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The volume of the asset investment and trading market can be expanded through the issuance and management of blockchain-based security tokens that logically divide the value of assets and guarantee ownership. This paper proposes a service model to solve a problem with the existing investment service model, identifies security threats to the service model, and specifies security requirements countering the identified security threats for privacy protection and anti-money laundering (AML) involving security tokens. The identified security threats and specified security requirements should be taken into consideration when implementing the proposed service model. The proposed service model allows users to invest in tokenized tangible and intangible assets and trade in blockchain-based security tokens. This paper discusses considerations to prevent excessive regulation and market monopoly in the issuance of and trading in security tokens when implementing the proposed service model and concludes with future works. [ABSTRACT FROM AUTHOR]

The Hawala system, an informal and traditional money transfer mechanism, has been a subject of concern for counterterrorism and counterintelligence agencies worldwide due to its potential exploitation for illicit financial activities, including terrorism financing. This paper provides a comprehensive examination of the Hawala system's presence in the Western Balkans, a region historically characterized by geopolitical complexity and a complex security landscape. Drawing on empirical data and regulatory analysis, this study explores the challenges posed by the Hawala system in the context of counterterrorism and counterintelligence efforts in the Western Balkans. It examines the system's vulnerabilities, its exploitation for terrorism financing and the difficulties faced by intelligence agencies in monitoring and disrupting Hawala networks. Moreover, the paper assesses the regulatory frameworks implemented by countries in the region and offers practical policy recommendations to enhance counterterrorism and counterintelligence strategies, fostering greater security and stability in the Western Balkans. [ABSTRACT FROM AUTHOR]

Money laundering identification (MLI) is a challenging task for financial AI research and application due to its massive transaction volume, label sparseness, and label bias. Most of the existing MLI methods focus on individual-level abnormal behavior while neglecting the community factor that money laundering is a collaborative group crime. Furthermore, the massive volume of transactions and the issue of label shifting also impede the application of supervised or semi-supervised models. To this end, this paper proposes an efficient community-oriented algorithm, namely SEGE, to identify money laundering based on structural entropy minimization (SEM) with graph embedding in an unsupervised approach. Experiments on both a private real-world money laundering network and a public synthetic dataset show that our SEGE algorithm derives prominent performance and outperforms the parameterized learning-based graph representation methods. Moreover, we find that there are pervasive sub-communities in the real-world money laundering network. Based on our local algorithm, we propose a real combat strategy against the money laundering group, in which when we have several scattered suspicious accounts in the transaction network, we are able to retrieve the whole money laundering group by the union of sub-communities with both high precision and high recall rates. [ABSTRACT FROM AUTHOR]

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others. [ABSTRACT FROM AUTHOR]

This paper deals with ensuring the sustainability of the financial system and combating hybrid threats in relation to anti-money laundering and counter-terrorist financing (AML/CTF) measures. International cooperation in the field of combating hybrid threats is only at the beginning, and in many ways, the experience of international cooperation in the fight against money laundering and terrorist financing, which is based on many years of experience in the institutional and legislative fields, could be used. Hybrid threats are constantly changing and evolving, which means our response to them must also constantly evolve and adapt. The aim of the presented study is the analysis of the problem of the legalization of income from criminal activity and the financing of terrorism and their possible relationship with the fight against hybrid threats and maintaining the stability of the financial system. [ABSTRACT FROM AUTHOR]

How is authority legitimated when global policy leads to the delegation of implementation and monitoring tasks to private actors? This paper shows that putting private actors at the forefront of compliance gives them significant interpretation autonomy, with knock-on effects for how rule-making authority is legitimated. I show that actors can mobilize to claim expertise based on their practice in, and input to, transnational administration and that professional associations are important arenas for establishing and legitimating these claims. The paper explores these dynamics through an empirical focus on the global anti-money laundering regime. Revolving around the Financial Action Task Force, the regime to tackle illicit finance has been a highly political process with a global reach. Yet it relies heavily on financial institutions to put systems in place that control the flow of money. This puts private compliance officers at the forefront of transnational administration. The paper shows that compliance officers have responded to the demands of this role by professionally organizing. Focusing on the largest professional association, the Association of Certified Anti-Money Laundering Specialists or ACAMS, a US-based association with a global program and membership, the paper explains that professionalization mechanisms have helped demarcate practice boundaries and support the expertise claims of private sector employees. By becoming the experts, these officers can claim pragmatic authority over what global policy rules and standards mean and how they can be implemented. [ABSTRACT FROM AUTHOR]

