Scalable Semi-Supervised Graph Learning Techniques for Anti Money Laundering

IEEE Access, vol. 12, pp. 50012–50029

Abstract

Money laundering is the process where criminals move large sums of illicit money to hidden locations and disguise them as legal funds using financial services. The United Nations (UN) estimates 2 to 5% of global GDP, which is approx. 0.8 to 2.0 trillion dollars are laundered globally every year. Therefore, accurate identification of such globally alarming activities is crucial to enforce anti-money laundering (AML) measures. To date numerous techniques have been proposed to detect money laundering from a graph of money transfers between bank accounts by analyzing the structural and behavioural dynamics of its dense subgraphs, thereby not taking into consideration that money laundering involves high-volume flows of funds through chains of bank accounts. Some other approaches model the transactions in the form of multipartite graphs to detect the complete flow of money from source to destination. However, most of the existing methods result in lower detection accuracy or higher computational cost, making them less reliable and practical for real financial systems. As a consequence, the current AML approaches can only prevent and detect a fraction of money laundering activities could be prevented. In this paper, we propose an efficient approach to AML by employing semi-supervised graph learning techniques on a large-scale financial transactional graph in both pipeline and end-to-end settings to identify nodes that may be involved in potential money laundering. We evaluated our approach on four datasets: AMLSim, Elliptic, IBM AML, and SynthAML with a view to its scalability and practicality for real financial systems. Further, we provide global (e.g., what factors contribute more in money laundering scenarios) and local (e.g., how money gets laundered between nodes) explanations to improve the interpretability of the AML model. Experimental results suggest that our approach is scalable as well as effective at spotting money laundering in both real and synthetic transaction graphs.

Authors 5

1. Md. Rezaul Karim corresponding Aachen Information Systems and Databases

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Information Systems and Databases, RWTH Aachen University, Aachen, Germany

   Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Germany

2. Felix Hermsen Aachen Information Systems and Databases

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Information Systems and Databases, RWTH Aachen University, Aachen, Germany

   Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Germany

3. Sisay Adugna Chala Aachen Information Systems and Databases

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Information Systems and Databases, RWTH Aachen University, Aachen, Germany

   Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Germany

4. Paola de Perthuis

   École Normale Supérieure - PSL

   Affiliation as printed

   École Normale Supérieure (ENS), Paris, France

5. Avikarsha Mandal Aachen Information Systems and Databases

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Sankt Augustin, Germany

   Information Systems and Databases, RWTH Aachen University, Germany