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Efforts to launder money and finance terrorism have been evolving rapidly in recent years in response to heightened countermeasures. The international community has witnessed the use of increasingly sophisticated methods to move illicit funds through financial systems across the globe and has acknowledged the need for improved multilateral cooperation to fight these criminal activities. This second edition is to serve as a single, comprehensive source of practical information for countries to fight money laundering and terrorist financing. It discusses the problems caused by these crimes, the specific actions countries need to take to address them and the role international organizations play in the process. The report is organized as follows: Part A of this Reference Guide describes the problem of money laundering and terrorist financing, their adverse consequences, and the benefits of an effective regime. It also identifies the relevant international standard-setting organizations and discusses their specific efforts and instruments that fight these activities. Part B describes the various elements that are part of a comprehensive legal and institutional framework for anti-money laundering and combating the financing of terrorism for any country. This part of the Reference Guide is a step-by-step approach to achieve compliance with international standards, although it does not dictate the specific methods or actions to be adopted. Rather, it raises the issues that must be addressed and discusses the options that a country has in order to resolve these issues. Part C describes the role of the World Bank and International Monetary Fund (IMF) in the global effort and the coordination of technical assistance available to countries in order to help them achieve compliance with international standards. Each chapter is a self-contained discussion of the topics covered in that chapter with detailed references to background and original source materials. Annexes I, II and III provide complete citations to reference materials.

Purpose The purpose of this paper is to examine the link between trade-based money laundering and organized crime. Trade-based money laundering (TBML) has emerged as the newest and possibly most complex method used by organized crime and white-collar crime groups for illegally laundering money in the international financial system. Using legitimate global trade streams, criminal organizations are able to transfer billions of dollars annually between jurisdictions without having to adhere to state-level currency regulations. Design/methodology/approach Using a rational approach to understanding the behavior of criminal organizations, it is argued that TBML will continue to grow as a preferred methodology for laundering money internationally. As criminal organizations continue to be displaced from the more traditional methods of money laundering, they will look for and find TBML as a viable alternative for moving money between different jurisdictions. Findings As the methodology becomes more developed, the skill set will transfer to an increasing number of organized crime groups and be incorporated as a mainstream method for laundering and moving money. Practical implications To stay current with contemporary money laundering schemes, law enforcement agencies will have to train their investigators to spot, investigate and collect requisite evidence for successful prosecution and disruption of TBML offences. Moreover, in the absence of a global regime for sharing trade and customs information, legislators and law enforcement agencies will have to consider how to best expedite the sharing of trade and customs information. Originality/value This is the only study to address TBML as an emerging money laundering technique and the transfer of the skill between organized crime groups. It further details the skills that police investigators needs to develop to successfully combat TBML.

Purpose The purpose of this paper is to illustrate how criminals launder money in the real estate business in Austria, Germany, Liechtenstein and Switzerland. Design/methodology/approach A qualitative content analysis of 58 semi-standardized expert interviews with both criminals and prevention experts and a quantitative survey of 184 compliance officers led to the identification of concrete techniques of money laundering in the real estate sector. Findings Real estate companies in German-speaking countries in Europe continue to be extraordinarily suitable for money laundering. In particular, they can be used for placement, layering and integration, combined with violations of the tax code. Most importantly, however, they are the vehicles for one of the very few profitable methods of laundering money. Research limitations/implications As the qualitative findings are based on semi-standardized interviews, these are limited to the 58 interviewees’ perspectives. Practical implications The identification of gaps in existing anti-money laundering mechanisms is meant to provide compliance officers, law enforcement agencies and legislators with valuable insights into how criminals operate. Originality/value While the existing literature focuses on organizations fighting money laundering and on the improvement of anti-money laundering measures, this paper describes how money launderers operate to avoid getting caught. Both prevention and criminal perspectives are taken into account.

Purpose – The purpose of this paper is to analyse money laundering and financing terrorism risks of present customer loyalty programs. We try to identify the current state of money laundering and financing of terrorism (ML/FT) risks and detect the vulnerabilities that may be present in loyalty schemes that tend to obtain wider payment functionality. Design/methodology/approach – The paper draws upon the risk matrix developed by the Financial Action Task Force experts for the new payment methods. Each risk factor is analysed against the features of the customer loyalty programs, and the aggregated risks are also reviewed within three stages of money laundering. Findings – The analysis shows that despite the obvious evolution of payment functionality of the customer loyalty awards, businesses have already put in place relevant risk-mitigation measures that support the hypothesis that business practices can effectively mitigate ML/FT risks even without precise regulation. Yet, the paper shows some potential vulnerabilities that are to be monitored in order to prevent the system from abuse by the criminals. Research limitations/implications – The paper shows that loyalty awards share certain characteristics of the centralized private currencies. Hence, researchers are encouraged to look more closely into the potential ML/FT risks posed by the private currencies as well. Practical implications – The paper provides an insight into money laundering and terrorist financing risks that can be relevant for the non-financial products which demonstrate some payment functionality. Originality/value – The paper is one of the first research of the loyalty awards as a quasi-payment tool in the context of the anti-money laundering and combating financing of terrorism (AML/CFT) regime.

