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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 |
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