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Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

Author

Listed:
  • Mark Weber
  • Giacomo Domeniconi
  • Jie Chen
  • Daniel Karl I. Weidele
  • Claudio Bellei
  • Tom Robinson
  • Charles E. Leiserson

Abstract

Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency has introduced an intriguing paradox: pseudonymity allows criminals to hide in plain sight, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. Meanwhile advances in learning algorithms show great promise for the AML toolkit. In this workshop tutorial, we motivate the opportunity to reconcile the cause of safety with that of financial inclusion. We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency. We share results from a binary classification task predicting illicit transactions using variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of special interest as an emergent new method for capturing relational information. The results show the superiority of Random Forest (RF), but also invite algorithmic work to combine the respective powers of RF and graph methods. Lastly, we consider visualization for analysis and explainability, which is difficult given the size and dynamism of real-world transaction graphs, and we offer a simple prototype capable of navigating the graph and observing model performance on illicit activity over time. With this tutorial and data set, we hope to a) invite feedback in support of our ongoing inquiry, and b) inspire others to work on this societally important challenge.

Suggested Citation

  • Mark Weber & Giacomo Domeniconi & Jie Chen & Daniel Karl I. Weidele & Claudio Bellei & Tom Robinson & Charles E. Leiserson, 2019. "Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics," Papers 1908.02591, arXiv.org.
  • Handle: RePEc:arx:papers:1908.02591
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    Citations

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    Cited by:

    1. Nasir Sultan & Norazida Mohamed & Mervyn Martin & Hafizah Mohd Latif, 2023. "Virtual currencies and money laundering: existing and prospects for jurisdictions that comprehensively prohibited virtual currencies like Pakistan," Journal of Money Laundering Control, Emerald Group Publishing Limited, vol. 27(2), pages 395-412, May.
    2. Zeinab Rouhollahi, 2021. "Towards Artificial Intelligence Enabled Financial Crime Detection," Papers 2105.10866, arXiv.org.
    3. Yang, Guo-Hui & Zhong, Guang-Yan & Wang, Li-Ya & Xie, Zu-Guang & Li, Jiang-Cheng, 2024. "A hybrid forecasting framework based on MCS and machine learning for higher dimensional and unbalanced systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    4. Alexander Wong & Andrew Hryniowski & Xiao Yu Wang, 2020. "Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning," Papers 2011.01961, arXiv.org.
    5. Claudio Bellei & Muhua Xu & Ross Phillips & Tom Robinson & Mark Weber & Tim Kaler & Charles E. Leiserson & Arvind & Jie Chen, 2024. "The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset," Papers 2404.19109, arXiv.org, revised Jul 2024.
    6. Ourania Theodosiadou & Alexandros-Michail Koufakis & Theodora Tsikrika & Stefanos Vrochidis & Ioannis Kompatsiaris, 2023. "Change Point Analysis of Time Series Related to Bitcoin Transactions: Towards the Detection of Illegal Activities," JRFM, MDPI, vol. 16(9), pages 1-20, September.
    7. Wai Weng Lo & Gayan K. Kulatilleke & Mohanad Sarhan & Siamak Layeghy & Marius Portmann, 2022. "Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin," Papers 2203.10465, arXiv.org, revised Oct 2022.
    8. Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021. "A Review on Graph Neural Network Methods in Financial Applications," Papers 2111.15367, arXiv.org, revised Apr 2022.
    9. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).

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