imbalance learning and graph neural network. Applied Soft Computing, 149, Article 110984.
10. Weber, M., Chen, J., Suzumura, T., Pareja, A., Ma, M., Kanezashi, H., Kaler, T., Leiserson, C. E., & Schardl, T. B. (2018). Scalable graph learning for anti-money laundering: A first look. arXiv:1812.00076. https://arxiv.org/abs/1812.00076
11. 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. KDD ’19 Workshop on Anomaly Detection in Finance.
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