Inspection-L: Practical GNN-Based Money Laundering Detection System for Bitcoin

• URL: http://arxiv.org/abs/2203.10465v1
• Date: Sun, 20 Mar 2022 06:19:18 GMT
• Title: Inspection-L: Practical GNN-Based Money Laundering Detection System for Bitcoin
• Authors: Wai Weng Lo, Siamak Layeghy and Marius Portmann
• Abstract summary: This paper proposes Inspection-L, a graph neural network (GNN) framework based on self-supervised Deep Graph Infomax (DGI), with Random Forest (RF) to detect illicit transactions for Anti-Money laundering (AML) To the best of our knowledge, our proposal is the first of applying self-supervised GNNs to the problem of AML in Bitcoin. The proposed method has been evaluated on the Elliptic dataset and shows that our approach outperforms the state-of-the-art in terms of key classification metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 gold mines for open-source intelligence, allowing law enforcement agencies to have more power in conducting forensic analyses. This paper proposed Inspection-L, a graph neural network (GNN) framework based on self-supervised Deep Graph Infomax (DGI), with Random Forest (RF), to detect illicit transactions for Anti-Money laundering (AML). To the best of our knowledge, our proposal is the first of applying self-supervised GNNs to the problem of AML in Bitcoin. The proposed method has been 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 cryptocurrency illicit transaction detection.

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