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Estimating money laundering flows with a gravity model-based simulation
It is important to understand the amounts and types of money laundering flows, since they have very different effects and, therefore, need different enforcement strategies. Countries that mainly deal with criminals laundering their proceeds locally, need other measures than countries that mainly dea...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596494/ https://www.ncbi.nlm.nih.gov/pubmed/33122829 http://dx.doi.org/10.1038/s41598-020-75653-x |
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author | Ferwerda, Joras van Saase, Alexander Unger, Brigitte Getzner, Michael |
author_facet | Ferwerda, Joras van Saase, Alexander Unger, Brigitte Getzner, Michael |
author_sort | Ferwerda, Joras |
collection | PubMed |
description | It is important to understand the amounts and types of money laundering flows, since they have very different effects and, therefore, need different enforcement strategies. Countries that mainly deal with criminals laundering their proceeds locally, need other measures than countries that mainly deal with foreign illegal investments or dirty money just flowing through the country. This paper has two main contributions. First, we unveil the country preferences of money launderers empirically in a systematic way. Former money laundering estimates used assumptions on which country characteristics money launderers are looking for when deciding where to send their ill-gotten gains. Thanks to a unique dataset of transactions suspicious of money laundering, provided by the Dutch Institute infobox Criminal and Unexplained Wealth (iCOV), we can empirically test these assumptions with an econometric gravity model estimation. We use this information for our second contribution: iteratively simulating all money laundering flows around the world. This allows us, for the first time, to provide estimates that distinguish between three different policy challenges: the laundering of domestic crime proceeds, international investment of dirty money and money just flowing through a country. |
format | Online Article Text |
id | pubmed-7596494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75964942020-10-30 Estimating money laundering flows with a gravity model-based simulation Ferwerda, Joras van Saase, Alexander Unger, Brigitte Getzner, Michael Sci Rep Article It is important to understand the amounts and types of money laundering flows, since they have very different effects and, therefore, need different enforcement strategies. Countries that mainly deal with criminals laundering their proceeds locally, need other measures than countries that mainly deal with foreign illegal investments or dirty money just flowing through the country. This paper has two main contributions. First, we unveil the country preferences of money launderers empirically in a systematic way. Former money laundering estimates used assumptions on which country characteristics money launderers are looking for when deciding where to send their ill-gotten gains. Thanks to a unique dataset of transactions suspicious of money laundering, provided by the Dutch Institute infobox Criminal and Unexplained Wealth (iCOV), we can empirically test these assumptions with an econometric gravity model estimation. We use this information for our second contribution: iteratively simulating all money laundering flows around the world. This allows us, for the first time, to provide estimates that distinguish between three different policy challenges: the laundering of domestic crime proceeds, international investment of dirty money and money just flowing through a country. Nature Publishing Group UK 2020-10-29 /pmc/articles/PMC7596494/ /pubmed/33122829 http://dx.doi.org/10.1038/s41598-020-75653-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ferwerda, Joras van Saase, Alexander Unger, Brigitte Getzner, Michael Estimating money laundering flows with a gravity model-based simulation |
title | Estimating money laundering flows with a gravity model-based simulation |
title_full | Estimating money laundering flows with a gravity model-based simulation |
title_fullStr | Estimating money laundering flows with a gravity model-based simulation |
title_full_unstemmed | Estimating money laundering flows with a gravity model-based simulation |
title_short | Estimating money laundering flows with a gravity model-based simulation |
title_sort | estimating money laundering flows with a gravity model-based simulation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596494/ https://www.ncbi.nlm.nih.gov/pubmed/33122829 http://dx.doi.org/10.1038/s41598-020-75653-x |
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