AI against money laundering networks: the Colombian case
| Date | 04 June 2020 |
| DOI | https://doi.org/10.1108/JMLC-04-2020-0033 |
| Pages | 49-62 |
| Published date | 04 June 2020 |
| Subject Matter | Accounting & Finance,Financial risk/company failure,Financial compliance/regulation,Financial crime |
| Author | Olmer Garcia-Bedoya,Oscar Granados,José Cardozo Burgos |
AI against money laundering
networks: the Colombian case
Olmer Garcia-Bedoya
Department of Engineering, Universidad de Bogota Jorge Tadeo Lozano,
Bogota, Colombia
Oscar Granados
Department of Economics, Universidad de Bogota Jorge Tadeo Lozano,
Bogota, Colombia, and
José Cardozo Burgos
Todosistemas STI, Bogota, Colombia
Abstract
Purpose –The purpose of this paper is to examine the artificial intelligence (AI) methodologies to fight
againstmoney laundering crimes in Colombia.
Design/methodology/approach –This paper examines Colombian money laundering situations with
some methodologiesof network science to apply AI tools.
Findings –This paper identifies the suspiciousoperations with AI methodologies, which are not common
by number, quantity or characteristics within the economic or financial system and normal practices of
companiesor industries.
Research limitations/implications –Access to financial institutions’data was the most difficult element
for research because affectthe implementation of a set of different algorithms and network science methodologies.
Practical implications –This paper tries to reduce the social and economic implications from money
laundering (ML) that result from illegal activities and different crimes against inhabitants, governments,
public resourcesand financial systems.
Social implications –This paper proposes a software architecturemethodology to fight against ML and
financial crime networks in Colombia which are common in different countries. These methodologies
complementlegal structure and regulatory framework.
Originality/value –The contribution of this paper is howwithin the flow already regulated by financial
institutions to managethe ML risk, AI can be used to minimize and identify this kind of risk. For this reason,
the authors propose to use the graph analysis methodologyfor monitoring and identifying the behavior of
different ML patterns with machine learning techniques and network science methodologies. These
methodologiescomplement legal structure and regulatory framework.
Keywords Money laundering, Artificial intelligence, Software architecture,
Financial crime networks
Paper type Research paper
Introduction
Criminal activities are increasingly diverse and have been carried out in different physical
and digital spaces. These activities involve a growing group of people, organizations and
The authors would like to thank anti-money laundering expert members in different financial
organizations. This project received financial support of Minciencias from call for research proposals
816–2018, Project Grant 696481765389.
Money
laundering
networks
49
Journalof Money Laundering
Control
Vol.24 No. 1, 2021
pp. 49-62
© Emerald Publishing Limited
1368-5201
DOI 10.1108/JMLC-04-2020-0033
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