Band 5, Heft 2 - Tackling Money Laundering

Money laundering - bringing illicit proceeds from drugs, fraud and other crime, back into the legal economy - has become an issue of international concern only in the last twenty years. Since 9/11 it has figured as a prominent issue of national and international safety on the agenda of international organizations such as the Financial Action Task Force, the IMF, the United Nations, the Bank of International Settlement, and the European Union. Since then, the need to know more about the volume of laundering and laundering techniques, about the behavior of launderers, the effects of laundering on crime and on the economy, and the potential for successful anti-money laundering policy, has dramatically increased. So far, the academic field of the economics of crime has not devoted much attention to financial crime. And the economics of finance has not dealt with criminal behavior The first three papers presented in this issue will show three very different ways of making use of economics to estimate money laundering. The last three papers show how the law and economics literature especially can be used to analyze the role of anti-money laundering policy.

Measuring global money laundering, the proceeds of transnational crime that are pumped through the financial system worldwide, is still in its infancy. Methods such as case studies, proxy variables, or models for measuring the shadow economy all tend to under- or overestimate money laundering. The model presented here is a gravity model which makes it possible to estimate the flows of illicit funds from and to each jurisdiction in the world and worldwide. This “Walker Model” was first developed in 1994, and used and updated recently. We show that it belongs to the group of gravity models which have recently become popular in international trade theory. Using triangulation, we demonstrate that the original Walker Model estimates are compatible with recent findings on money laundering. Once the scale of money laundering is known, its macroeconomic effects and the impact of crime prevention, regulation and law enforcement effects on money laundering and transnational crime can also be measured.

Money laundering can be defined, generally, as the process of concealing the existence, illegal source, or application of income derived from a criminal activity, and the subsequent disguising of the source of that income to make it appear legitimate. Deception is the heart of money laundering. The use of international trade to move money, undetected, from one country to another is one of the oldest techniques used to circumvent government scrutiny. International trade as a means of laundering money is also a technique generally ignored by most government law enforcement agencies. This article details how false international trade invoicing is used to move money across borders, undetected. This research details how the statistical analysis of the U.S. trade database can assist in measuring illegal money flows. It also details some statistical techniques that may be used to detect and monitor these abnormal transactions.

This paper explores the ability of a class of two-sector dynamic general equilibrium models to generate equilibrium time series for Money Laundering (ML), through numerical simulations in accordance with the works of Ingram, Kocherlakota and Savin (1997), Busato, Chiarini and Di Maro (2006), and Argentiero, Bagella and Busato (2008). The paper adopts this approach for the US and the EU-15 economies. The simulations show that ML accounts for 19 percent of GDP in the EU-15 economy, while it accounts for 13 percent in the US economy over the sample 2000:01-2007:04. Moreover, the ML simulated for the EU-15 is less volatile (relative standard deviation to GDP is 0.288 compared to a figure of almost 0.4 for the US economy), and negatively correlated with respect to GDP. The latter statistic is positive for the US economy.

Anti-money laundering policy has become a major issue in the Western world, especially in the United States after 9-11. Basically, all countries in the world are more or less forced to cooperate in the global fight against money laundering. In this paper, the criminalization of money laundering is modelled, assuming rational behavior of criminals, following the law and economics strand of the literature described as the economics of crime. The theoretical model shows that a) the probability of being caught for money laundering, b) the sentence for money laundering, c) the probability of being convicted for the predicate crime, and d) the transaction costs of money laundering are negatively related to the amount of crime. Under the assumption that these factors are all positively influenced by a stricter anti-money laundering policy, the hypothesis empirically tested in this paper is that anti-money laundering policy deters potential criminals from illegal behavior and therefore lowers the crime rate. Since the data on anti-money laundering policy used in the literature thus far is not all-embracing, a unique indicator is constructed using information from the mutual evaluation reports on money laundering of the FATF, IMF and World Bank. This unique dataset is used in an empirical estimation based on a Mundlak specification to test the effect of anti-money laundering policy on the crime rate. Among the four policy areas measured – the role of law, the institutional framework, the duties of the private sector in law enforcement, and international cooperation – the latter turned out to be the policy area that is associated with a lower crime rate. This result should be an extra incentive for countries and international organizations to continue their efforts to promote and develop international cooperation in the fight against money laundering.

In 2005 the European Commission adopted the Third Directive on Anti-Money Laundering (AML), which was to be implemented into national laws at the latest by December 2007. The key feature that characterizes the Third Directive is the idea that the regulatory framework should be risk-based (RBA). The aim of this regulation is to elicit a high level of outcome in terms of AML effectiveness from self-interested financial institutions (FIs) who hold private information. In this paper we study how to increase the effectiveness of AML rules, using a principal-agent framework to describe the regulatory setting in which an RBA is applied. We focus on incentive problems arising in a three-layer hierarchy, which includes public authorities (policymakers), financial institutions, and supervisors.

In the past decade, anti-money laundering policy has switched both in the US and in Europe from a rule- to a risk-based reporting system in order to avoid over-reporting by the private sector. However, reporting instead increased in most countries, while the quality of information decreased. Governments were drowned in data because private agents feared sanctions for not reporting. However, unlike in other countries, this “crying wolf problem” (Takats, 2007) did not happen in the Netherlands, where the number of reports diminished, but information quality improved. The reasons for this can be found in differences in legal institutions and legal culture, notably the contrast between US adversarial legalism and Dutch cooperative informalism. The established legal systems also provide for resistance to change. Thus, lowering sanctions in order to reduce over-reporting may not be a realistic option in a legal system which traditionally uses deterrence by fierce criminal and private legal sanctions. Furthermore, a risk-based approach may not be sustainable in the long run, as litigation may eventually replace a risk-based approach again by a rule-based one, now with precise rules set by the courts.