Towards Collaborative Anti-Money Laundering Among Financial Institutions

2025-02-27

AI Summary

To address the challenge of collaborative anti-money laundering (AML) modeling across financial institutions hindered by data silos, this paper proposes FedAML—the first privacy-preserving federated graph learning framework. FedAML integrates federated graph neural networks, differentially private graph aggregation, and distributed subgraph anomaly detection, enabling multiple institutions to jointly train models without sharing raw transaction data. Evaluated on Alipay-ECB—the first large-scale, real-world cross-institutional transaction graph dataset (200M accounts, 300M transactions)—FedAML consistently outperforms centralized baselines in both real and synthetic AML scenarios, detecting money laundering subgraphs among millions of transactions within minutes. This work establishes the first end-to-end, scalable, and high-accuracy solution for privacy-preserving cross-institutional money laundering subgroup identification.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Blockchains and distributed ledgers

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 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.

Problem

• Detecting cross-institution money laundering activities
• Protecting data security and privacy in AML
• Enhancing AML with collaborative financial institution algorithms

Innovation

• Graph-based learning for AML
• Cross-institution data collaboration
• Privacy-preserving algorithm design