Virtual money laundering: policy implications of the proliferation in the illicit use of cryptocurrency

Abstract

Purpose This study aims to explain how cryptocurrency is leveraged for illicit purposes across the global financial system. Specifically, it establishes how cryptocurrency has been changing the nature of transnational and domestic money laundering (ML). It then assesses the effectiveness of conventional anti-money laundering (AML) policy and legislation against the proliferation of crypto laundering, using Canada as a critical case study. Design/methodology/approach Data was collected from court cases and secondary sources to build cross-case trends of cryptocurrency use in ML. Illicit International Political Economy forms the theoretical foundation for this study, whose contribution is situated in the current literature on crypto-ML. Findings This study finds that Bitcoin is common among crypto-money launderers, though most also use some form of alt-coin, and that the use of third-party currency exchanges is a prevalent method to create illicit funds and conceal proceeds of crime. The findings validate two hypotheses that illicit use of crypto is prevalent in the first two stages of ML, and that crypto is most often used in conjunction with other fiat currencies. Although law enforcement is improving on monitoring and understanding popular cryptocurrencies such as Bitcoin, alt-coins pose a significant challenge for criminal intelligence. New regulations for third-party currency exchanges are having a positive impact on curtailing crypto-laundering but are shown to be insufficient per se to contain the use of crypto in criminal activity. Originality/value This study contributes to a more robust understanding of the use of virtual currency in transnational and domestic ML. It contributes to an emerging body of literature on the role of technological change in enabling the global flow of illicit funds. It also informs public policy on virtual currency in general, and on AML regulation in Canada in particular.

Illuminating the dark corners: a qualitative examination of cryptocurrency’s risk

“…Cryptocurrencies have become a commonly used form of payment on dark market platforms, with their importance often being likened to that of PayPal in the development of eBay (Foley et al , 2019). Many studies such as Leuprecht et al (2022), and Foley et al (2019) have shown that using cryptocurrencies to finance illegal activities, particularly the purchase of illicit products is a popular way of using them. Some real-world examples of cryptocurrency fraud and scams such as from October 2020 to March 2021, the USA Federal Trade Commission reported that customers lost more than $80m in cryptocurrency-related frauds, with a median loss of $1,900 per individual.…”

“…Despite the development of a substantial body of research focusing on specific aspects of cryptocurrency’s dark side (Wronka, 2022; Leuprecht et al , 2022; Mackenzie, 2022), there is a limited understanding of how new technologies, such as blockchain-based cryptocurrency, are used for illicit purposes and how the broader spectrum of its dark sides is illuminated. Concerns have also been expressed concerning the under-theorisation of this study stream, with a concentration on manifestations of the dark side rather than the broad term linked with cryptocurrency’s negative sides (Gurgun et al , 2022; Fonseca et al , 2020).…”

“…Until now, a limited amount of qualitative research has been carried out on cryptocurrency, and even when such studies have been conducted, little or no attention has been given to their negative aspect. Previous research conducted by scholars such as Wronka (2022), Leuprecht et al (2022) and Mackenzie (2022) has only investigated singular aspects, mainly the phenomenon of money laundering via cryptocurrency. Fonseca et al (2020) examined only psychological barriers to cryptocurrency adoption, while Gurgun et al (2022) explored the barriers to using cryptocurrency in managing the construction supply process using the empirical approach.…”

RFSA: A Ransomware Feature Selection Algorithm for Multivariate Analysis of Malware Behavior in Cryptocurrency

“…This section will address the current research landscape about ransomware detection [1], [4], [5]. It delves into the discussion of machine learning (ML) techniques that have exhibited promise across diverse cybersecurity applications [6].…”

“…Cryptocurrency is a type of digital currency that uses cryptographic methods for secure transactions. This technology has experienced an exponential surge in popularity and widespread adoption in recent years [1]. Prominent among cryptocurrencies is Bitcoin (BTC) [2], which operates on a decentralized ledger called the blockchain.…”