Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin
This addresses the problem of detecting illicit transactions for law enforcement agencies in cryptocurrency, offering a novel approach but is incremental in applying existing techniques to a specific domain.
The paper tackled money laundering detection in Bitcoin by proposing Inspection-L, a self-supervised GNN framework, which outperformed state-of-the-art methods on the Elliptic dataset with improved classification metrics.
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.