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 supervised learning algorithms, namely Random Forest (RF) to detect illicit transactions for 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 baseline in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in cryptocurrency illicit transaction detection.

翻译：使用私隐可以隐藏犯罪身份,并通过其犯罪数字钱包转移数亿美元脏钱。然而,这被认为是一种自相矛盾,因为私隐是用于公开来源情报的金矿,使执法机构在进行法证分析方面拥有更大的权力。本文建议检查-L,一个基于自我监督的深图信息(DGI)的图形神经网络框架,有监督的学习算法,即随机森林(Roming Forest)来侦查反洗钱的非法交易。据我们所知,我们的建议是首先对比特库因的反洗钱问题采用自我监督的GNNs。对Elliptic数据集进行了评估,并表明我们的方法超越了关键分类指标的基线,这表明了在加密非法交易检测中自我监督的GNN(R)的潜力。