Money Laundering Detection using Synthetic Data

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Edgar Alonso Lopez-Rojas, Stefan Axelsson
Title: Money Laundering Detection using Synthetic Data
Conference name: Annual workshop of the Swedish Artificial Intelligence Society (SAIS)
Year: 2012
Pagination: 33-40
Publisher: Linköping University Electronic Press, Linköpings universitet
City: Örebro, Sweden
Other identifiers: ISSN (print): 1650-3686
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
Authors e-mail:,
Language: English
Abstract: Criminals use money laundering to make the proceeds from their illegal activities look legitimate in the eyes of the rest of society. Current countermeasures taken by financial organizations are based on legal requirements and very basic statistical analysis. Machine Learning offers a number of ways to detect anomalous transactions. These methods can be based on supervised and unsupervised learning algorithms that improve the performance of detection of such criminal activity.
In this study we present an analysis of the difficulties and considerations of applying machine learning techniques to this problem. We discuss the pros and cons of using synthetic data and problems and advantages inherent in the generation of such a data set. We do this using a case study and suggest an approach based on Multi-Agent Based Simulations (MABS).
Subject: Computer Science\Artificial Intelligence
Computer Science\Electronic security
Computer Science\Effects on Society
Keywords: Machine Learning, Anti-Money Laundering, Money Laundering, Anomaly Detection, Synthetic Data, Multi-Agent Based Simulation
Note: Linkoping Press;article=005