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The purpose of this study is to examine the South African banks’ customer due diligence (CDD) practices in the fintech era to mitigate money laundering (ML) risks and ensure financial stability. Financial technologies have brought substantial transformations to the financial services sector. However, such technologies have exposed the sector to emerging risks that threaten the integrity and stability of the financial system globally. Before any bank–customer relationship is established, proper customer background checks must be conducted. These background checks enable financial institutions to validate information provided and ensure customers are properly risk profiled. Failure to risk profile customers could result in financial institutions being used as conduits for ML. Undoubtedly, CDD procedures are pivotal to overall anti-money laundering efforts and curbing financing terrorism in a regulatory framework.

A qualitative research approach was adopted to address the research questions of the study. Given the confidentiality associated with the financial services sector, data triangulation was used in blending mainly secondary and primary data sources. Secondary data sources used in the study were published reports available in the public domain that were corroborated with subject matter experts’ interviews.

Based on the findings of this study, it is concluded that in South Africa, technological solutions have been incorporated into CDD functions, which is now risk-based (enhanced due diligence). Also, legally, South Africa has incorporated the biometrics, integration with Department of Home Affairs and Companies and Intellectual Property Commission databases, customer consent to third-party sources with the Financial Intelligence Centre Act and the Protection of Personal Information Act.

The shift towards digital banking in South Africa results in increased data and dynamic risk profiling. This study advocates a policy shift requiring a risk-based approach to mitigating emerging ML risks (in particular digital laundering), especially in the wake of South Africa’s recent greylisting by the Financial Action Task Force.

The purpose of this paper is to develop, describe and validate a machine learning model for prioritising which financial transactions should be manually investigated for potential money laundering. The model is applied to a large data set from Norway’s largest bank, DNB.

A supervised machine learning model is trained by using three types of historic data: “normal” legal transactions; those flagged as suspicious by the bank’s internal alert system; and potential money laundering cases reported to the authorities. The model is trained to predict the probability that a new transaction should be reported, using information such as background information about the sender/receiver, their earlier behaviour and their transaction history.

The paper demonstrates that the common approach of not using non-reported alerts (i.e. transactions that are investigated but not reported) in the training of the model can lead to sub-optimal results. The same applies to the use of normal (un-investigated) transactions. Our developed method outperforms the bank’s current approach in terms of a fair measure of performance.

This research study is one of very few published anti-money laundering (AML) models for suspicious transactions that have been applied to a realistically sized data set. The paper also presents a new performance measure specifically tailored to compare the proposed method to the bank’s existing AML system.

Money laundering continues to emerge as a transnational phenomenon that has harmful consequences for the global economy and society. Despite the theoretical and practical magnitude of money laundering, international business (IB) research on the topic is scarce and scattered across multiple disciplines. Accordingly, this study aims to advance an integrated understanding of money laundering from the IB perspective.

The authors conduct a systematic review of relevant literature and qualitatively analyze the content of 57 studies published on the topic during the past two decades.

The authors identify five streams (5Cs) of research on money laundering in the IB context: the concept, characteristics, causes, consequences and controls. The analysis further indicates six theoretical approaches used in the past research. Notably, normative standards and business and economics theories are dominant in the extant research.

The authors review the literature on an under-researched but practically significant phenomenon and found potential for advancing its theoretical foundations. Hence, the authors propose a 5Cs framework and a future agenda for research and practice by introducing 21 future research questions and two plausible theories to help study the phenomenon more effectively in the future.

In practical terms, the study extends the understanding of the money laundering phenomenon and subsequently helps mitigating the problem of money laundering in the IB environment, along with its harmful economic and societal impacts.

The authors offer an integrative view on money laundering in the IB context. Additionally, the authors emphasize wider discussions on money laundering as a form of mega-corruption.

This paper aims to critically examine the digital transformation of anti-money laundering (AML) and countering the financing of terrorism (CFT) in light of the Financial Action Task Force (FATF) San Jose principles, the Organisation for Economic Co-operation and Development (OECD) principles for artificial intelligence (AI) and the proposed European Union (EU) Artificial Intelligence Act. The authors argue that AI tools can revolutionize AML/CFT and asset recovery, but there is a need to strike a balance between optimizing AML efficiency and safeguarding fundamental rights.

This paper draws on reports, legislation, legal scholarships and other open-source data on the digital transformation of AML/CFT, particularly the deployment of AI in this context.

A new regulatory framework with robust safeguards is necessary to mitigate the risks associated with the use of new technologies in the AML context.

This study is one of the first to examine the use of AI in the AML/CFT context in light of the FATF San Jose principles, the OECD AI principles and the proposed EU AI Act.

This paper aims at comparing and contrasting the Ao Man-long scandal with the Ho Chio-meng case in Macau, drawing lessons from the two events and casting lights on the literature on corruption scandals.

The study used documentary research and interpretative and analytical approaches.

The two cases show considerable administrative discretion on the part of the principal officials involved, and remedial measures along the line of having more rigorous and frequent internal auditing may be necessary.

Original analyses were conducted together with literature review and documentary research. This paper would be of interest to scholars and practitioners concerned with how Macau combats corruption.

The aim of the present study is to shed light on the role of legal practitioners, namely, lawyers and notaries, in the fight against money laundering: Are they considered as facilitators or obstacles against money laundering? How does the global and the EU legal framework deal with the legal professionals?

The research follows a deductive approach attempting to respond to questions such as: How do the lawyers’ and notaries’ societies react in front of the anti-money laundering measures that concern them and why? What are the discrepancies between the lawyers’ professional secrecy and the obligations that EU anti-money laundering legislation assigns them?

This study disclosures the response of the European union and international legal and regulatory framework as well as the reflexes of the international and European legal professionals’ associations to this danger. It also demonstrates the reaction of lawyers against European union anti-money laundering legislation, to the point that it limits not only the confidentiality principle but also the position of the European judicial systems to the contradiction between this principle and the lawyers’ obligation to report their suspicions to the authorities.

To fulfil the study goals, it was necessary to overcome some obstacles, like the limitation of existing sources. Indeed, transnational empirical research considering the professionals who facilitate money laundering is narrow. Besides, policymakers and academics only recently expressed more interest in money laundering and its facilitators.

This paper fulfils an identified need to study the legal professionals’ role not only in money laundering practices but also in anti-money laundering policies.