RESUMEN
The Financial Action Task Force (FATF) requires national governments to demonstrate an understanding of the distribution of money laundering risks across different sectors of the financial system. Such understanding is the foundation for effective control of money laundering under the risk-based approach called for by the FATF. We analyzed the National Risk Assessments (NRAs) of eight systemically important countries before 2020 to test whether these demonstrated that basic understanding. The eight show very different conceptualizations, analytic approaches, and products. None showed more than minimal competence at risk assessment. For example, most relied largely on expert opinion, solicited, however, in ways that violated the well-developed methodology for eliciting expert opinion. They consistently misinterpreted Suspicious Activity Reports, the most fine-grained quantitative data available on money laundering, and failed to provide risk assessments relevant for policymakers. Only one described the methodology employed. Although conducting strong money laundering risk assessments is challenging, given the difficulty of estimating the extent of laundering in any sector, existing practices can be improved. We offer some potential explanations for the failure of governments to take this task seriously. The lack of involvement of risk assessment professionals is an important contributing factor to the weaknesses of the current NRAs.
RESUMEN
Bank transactions are highly confidential. As a result, there are no real public data sets that can be used to investigate and compare anti-money laundering (AML) methods in banks. This severely limits research on important AML problems such as efficiency, effectiveness, class imbalance, concept drift, and interpretability. To address the issue, we present SynthAML: a synthetic data set to benchmark statistical and machine learning methods for AML. The data set builds on real data from Spar Nord, a systemically important Danish bank, and contains 20,000 AML alerts and over 16 million transactions. Experimental results indicate that performance on SynthAML can be transferred to the real world. As use cases, we present and discuss open problems in the AML literature.
RESUMEN
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.
RESUMEN
Financial and legal entities (e.g. banks, casinos, notaries etc.) have to report money laundering suspicions. Countries' engagement in fighting money laundering is evaluated-among others-with statistics on how often these suspicions are reported. Lack of compliance can result in economically harmful blacklisting. Nevertheless, these blacklists repeatedly become empty-in what is known as the emptying blacklist paradox. We develop a principal-agent model with intermediate agents and show that non-harmonized statistics can lead to strategic reporting to avoid blacklisting, and explain the emptying blacklist paradox. We recommend the harmonization of the standards to report suspicion of money laundering.
