Losing the War Against Dirty Money: Rethinking Global Standards on Preventing Money Laundering and Terrorism Financing

Following a brief overview in Part I.A of the overall system to prevent money laundering, Part I.B describes the role of the private sector, which is to identify customers, create a profile of their legitimate activities, keep detailed records of clients and their transactions, monitor their transactions to see if they conform to their profile, examine further any unusual transactions, and report to the government any suspicious transactions. Part I.C continues the description of the preventive measures system by describing the government\u27s role, which is to assist the private sector in identifying suspicious transactions, ensure compliance with the preventive measures requirements, and analyze suspicious transaction reports to determine those that should be investigated. Parts I.D and I.E examine the effectiveness of this system. Part I.D discusses successes and failures in the private sector\u27s role. Borrowing from theory concerning the effectiveness of private sector unfunded mandates, this Part reviews why many aspects of the system are failing, focusing on the subjectivity of the mandate, the disincentives to comply, and the lack of comprehensive data on client identification and transactions. It notes that the system includes an inherent contradiction: the public sector is tasked with informing the private sector how best to detect launderers and terrorists, but to do so could act as a road map on how to avoid detection should such information fall into the wrong hands. Part I.D discusses how financial institutions do not and cannot use scientifically tested statistical means to determine if a particular client or set of transactions is more likely than others to indicate criminal activity. Part I.D then turns to a discussion of a few issues regarding the impact the system has but that are not related to effectiveness, followed by a summary and analysis of how flaws might be addressed. Part I.E continues by discussing the successes and failures in the public sector\u27s role. It reviews why the system is failing, focusing on the lack of assistance to the private sector in and the lack of necessary data on client identification and transactions. It also discusses how financial intelligence units, like financial institutions, do not and cannot use scientifically tested statistical means to determine probabilities of criminal activity. Part I concludes with a summary and analysis tying both private and public roles together. Part II then turns to a review of certain current techniques for selecting income tax returns for audit. After an overview of the system, Part II first discusses the limited role of the private sector in providing tax administrators with information, comparing this to the far greater role the private sector plays in implementing preventive measures. Next, this Part turns to consider how tax administrators, particularly the U.S. Internal Revenue Service, select taxpayers for audit, comparing this to the role of both the private and public sectors in implementing preventive measures. It focuses on how some tax administrations use scientifically tested statistical means to determine probabilities of tax evasion. Part II then suggests how flaws in both private and public roles of implementing money laundering and terrorism financing preventive measures might be theoretically addressed by borrowing from the experience of tax administration. Part II concludes with a short summary and analysis that relates these conclusions to the preventive measures system. Referring to the analyses in Parts I and II, Part III suggests changes to the current preventive measures standard. It suggests that financial intelligence units should be uniquely tasked with analyzing and selecting clients and transactions for further investigation for money laundering and terrorism financing. The private sector\u27s role should be restricted to identifying customers, creating an initial profile of their legitimate activities, and reporting such information and all client transactions to financial intelligence units

The other War on Terror revealed: global governmentality and the Financial Action Task Force's campaign against terrorist financing

Abstract. Despite initial fanfare surrounding its launch in the White House Rose Garden, the War on Terrorist Finances (WOTF) has thus far languished as a sideshow, in the shadows of military campaigns against terrorism in Afghanistan and Iraq. This neglect is unfortunate, for the WOTF reflects the other multilateral cooperative dimension of the US-led ‘war on terror’, quite contrary to conventional sweeping accusations of American unilateralism. Yet the existing academic literature has been confined mostly to niche specialist journals dedicated to technical, legalistic and financial regulatory aspects of the WOTF. Using the Financial Action Task Force (FATF) as a case study, this article seeks to steer discussions on the WOTF onto a broader theoretical IR perspective. Building upon emerging academic works that extend Foucauldian ideas of governmentality to the global level, we examine the interwoven overlapping national, regional and global regulatory practices emerging against terrorist financing, and the implications for notions of government, regulation and sovereignty

Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsEvery year, criminals launder billions of dollars acquired from serious felonies (e.g. terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. This research addresses money laundering detection assuming minimal access to labels. The results show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. The proposed active learning solution, however, is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts

Does Money Laundering on Ethereum Have Traditional Traits?

As the largest blockchain platform that supports smart contracts, Ethereum has developed with an incredible speed. Yet due to the anonymity of blockchain, the popularity of Ethereum has fostered the emergence of various illegal activities and money laundering by converting ill-gotten funds to cash. In the traditional money laundering scenario, researchers have uncovered the prevalent traits of money laundering. However, since money laundering on Ethereum is an emerging means, little is known about money laundering on Ethereum. To fill the gap, in this paper, we conduct an in-depth study on Ethereum money laundering networks through the lens of a representative security event on \textit{Upbit Exchange} to explore whether money laundering on Ethereum has traditional traits. Specifically, we construct a money laundering network on Ethereum by crawling the transaction records of \textit{Upbit Hack}. Then, we present five questions based on the traditional traits of money laundering networks. By leveraging network analysis, we characterize the money laundering network on Ethereum and answer these questions. In the end, we summarize the findings of money laundering networks on Ethereum, which lay the groundwork for money laundering detection on Ethereum

Detecting money laundering using hidden Markov model

Recent money laundering scandals, like the Danske Bank and Swedbank’s failure to mitigate money laundering risks (Kim, 2019), have made “anti money laundering” (AML) a much discussed topic. Governments are making AML regulations tougher and financial institutions are struggling to comply, one of the requirements is to actively monitor financial transactions to detect suspicious ones. Most of the financial industry applies simple rule-based methods for monitoring. This thesis provides a practical model to detect suspicious transactions using the hidden Markov model (HMM). The use of HMM is justified, because the criminal nature of a transaction is hidden to the financial institution, only transaction parameters can be observed. By using past data, a model is built to detect if current transaction is suspicious or not. The model is assessed with artificial and real transactions data. It was concluded that this model performs better than a classical k-means clustering algorithm