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This paper aims to offer the first known synthesis of peer-reviewed literature on trade-based money laundering (TBML). Given the topic is in its nascent stage yet gaining prominence across scholarship and practice, this foundation is pertinent for future TBML research.

A systematic literature review was undertaken with a formulaic search string. Both qualitative (thematic) and quantitative (meta) analysis methods were used to illustrate the findings.

The systematic literature review, using qualitative and quantitative synthesis, led to a thematic categorization of extant TBML literature into four categories: TBML risk assessment, TBML detection, the role of professionals and understanding of TBML. Due to the limited number of studies, insights that can be drawn from the extant literature on the best way to combat TBML are also limited.

As the first systematic literature review on TBML, this study identified that the existing TBML literature has focused on increasing the understanding of the phenomenon in terms of its definition and mechanisms, detection, linkage with other crimes, such as organized crime and terrorism financing, and risk assessment frameworks. The originality of these findings lies in identifying areas future researchers might explore to broaden the academic literature.

The purpose of this paper is to interrogate if the legal status of a cannabis affects money laundering activity. The legal status of cannabis continues to evolve globally; at the same time, its market remains enormous. Much of this market represents dirty money from criminal acts, which often requires laundering. In the context of changing cannabis regulations, legislation, and policies, the authors propose the possible implications such changes may have on the extent of money laundering.

This paper proposes the implications of the evolution of cannabis regulations on money laundering activities, using the theoretical underpinning of rational choice. Using Australia as a replicable critical case study, the paper, using the Walker gravity model and using United Nations Office on Drugs and Crime-reported prices of cannabis from 2003 to 2017 and Australian Criminal Intelligence Commission reports empirically validates the effects of cannabis regulations on the proceeds available for laundering.

This study finds support for the argument that prohibitive measures toward cannabis use contribute to increases in the need to launder generated proceeds.

The findings can be replicated in other countries and may contribute to novel propositions within the debate on the legalization of cannabis use, which has, thus, far primarily focused on the areas of health, crime, taxation and education.

To the best of the authors’ knowledge, no study has yet attempted to provide an economic analysis of the effects of cannabis policy changes on money laundering.

This paper advocates the use of graph database platforms to investigate networks of illicit companies identified in money laundering schemes. It explains the setup of the data structure to investigate a network of illicit companies identified in cases of money laundering schemes and presents its key application in practice. Grounded in the technology acceptance model (TAM), this paper aims to present key operationalisations and theoretical considerations for effectively driving and facilitating its wider adoption among a range of stakeholders focused on anti-money laundering solutions.

This paper explores the benefits of adopting graph databases and critiques their limitations by drawing on primary data collection processes that have been undertaken to derive a network topology. Such representation on a graph database platform provides the opportunity to uncover hidden relationships critical for combatting illicit activities such as money laundering.

The move to adopt a graph database for storing information related to corporate entities will aid investigators, journalists and other stakeholders in the identification of hidden links among entities to deter activities of corruption and money laundering.

This paper does not display the nodal data as it is framed as a background to how graph databases can be used in practice.

To the best of the authors’ knowledge, no studies in the past have considered companies from multiple cases in the same graph network and attempted to investigate the links between them. The advocation for such an approach has significant implications for future studies.