AbstractThe rise in financial crimes has resulted in a wide range of regulatory reforms to combat the problem. The failure of regulations to accomplish the desired objectives has brought regulators under pressure to justify their actions concerning political, executive and judicial scrutiny. One example of non-compliant behaviour subjected to extensive debate among practitioners, academics and regulators has been money laundering, an act of giving dirty money a legitimate appearance. In 2020, the amount of money being laundered after five rounds of international flows for the year of 2014 was estimated to be three percent of global Gross Domestic Product (GDP) or USD 2.3 trillion. The seriousness of the problem has increased over time, as indicated by the uncovering of the "Troika Laundromat" by the Organised Crime and Corruption Reporting Project (OCCRP), which involved moving billions of dollars of illicit Russian funds through the use of secretive offshore companies. On similar lines, shell companies incorporated in the U.K. alone were identified to be involved in laundering 80 billion pounds of stolen money between 2010 and 2014.
As a result, an emerging interest from both researchers and practitioners has surrounded money laundering. In the current economic environment, regulators are struggling to stay ahead of fraudulent schemes, and financial institutions are being challenged to ensure that they identify and stop criminal activities while serving legitimate customers effectively and efficiently. Consequently, it is necessary to improve understanding and come up with mechanisms to detect money laundering. This thesis contributes to this by: exploring the literature in the field; synthesising existing knowledge to develop a framework for explaining techniques adopted to launder funds; developing a model for detecting shell companies being used to launder illicit proceeds of crime using publicly available information; assessing an opportunity created by technological innovation to commit fraud and money laundering; assessing the implication of cannabis regulations on money laundering; and, finally, developing a money-laundering attractiveness index.
This dissertation first conducts a thematically systematised literature review to identify the extent of research conducted on money laundering. The complexity of techniques adopted to launder funds may vary depending upon the situation, with new typologies being created in response to changes in technology and regulations. However, no attempt is made in the literature to explain a launderer's choice of techniques. This research develops the new APPT framework of money laundering to explain the interrelated factors influencing the choice of laundering techniques used to accomplish the objective. The new APPT framework is named according to four factors that each play a role in explaining the choice of techniques: the Actors involved, Predicate crime, the Purpose for laundering, and Technological innovations. The framework would assist in acknowledging the continuously evolving regulatory landscape and would direct attention towards the need for better mechanisms in combatting money-laundering activities.
Amongst the various techniques to launder funds, the review of existing literature identified the use of shell companies to be an under-researched technique to launder funds. Consequently, to address this need, the focus shifts towards developing a model for detecting shell companies being used to launder illicit proceeds of crime. The opportunity to detect illicit shell companies rested in using the networks prevalent among entities in a corrupt network and analysing the links and similarities. The analysis would facilitate scores of links and similarities that could be useful in distinguishing corrupt entities from non-fraudulent ones. Facilitating models to detect illicit shell companies using publicly available information quantitatively is under-researched.
The use of data science techniques in coming up with new detection mechanisms is worth appreciating. However, as depicted in the APPT framework, the importance of technological innovation in undertaking illicit acts of fraud and money laundering must not be undermined. The thesis takes a step in this direction by exploring an opportunity, initial coin offerings (ICOs), created by technological innovation to give rise to a predicate crime that may subsequently lead to money laundering.
To further understand the need for the APPT framework, detection mechanisms and being aware of new technological innovations capable of paving the way for illicit acts such as money laundering, it becomes critical to be aware of the magnitude of the problem. The review of existing literature identified a stream of research focusing on estimating the magnitude of money laundering. Among a range of illicit activities, the money from drug trafficking is the most prominent for immediate money laundering, and research work on money laundering would be incomplete without incorporating the contribution of drug trafficking to the amount of funds laundered. Considering the case of Australia, this thesis extends the literature by estimating the magnitude of money laundering by quantifying the amount of funds being laundered through cannabis trafficking.
Finally, acknowledging the debate in literature on lack of consensus on generated estimates of the magnitude of money laundering and the need to find countries attractive to launder funds, this thesis proceeds to construct a reliable and robust index for measuring a country's appeal as a destination for money laundering. It uses the Principal Component Analysis (PCA) to come up with a Money-Laundering Appeal Index (MLAI), thus avoiding the difficulty of precisely calculating illicit financial flows.
The key stakeholders to benefit from such research would be legal and compliance professionals and government officials, especially tax officials and anti-corruption NGOs. Among experienced practitioners, the knowledge of the APPT framework would aid in exercising professional judgement to come up with appropriate detection and deterrence mechanisms. In educational institutions, such a framework would suggest a move towards the incorporation of pedagogical techniques aimed at improving the content value and encouraging the development of skills valued by academics and practitioners.
The models developed to detect illicit shell entities could be of use to investigators, regulatory agencies and banks with access to transaction information. They could use the models alongside suspicious transaction analysis to increase the accuracy of entities they term as suspicious. The models developed as part of this research would assess the risk of a company being involved in illicit activities and whether there is a need to investigate further.
The work around Initial Coin Offerings (ICOs) would raise awareness around a possible opportunity to commit fraud as well as launder funds. Similarly, the work surrounding money laundered through cannabis trafficking could play a vital role in the debate around the legalisation of cannabis by providing an additional component for depicting harms from illegal markets. It may have implications for economies attempting to tackle the problem of money laundering by providing an avenue to consider the cannabis angle. Finally, the complex phenomenon of money laundering appeal can be placed into a single composite indicator and this might not only inform national strategies to prevent money laundering but provide an opportunity to use a similar approach to develop more localized hotspot maps that could move analysis at the sub-national level.
|Date of Award||9 Feb 2021|
|Supervisor||Kuldeep Kumar (Supervisor) & Adrian Gepp (Supervisor)|