IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2203.10465.html
   My bibliography  Save this paper

Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin

Author

Listed:
  • Wai Weng Lo
  • Gayan K. Kulatilleke
  • Mohanad Sarhan
  • Siamak Layeghy
  • Marius Portmann

Abstract

Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transfer hundreds of millions of dollars of dirty funds through their criminal digital wallets. However, this is considered a paradox because cryptocurrencies are goldmines for open-source intelligence, giving law enforcement agencies more power when conducting forensic analyses. This paper proposed Inspection-L, a graph neural network (GNN) framework based on a self-supervised Deep Graph Infomax (DGI) and Graph Isomorphism Network (GIN), with supervised learning algorithms, namely Random Forest (RF), to detect illicit transactions for anti-money laundering (AML). To the best of our knowledge, our proposal is the first to apply self-supervised GNNs to the problem of AML in Bitcoin. The proposed method was evaluated on the Elliptic dataset and shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in the detection of illicit cryptocurrency transactions.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2203.10465
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2203.10465
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Zeinab Rouhollahi, 2021. "Towards Artificial Intelligence Enabled Financial Crime Detection," Papers 2105.10866, arXiv.org.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2203.10465. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.