Money laundering using investment companies

Abstract

Purpose – This paper aims to start with the assumption that money laundering through the use of investments will continue to occur and will become increasingly more complex to try and avoid detection. The paper aims to explore some of the theoretical factors that would need to be considered in any risk based framework and also to consider how an empirical model can try and prioritise the information and intelligence gathered through existing beneficial ownership and customer due diligence (CDD) systems. Design/methodology/approach – This paper uses an empirical example of money laundering with investments and highlights the red flag indicators that led to its eventual discovery. The theoretical framework considers the difficulties of information overload and suggests that any empirical model of risk-based assessment would need to be able to discern between the various types of risk information gathered. The paper has developed one empirical model that could be used. Findings – The paper suggests a model that breaks down beneficial ownership and CDD information into three areas: beneficial ownership for all major players, transparency of transactions and accountability of companies involved. Practical implications – The paper has implications for the banking, regulatory and law enforcement sectors working in Anti-Money Laundering (AML). Originality/value – The paper analyses a particular type of money laundering activity which it terms “investment laundering” using an empirical case study. It then develops a new theoretical and empirical risk assessment model to illustrate how risk-based approaches need to be able to discern between the different types of information gathered and the application to overall risk.

Lifting the veil on beneficial ownership

“…The purpose of money laundering is to legitimise and hide illicit funds (Zali and Maulidi, 2018, p. 44). The money laundering process, is characterised by a three-stage model, which is easily understood by authorities and applies in many cases of money laundering: “ placing ” illicit funds into the economy, for example, crediting cash into a bank account; “ layering ”, whereby proceeds are then redirected and covered to conceal their origin, for example, placing trivial cash amounts into numerous bank accounts, or mixing illicit funds with legal funds to obscure the money trail; and “ Integrating ” the funds back into the economy by converting them into seemingly legitimate earnings, such as real estate and luxury possessions, or other investments (Cassella, 2018; Naheem, 2015). …”

“…“ Integrating ” the funds back into the economy by converting them into seemingly legitimate earnings, such as real estate and luxury possessions, or other investments (Cassella, 2018; Naheem, 2015).…”

“…However, Cassella (2018, p. 495) claims this model is insufficient in scope to address the problem that money laundering poses because it concentrates only on the money-laundering methods; does not assist authorities to recognise why money laundering occurs, or help trace the people involved in the money-laundering process. Money launderers have developed more sophisticated and varied means by which to mask the origins of their illicit money and have adapted to governments’ strengthening AML regulation (Naheem, 2015, p. 439; also see Brown, 2016; Cassella, 2018). Moreover, money laundering is an international problem and criminals often find new methods to launder money quicker than governments can progress new AML laws (Turner, 2011).…”

“…Many officials have also been critical of the EU’s risk-based approach towards customer due diligence (CDD), arguing the process is costly to the financial sector, and induces formulaic reporting via its bureaucratic requirements (Naheem, 2015, pp. 441-442).…”

The Determinant of Money Laundering: Evidence from Italian Regions

“…In conclusion, it is clear that money laundering is a strong risk in Europe, and only with a common strategy it is possible to contrast it (Gilmour 2016;Yasaka 2017 In order to develop some testable hypotheses that guide the empirical analysis, we have considered a set of determinants that include socio-economic, enforcement and crime-specific factors. First, with reference at the principal money laundering literature (Masciandaro 2007;Ahmad 2015;Braun et al 2016;Ebikake 2016), potential criminals are profit-maximizers who make their decisions on illegal action in order to maximize the expected utility. In particular, criminals commit the crime when the expected benefits of offending are greater than the expected costs of compliance.…”