The global illegal wildlife trade has been anecdotally linked to other serious crimes, such as fraud, corruption, and money laundering, as well as the cross-border trafficking of drugs, arms, counterfeit goods, and persons. As research on this topic is scarce and sporadic, we conducted a scoping literature review to gather information across multiple disciplines and evidence types on crime convergences in the illegal wildlife trade. We reviewed 150 papers published between 2000 and 2020. We found that the illegal trade in many of the most frequently trafficked species have reportedly converged with numerous other serious and organised crimes, most commonly drug trafficking. Convergences can occur in a variety of ways, although the diversification of organised crime groups, parallel trafficking of contraband, and use of enabling crimes (such as corruption and violence) were the most frequently described. Possible explanations for our results and future research directions are discussed.

The widespread adoption of cryptocurrencies has transformed the financial landscape by enabling swift, decentralised transactions. However, the pseudonymous nature of digital currencies has also fuelled illicit activities, such as money laundering. Criminals perform money laundering to access illicitly acquired funds without detection and convert illegally obtained assets into untraceable commodities, seamlessly integrated into the financial system. Although new regulatory measures have been introduced, illicit actors continue to exploit various methods, from peer-to-peer exchanges to cryptocurrency mixing services, to obscure the origins of illegal funds. This study presents a parametric analysis of these methods, examining dimensions such as duration, number of actors, contextual requirements, operational difficulty, traceability, and costs across each stage of the money laundering process: placement, layering, and integration. The analysis indicates that, while more sophisticated techniques may provide a higher degree of anonymity, they simultaneously require specialised technical expertise and meticulous planning. Consequently, there is a trade-off between the level of privacy attainable and the operational complexity inherent to each method. By systematically comparing these strategies, this analysis aims to contribute to a deeper understanding of cryptocurrency-based money laundering techniques, providing insight for more effective prevention and mitigation measures for both regulatory authorities and the financial sector.

This paper studies the interplay of machine learning and sampling scheme in an empirical analysis of money laundering detection algorithms. Using actual transaction data provided by a U.S. financial institution, we study five major machine learning algorithms including Bayes logistic regression, decision tree, random forest, support vector machine, and artificial neural network. As the incidence of money laundering events is rare, we apply and compare two sampling techniques that increase the relative presence of the events. Our analysis reveals potential advantages of machine learning algorithms in modeling money laundering events. This paper provides insights into the use of machine learning and sampling schemes in money laundering detection specifically, and classification of rare events in general.

The study econometrically analysed anti-money laundering regulations and banking sector stability in Africa. A panel data on 51 African countries over the period of 2012 to 2019 were used. Secondary data were sourced from the World Bank's indicators, the IMF, the Basel Institute on Governance other financial websites. The two-staged Generalised Moment Method (GMM) was used to analyse the effect of AML regulations on banking sector stability and the effects of the different levels of AML effectiveness and its impact on the banking sector stability in Africa. The study discovered that AML regulations had a significant positive effect on the stability of banking sectors in African countries. This indicated that whether there was high effectiveness or low effectiveness of the AML regulations, it would still have a positive impact on the stability of the banking sector of the country.

Purpose This study aims to estimate the amount of money laundering (ML) with multiple proxy approaches and measure the effects of ML on various indicators of the economic and financial sectors. Theoretical justifications are recruited from the parasite theory of organised crime. Design/methodology/approach A quantitative research methodology was used on a balanced panel data set to test the study’s hypothesis through generalised method of moment (GMM). The study sample consisted of 77 countries, and the data was collected for 15 years (2005–2019). Findings A study has found that 1.23% of global gross domestic product is laundered yearly, and there is no noticeable decline in ML activities. Further study has also found that ML has devastating effects on countries, government revenue, foreign investment, economic development, political and peace conditions, bank liquidity, interest rate volatility and exchange rate volatility. The study has not witnessed the negative consequence of ML on countries’ inflation rates. Practical implications Estimates of the study guide policymakers about the volume of resources fleeing and helps them to decide the level of response needed. Further findings help them prioritise the response system according to the area most affected. Originality/value This study is an original contribution by the authors and has studied the effects of ML by computing the amount of ML by four different proxies.

Criminal activities generate an estimated $2 trillion in laundered money per year, highlighting the need for financial institutions to detect and report suspicious activity to protect their reputation. However, rule-based models commonly used for this purpose generate a high number of false positives, draining compliance team time, and increasing investigation costs. However, the application of machine learning in conjunction with rule-based models presents noteworthy implications, encompassing the potential reduction in false positives and the concomitant risk of machine learning inadvertently suppressing true positive alerts. This paper proposes a framework called automatic suppression based on XGBoost for anti-money laundering (ASXAML) to enhance detection by reducing false positives. ASXAML leverages recursive feature elimination with cross-validation for optimal feature selection. Subsequently, Optuna is employed to fine-tune hyperparameters for the XGBoost model. Results indicate that ASXAML achieves an optimal balance between reducing false positives and avoiding missed money laundering events, with an 86% F-beta score and only 11% money laundering customers were incorrectly closed out of 1926 in the test data.