Table of Contents

Research Handbook on Money Laundering

Research Handbook on Money Laundering

Elgar original reference

Edited by Brigitte Unger and Daan van der Linde

Although the practice of disguising the illicit origins of money dates back thousands of years, the concept of money laundering as a multidisciplinary topic with social, economic, political and regulatory implications has only gained prominence since the 1980s. This groundbreaking volume offers original, state-of-the-art research on the current money laundering debate and provides insightful predictions and recommendations for future developments in the field.

Chapter 8: Measuring money laundering threat

Jakub Brettl

Subjects: economics and finance, economic crime and corruption, law - academic, corruption and economic crime, politics and public policy, terrorism and security


The goal of the chapter is two-fold. First we introduce a money laundering (ML) threat assessment method, originally developed by Brettl and Usov (2010) and present its results. Secondly, we will compare the methodology and its results with the one produced by Walker (2011). Both methods were applied to a sample of European Union member states. The Brettl-Usov method is based on the computation of a so-called Threat Index that measures the degree to which a particular country is attractive to money launderers relative to other countries. The approach is unique in that it explicitly distinguishes between five different types of money laundering offences (e.g. drug crime ML) and hence it can identify not only the overall ML threat a country faces, but also the degree to which each ML offence contributes to the overall threat. As a result, the method produces six different indices – one overall threat index and five threat indices for each ML offence (as classified by Reuter and Truman 2004). Walker takes a completely different angle when looking at ML threat. His method is based on the application of a gravity model/formula in order to compute the amount of criminal proceeds that can potentially be laundered in a jurisdiction of interest. The gravity model is augmented for a number of factors, besides geographical distance, that ought to influence international money laundering flows.

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