A review of money laundering literature: the state of research in key areas

Abstract

Purpose:The purpose of this study is to review the literature on money laundering and its related areas. The main objective is to identify any gaps in the literature and direct attention towards addressing them.Design/Methodology/Approach: A systematic review of the money laundering literature was conducted with an emphasis on the Pro-Quest, Scopus and Science-Direct databases. Broad research themes were identified after investigating the literature. The theme about the detection of money laundering was then further investigated. The major approaches of such detection are identified as well as research gaps that could be addressed in future studies. Findings:The literature on money laundering can be classified into the following six broad areas: (i) anti-money laundering framework and its effectiveness, (ii) the effect of money laundering on other fields and the economy, (iii) the role of actors and their relative importance, (iv) the magnitude of money laundering, (v) new opportunities available for money laundering and (vi) detection of money laundering. Most studies about the detection of money laundering have focused on the use of innovative technologies, banking transactions, or real estate and trade-based money laundering. However, the literature on the detection of shell companies being explicitly used to launder funds is relatively scarce.Originality/Value: This paper provides insights into an area related to money laundering where research is relatively scant. Shell companies incorporated in the UK alone were identified to be associated with laundering 80 billion pounds of stolen money between 2010 and 2014. The use of these entities to launder billions of dollars as witnessed through the laundromat schemes and several data leaks clearly indicate the need to focus on illicit financial flows through such entities.

Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

“…There are several comprehensive and systematic review papers published over the decade that describes data mining and machine learning methods applied in AML domain. This paper will not describe those methods again here, instead list the most recent review papers in TABLE III: • Review of machine learning methods used for money laundering detection A literature review of money laundering and its related area was conducted by [3], with the aim of identifying the gaps and directing attention towards addressing them. The key findings are categorized into six groups as -(i) AML framework and effectiveness, (ii) impact of money laundering on economy, (iii) money laundering ecosystem and motivation, (iv) magnitude of money laundered, (v) avenues for money laundering, and (vi) detection methods of money laundering.…”

“…The key findings are categorized into six groups as -(i) AML framework and effectiveness, (ii) impact of money laundering on economy, (iii) money laundering ecosystem and motivation, (iv) magnitude of money laundered, (v) avenues for money laundering, and (vi) detection methods of money laundering. As per the findings by [3], most studies of detecting money laundering have focused on transactions in banks, real-estate and trade based companies; however, the literature on detection of shell companies used for money laundering is meagre. Shell companies established in UK alone, were found to be linked with laundering £80bn between 2010 and 2014.…”

“…Money laundering and terrorism financing has been one of the major threat to the integrity of international financial system since last five decades [1]. The United Nations Office on Drugs and Crime (UNODC) estimates the amount of money laundered every year is approximately 2 -5% of global GDP which amounts to $800bn -$2 trillion [2] and it is one of the biggest threat for the global economy and its security [3]. To fight against finance crime, many countries have laid stringent Anti-Money Laundering / Counter-Terrorism Financing (AML/CTF) policies for organizations who deal in cash, bullion, cryptocurrencies and financial transactions, for example AML/CTF act 2006 in Australia [4], Bank Secrecy Act 1970 in USA [5].…”

“…The solutions to detect the money laundering pattern have been evolving from statistical methods , data mining [19,20] and ML [13,21] to DL [22,23]. There are several review papers in literature that demonstrates the application of these methods for detecting suspicious transactions [3,13,20,21,[24][25][26][27][28][29], however lacks the focused review on deep learning techniques or XAI techniques in the same domain.…”

To launder or not to launder: are there positive effects for the economies of countries who launder money?

“…According to the Internal Revenue Service (IRS), money laundering is tax evasion in progress if the underlying conduct violates income tax laws and the Bank Secrecy Act (Verni, 2016). Tiwari et al (2020) conduct a systematic literature review on money laundering and its related areas to show that existing research is focused on anti-money laundering framework and its effectiveness, the effect of money laundering on other fields and the economy, the role of actors and their relative importance, the magnitude of money laundering, opportunities for money laundering, and detection of money laundering. Moreover, the United Kingdom (Cowdock, 2017), the United States (Van Duyne, 2003;Levi and Reuter, 2009), Spain (Soriano, 2016), Malaysia (Aurasu and Rahman, 2018), Italy (Ravenda et al, 2018) and Hong Kong (Ho, 2017) have been investigated by researchers, whereas Australia and Canada have received less attention.…”