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Good quality input data is critical to developing a robust machine learning model for identifying possible money laundering transactions. McKinsey, during one of the conferences of ACAMS, attributed data quality as one of the reasons for struggling artificial intelligence use cases in compliance to data. There were often use concerns raised on data quality of predictors such as wrong transaction codes, industry classification, etc. However, there has not been much discussion on the most critical variable of machine learning, the definition of an event, i.e. the date on which the suspicious activity reports (SAR) is filed.

The team analyzed the transaction behavior of four major banks spread across Asia and Europe. Based on the findings, the team created a synthetic database comprising 2,000 SAR customers mimicking the time of investigation and case closure. In this paper, the authors focused on one very specific area of data quality, the definition of an event, i.e. the SAR/suspicious transaction report.

The analysis of few of the banks in Asia and Europe suggests that this itself can improve the effectiveness of model and reduce the prediction span, i.e. the time lag between money laundering transaction done and prediction of money laundering as an alert for investigation

The analysis was done with existing experience of all situations where the time duration between alert and case closure is high (anywhere between 15 days till 10 months). Team could not quantify the impact of this finding due to lack of such actual case observed so far.

The key finding from paper suggests that the money launderers typically either increase their level of activity or reduce their activity in the recent quarter. This is not true in terms of real behavior. They typically show a spike in activity through various means during money laundering. This in turn impacts the quality of insights that the model should be trained on. The authors believe that once the financial institutions start speeding up investigations on high risk cases, the scatter plot of SAR behavior will change significantly and will lead to better capture of money laundering behavior and a faster and more precise “catch” rate.

Illegal wildlife trade (IWT) is a transnational organized crime that generates billions in criminal proceeds each year. Yet, it is not regarded by many countries as a serious crime. There is also no general consensus on its recognition as a predicate offence for money laundering. In this regard, banks are misused in different ways to facilitate financial flows linked to IWT. This paper aims to illustrate the importance of the banking sector in combating money laundering relating to IWT. It also aims to demonstrate the need for a general recognition of IWT as a predicate offence for money laundering.

This study investigates the implementation of money laundering controls by banks in the illegal-wildlife-trade context. As background to this investigation, it provides an overview of IWT, which is followed by an exploration of some of the general characteristics of the banking sector, before discussing the relevant Financial Action Task Force (FATF) recommendations.

This study finds that the banking sector is well-placed to combat money laundering relating to the IWT and is, by virtue of its international nature and strong focus on compliance, able to be effective in preventing the use of the proceeds of IWT as well as in identifying broader trafficking networks. Moreover, the banking sector is well-equipped to develop appropriate platforms to facilitate the swift, easy and effective sharing of financial intelligence between banks at the local, regional and especially international level.

This study draws on publicly available information on financial flows relating to IWT. Little data and research are available on the financial flows and consequently the money laundering techniques used in cases suspected of IWT.

There has been little scholarly research on the relationship between money laundering and the IWT as well as the financial flows of IWT in general. This study highlights some of the money laundering techniques used in relation to IWT by drawing on the works of various international organizations, including the FATF.

Despite 2016 legalization of recreational cannabis cultivation and sale in California with the passage of Proposition 64, many cannabis businesses operate without licenses. Furthermore, federal regulations disincentivize financial institutions from banking and lending to licensed cannabis businesses. The authors explore the impact of legal cannabis business activity on California financial institutions, the barriers to banking faced by cannabis businesses, and the nontraditional sources of financing used by the industry.

The authors use a mixed methods approach. The authors utilize call data for banks and credit unions headquartered in California and state cannabis licensing data to estimate the impact of the extensive and intensive margins of licensed cannabis activity on key banking indicators using difference-and-difference and fixed effects regressions. The qualitative data come from interviews with industry stakeholders in northern California's “Emerald Triangle” and add important context.

The quantitative results show economically and statistically significant impacts of licensed cannabis activity on banking indicators, suggesting both direct and spillover effects from cannabis activity to the financial sector. However, cannabis businesses report substantial barriers to accessing basic financial services and credit, leading to nontraditional financing arrangements.

The results suggest opportunities for cannabis businesses and financial institutions if regulations are eased and important avenues for further study.

The authors contribute to the nascent literature on cannabis economics and the literature on banking regulation and nontraditional finance.

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.

The reality of domestic violence does not disappear when people enter the digital world, as abusers may use technology to stalk, exploit, and control their victims. In this chapter, we discuss three unique types of technological abuse: (1) financial abuse via banking websites and apps; (2) abuse via smart home devices (i.e., “Internet of Things” abuse); and (3) stalking via geo-location or GPS. We also argue pregnancy and wellness apps provide an opportunity for meaningful intervention for pregnant victims of domestic violence.

While there is no way to ensure users' safety in all situations, we argue thoughtful considerations while designing and building digital products can result in meaningful contributions to victims' safety. This chapter concludes with PenzeyMoog's (2020) “Framework for Inclusive Safety,” which is a roadmap for building technology that increases the safety of domestic violence survivors. This framework includes three key points: (1) the importance of educating technologists about domestic violence; (2) the importance of identifying possible abuse situations and designing against them; and (3) identifying user interactions that might signal abuse and offering safe interventions.

Customer due diligence measures that are employed in the United Kingdom (UK) to detect and combat money laundering are discussed. The UK adopted a progressive regulatory and enforcement framework to combat money laundering which relies, inter alia, on the use of customer due diligence measures to regulate and curb the occurrence of money laundering activities in its financial institutions and financial markets. However, other regulatory measures that could have contributed to the effective combating money laundering in the UK will not be explored in detail since the article is focused on the reliance and use of customer due diligence measures to curb money laundering activities. Accordingly, the strength, flaws and weaknesses of the UK anti-money laundering regulatory and enforcement framework are examined. Lastly, possible recommendations to address such flaws and weaknesses are provided.

The paper discusses customer due diligence measures that are used in the UK to detect and combat money laundering.

It is hoped that policymakers and other relevant persons will use the recommendations provided in the paper to enhance the curbing of money laundering in the UK.

The paper does not provide empirical research.

The study is useful to all policymakers, lawyers, law students and regulatory bodies in the UK.

The study seeks to curb money laundering in the UK society globally.

The study is original research on the use of customer due diligence measures to detect and combat money laundering in the UK.

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.