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.
TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Institute of Financial Technology, Fudan University（复旦大学金融技术研究所）；Peking University（北京大学）
Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling
ING Bank Amsterdam, The Netherlands Eindhoven University of Technology Eindhoven, The Netherlands；Identity \& Risk Intelligence Adyen Amsterdam, The Netherlands；Mathematics \& Computer Science Eindhoven University of Technology Eindhoven, The Netherlands
AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions
School of Information and Communication Technology, Griffith University, QLD Australia（信息与通信技术学院，格里菲斯大学，昆士兰州澳大利亚）；School of Information and Physical Sciences, The University of Newcastle, NSW Australia（信息与物理科学学院，新castle大学，新南威尔士州澳大利亚）
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
KU Leuven University of Antwerp - imec（库勒芬大学安特卫普大学-imec）；KU Leuven University of Southampton（库勒芬大学南安普顿大学）；University of Antwerp - imec（安特卫普大学-imec）；KU Leuven（库勒芬大学）
FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering
Dept. of Multidisciplinary Engineering, Texas A&M University（多学科工程系，德克萨斯A&M大学）；Dept. of Information Systems, King Abdulaziz University（信息系统系，国王阿卜杜勒阿齐兹大学）；Dept. of Computer Science & Cybersecurity, Metro State University（计算机科学与网络安全系，梅特罗州立大学）；Dept. of Engineering Technology & Industrial Distribution, Texas A&M University（工程技术与工业分布系，德克萨斯A&M大学）
Exploring Multi-Banking Customer-to-Customer Relations in AML Context with Poincaré Embeddings
IBM Global Business Services（IBM全球企业咨询服务部）；IBM Lithuania, Client Innovation Center Baltic（IBM立陶宛波罗的海客户创新中心）；IBM T.J. Watson Research Center（IBM托马斯·J·沃森研究中心）
Towards Federated Graph Learning for Collaborative Financial Crimes Detection
IBM T.J. Watson Research Center（IBM T.J. 沃森研究中心）；IBM Research Almaden（IBM阿尔马登研究院）；IBM Research Tokyo（IBM东京研究院）；IBM Global Business Services（IBM全球企业咨询服务部）
