Anomaly Detection applied to Money Laundering Detecion using Ensemble Learning

Daniel Otero Gomez, Santiago Cartagena Agudelo, Andres Ospina Patiño and Edgar Lopez-Rojas

No f84ht, OSF Preprints from Center for Open Science

Abstract: Financial crime and, specifically, the illegal business of money laundering are increasing dramatically with the expansion of modern technology and global communication, resulting in the loss of billions of dollars worldwide each year. Money laundering, known as the process that transforms the proceeds of crime into clean legitime assets, is a common phenomenon that occurs around the world. Irregular obtained money is generally cleaned up thanks to transfers involving banks or companies, see Walker (1999). Hence, one of the main problems remains to find an efficient way to identify suspicious actors and transactions, in each operation attention should be focused on the type, amount, motive, frequency, and consistency with the previous activity and the geographic area. This identification must be the result of a process that cannot be based solely on individual judgments but must, at least in part, be automated. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to overcome such measures, see Perols (2011). Then, what we propose is to enrich this set of information by building an anomaly detection model in operations related to money transfer in order to benefit from the power of artificial intelligence. Now, anti­money laundering is a complex problem but We believe Artificial Intelligence can play a powerful role in this area.

Date: 2021-12-08
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:f84ht

DOI: 10.31219/osf.io/f84ht

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