Article | The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14-15 May 2012; √Ėrebro; Sweden | Money Laundering Detection using Synthetic Data

Title:
Money Laundering Detection using Synthetic Data
Author:
Edgar Alonso Lopez-Rojas: School of Computing, Blekinge Institute of Technology, Sweden Stefan Axelsson: School of Computing, Blekinge Institute of Technology, Sweden
Download:
Full text (pdf)
Year:
2012
Conference:
The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14-15 May 2012; √Ėrebro; Sweden
Issue:
071
Article no.:
005
Pages:
33-40
No. of pages:
8
Publication type:
Abstract and Fulltext
Published:
2012-05-14
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


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).

Keywords: Machine Learning; Anti-Money Laundering; Money Laundering; Anomaly Detection; Synthetic Data; Multi-Agent Based Simulation

The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14-15 May 2012; √Ėrebro; Sweden

Author:
Edgar Alonso Lopez-Rojas, Stefan Axelsson
Title:
Money Laundering Detection using Synthetic Data
References:

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The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14-15 May 2012; √Ėrebro; Sweden

Author:
Edgar Alonso Lopez-Rojas, Stefan Axelsson
Title:
Money Laundering Detection using Synthetic Data
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