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Authors:Edgar Alonso Lopez-Rojas: School of Computing, Blekinge Institute of Technology, Sweden
Stefan Axelsson: School of Computing, Blekinge Institute of Technology, Sweden
Publication title:Money Laundering Detection using Synthetic Data
Conference:The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14–15 May 2012; Örebro; Sweden
Publication type: Abstract and Fulltext
Issue:071
Article No.:005
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).

Language:English
Keywords:Machine Learning; Anti-Money Laundering; Money Laundering; Anomaly Detection; Synthetic Data; Multi-Agent Based Simulation
Year:2012
No. of pages:8
Pages:33-40
Series:Linköping Electronic Conference Proceedings
ISSN (print):1650-3686
ISSN (online):1650-3740
File:http://www.ep.liu.se/ecp/071/005/ecp12071005.pdf
Available:2012-05-14
Publisher:Linköping University Electronic Press; Linköpings universitet

REFERENCE TO THIS PAGE
Edgar Alonso Lopez-Rojas, Stefan Axelsson (2012). Money Laundering Detection using Synthetic Data, The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14–15 May 2012; Örebro; Sweden http://www.ep.liu.se/ecp_article/index.en.aspx?issue=071;article=005 (accessed 12/23/2014)