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Original Article

Anti-Money Laundering Tracker Using Graph-AI

  • Paper ID 415
  • Volume 6, Issue 6
  • Pages 44-49
  • Published Online: November-December 2026
Open Access Peer Reviewed Crossref DOI

Authors

  • Hima J Ajjapla1
  • Manasa M2
  • Megha M3
  • Nanditha N4
  • Dr. Arun Kumar G. H5

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 Online
November-December 2026
Pages
44-49
Article Type
Original Article
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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.
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  • CrossRef DOI
  • ISSN 2583-1240
  • Open Access
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