Money Laundering Detection using Synthetic Data
2012 (English)Conference paper (Refereed) Published
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).
Place, publisher, year, edition, pages
Örebro, Sweden: Linköping University Electronic Press, Linköpings universitet , 2012.
Machine Learning, Anti-Money Laundering, Money Laundering, Anomaly Detection, Synthetic Data, Multi-Agent Based Simulation
IdentifiersURN: urn:nbn:se:bth-7119Local ID: oai:bth.se:forskinfoEB952EA69906EE79C1257AC6003BB536OAI: oai:DiVA.org:bth-7119DiVA: diva2:834701
Annual workshop of the Swedish Artificial Intelligence Society (SAIS)
Linkoping Press http://www.ep.liu.se/ecp_article/index.en.aspx?issue=071;article=0052012-12-062012-11-302015-06-30Bibliographically approved