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

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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
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    References listed on IDEAS

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    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.
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