Money laundering is the process that intends to legalize the income derived from illicit activities, thus facilitating their entry into the monetary flow of the economy without jeopardizing their source. It is crucial to identify such activities accurately and reliably in order to enforce anti-money laundering (AML). Despite considerable efforts to AML, a large number of such activities still go undetected. Rule-based methods were first introduced and are still widely used in current detection systems. With the rise of machine learning, graph-based learning methods have gained prominence in detecting illicit accounts through the analysis of money transfer graphs. Nevertheless, these methods generally assume that the transaction graph is centralized, whereas in practice, money laundering activities usually span multiple financial institutions. Due to regulatory, legal, commercial, and customer privacy concerns, institutions tend not to share data, restricting their utility in practical usage. In this paper, we propose the first algorithm that supports performing AML over multiple institutions while protecting the security and privacy of local data. To evaluate, we construct Alipay-ECB, a real-world dataset comprising digital transactions from Alipay, the world's largest mobile payment platform, alongside transactions from E-Commerce Bank (ECB). The dataset includes over 200 million accounts and 300 million transactions, covering both intra-institution transactions and those between Alipay and ECB. This makes it the largest real-world transaction graph available for analysis. The experimental results demonstrate that our methods can effectively identify cross-institution money laundering subgroups. Additionally, experiments on synthetic datasets also demonstrate that our method is efficient, requiring only a few minutes on datasets with millions of transactions.

翻译：洗钱是指将非法活动所得收入合法化的过程，从而使其在不暴露来源的情况下进入经济货币流通。准确可靠地识别此类活动对于实施反洗钱（AML）至关重要。尽管在反洗钱方面付出了大量努力，仍有大量此类活动未被发现。基于规则的方法最早被引入，并且在当前检测系统中仍被广泛使用。随着机器学习的兴起，基于图的学习方法通过分析资金转移图在检测非法账户方面获得了显著关注。然而，这些方法通常假设交易图是集中式的，而在实践中，洗钱活动通常跨越多个金融机构。由于监管、法律、商业和客户隐私方面的考虑，各机构往往不愿共享数据，这限制了这些方法在实际应用中的效用。在本文中，我们提出了首个支持在保护本地数据安全与隐私的前提下，跨多个机构执行反洗钱的算法。为进行评估，我们构建了Alipay-ECB数据集，这是一个包含来自全球最大移动支付平台支付宝的数字交易以及电子商务银行（ECB）交易的真实数据集。该数据集涵盖超过2亿个账户和3亿笔交易，既包括机构内交易，也包含支付宝与ECB之间的交易。这使其成为可用于分析的最大规模真实世界交易图。实验结果表明，我们的方法能够有效识别跨机构洗钱子群。此外，在合成数据集上的实验也证明我们的方法是高效的，在处理包含数百万笔交易的数据集时仅需几分钟。