Modeling International Money Laundering
hubie writes:
Money laundering comes in all shapes and sizes. Money can be handled domestically within the country where it was acquired, it can be moved internationally to a location deemed better suited to mask its origin, or a country could be an intermediary where money just flows though from Country A to Country B. These days there are inter-governmental efforts to monitor suspicious financial transactions to curb illicit money laundering, and key to these efforts are attempts to understand how ``dirty'' funds move around the world.
Researchers from The Netherlands and Austria developed a model to understand how money moves around. Their model is based upon a gravity model, which is very commonly used in the social sciences for modeling complex flow interactions such as commuter and pedestrian traffic, drug cartel activity, and international trade. These models get their name and inspiration from Newton's Law of Gravity which has that the interaction strength between bodies is dependent upon the sizes of the bodies and inversely with the distance between them.
Most money laundering, according to our simulations, happens in the United States and the United Kingdom, together responsible for 40% of all money laundering in the 36 OECD countries [Ed. note: the richest]. However, as a percentage of GDP, the amount of money laundering is highest in Belgium, Luxembourg, and Israel. Japan and South Korea have relatively the smallest money laundering problem.
Ferwerda, J., van Saase, A., Unger, B. et al. Estimating money laundering flows with a gravity model-based simulation. Sci Rep 10, 18552 (2020). https://doi.org/10.1038/s41598-020-75653-x
ABSTRACT:
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.
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