Multi Agent Based Simulation (MABS) of Financial Transactions for Anti Money Laundering (AML)

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Edgar Alonso Lopez-Rojas, Stefan Axelsson
Title: Multi Agent Based Simulation (MABS) of Financial Transactions for Anti Money Laundering (AML)
Conference name: Nordic Conference on Secure IT Systems
Year: 2012
Pagination: 25-32
ISBN: 978-91-7295-973-6
Publisher: Blekinge Institute of Technology
City: Karlskrona
Other identifiers: Short-Paper Proceedings
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
http://www.bth.se/com
Authors e-mail: edgar.lopez@bth.se, stefan.axelsson@bth.se
Language: English
Abstract: Mobile money is a service for performing financial transactions using a mobile phone. By law it has to have protection against money laundering and other types of fraud. Research into fraud detection methods is not as advanced as in other similar fields. However, getting access to real world data is difficult, due to the sensitive nature of financial transactions, and this makes research into detection methods difficult.
Thus, we propose an approach based on a Multi-Agent Based Simulation (MABS) for the generation of synthetic transaction data. We present the generation of synthetic data logs of
transactions and the use of such a data set for the study of different detection scenarios using machine learning.
Summary in Swedish: Vi föreslår en strategi som bygger på en Multi-Agent simulering (MAb) för generering av syntetiska transaktionsdata. Vi presenterar generering av syntetiska dataloggarna i transaktioner och användning av sådana en datamängd för att studera olika upptäckt scenarier med hjälp maskininlärning.
Subject: Computer Science\Artificial Intelligence
Computer Science\Electronic security
Computer Science\Effects on Society
Keywords: Machine Learning, Anti-Money Laundering, Money Laundering, Synthetic Data, Data Simulation, Multi- Agent Based Simulation, Fraud detection
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