Towards Collaborative Anti-Money Laundering Among Financial Institutions

Zhihua Tian, Yuan Ding, Jian Liu, XIANG YU, Enchao Gong, Kui Ren

Track: Economics, online markets and human computation

Keywords: Anti-money laundering, Collaborative anti-money laundering, Graph mining

TL;DR: We propose a collaborative anti-money laundering algorithm that supports detecting money laundering groups over transaction graphs of multiple institutions while protecting their privacy.

Abstract:

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 widely used in the early days and still be widely used in existing detection systems. With the rise of machine learning, graph-based learning methods have gained prominence in detecting illicit accounts by analyzing money transfer graphs between accounts. However, existing approaches work based on the prerequisite that the transaction graph is centralized, while 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, limiting 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.

Submission Number: 1196