Criminal networks continue to utilize the global financial system to launder their proceeds of crime, despite the broad enactment of anti-money laundering (aml) laws and regulations in many countries. Money laundering consumes capital resources and the tax revenue needed to fund infrastructure development and alleviate poverty in developing market economies. This paper, therefore, expands on the tools available for enabling privacy-preserving data mining in multidimensional datasets to combat cross-border money laundering. Most importantly, this paper develops a novel measure for detecting anomalies in cross-border financial networks, allowing financial institutions and regulatory organizations to identify suspicious nodes. The research used a sample dataset comprising international financial transactions and a hypothetical dataset to demonstrate the measure of node importance and the symmetric-key encryption algorithm. The results support the argument that the proposed network measure can detect node anomalies in the cross-border financial flows network, enabling regulatory authorities and law enforcement agencies to investigate financial transactions for suspicious activity and criminal conduct. The encryption algorithm can ensure adherence to information privacy laws and policies without compromising data reusability. Hence, the proposed methodology can improve the proactive management of money laundering risks associated with cross-border fund flows for the global financial system’s benefit. [ABSTRACT FROM AUTHOR]

The paper aims to provide an in-depth understanding of the effectiveness of anti-money laundering (AML) regulations in measuring banking sector stability in Western Balkan countries, as well as to explore the possibility of enhancing banking sector policy and performance. The study employs a quantitative methodology created on secondary data from 2012 to 2021. The data analysis methodology incorporates static and dynamic approaches to examine the banking sector stability using OLS and 2SLS. The results of the study show that the tight A M L i , t implementation of both approaches has a positive and statistically significant impact on the banking sector stability ( B S S ). The value of the paper is unique in that it applies the most recent data in this field for Western Balkans countries, and it brings benefits for a better understanding of the effectiveness of A M L regulations. The research will encourage fruitful discussion among policymakers, practitioners, researchers, and financial institution executives. [ABSTRACT FROM AUTHOR]

The Hawala system, an informal and traditional money transfer mechanism, has been a subject of concern for counterterrorism and counterintelligence agencies worldwide due to its potential exploitation for illicit financial activities, including terrorism financing. This paper provides a comprehensive examination of the Hawala system’s presence in the Western Balkans, a region historically characterized by geopolitical complexity and a complex security landscape. Drawing on empirical data and regulatory analysis, this study explores the challenges posed by the Hawala system in the context of counterterrorism and counterintelligence efforts in the Western Balkans. It examines the system’s vulnerabilities, its exploitation for terrorism financing and the difficulties faced by intelligence agencies in monitoring and disrupting Hawala networks. Moreover, the paper assesses the regulatory frameworks implemented by countries in the region and offers practical policy recommendations to enhance counterterrorism and counterintelligence strategies, fostering greater security and stability in the Western Balkans.

AbstractThe paper aims to provide an in-depth understanding of the effectiveness of anti-money laundering (AML) regulations in measuring banking sector stability in Western Balkan countries, as well as to explore the possibility of enhancing banking sector policy and performance. The study employs a quantitative methodology created on secondary data from 2012 to 2021. The data analysis methodology incorporates static and dynamic approaches to examine the banking sector stability using OLS and 2SLS. The results of the study show that the tight [Formula: see text] implementation of both approaches has a positive and statistically significant impact on the banking sector stability ([Formula: see text]). The value of the paper is unique in that it applies the most recent data in this field for Western Balkans countries, and it brings benefits for a better understanding of the effectiveness of [Formula: see text] regulations. The research will encourage fruitful discussion among policymakers, practitioners, researchers, and financial institution executives.

The volume of the asset investment and trading market can be expanded through the issuance and management of blockchain-based security tokens that logically divide the value of assets and guarantee ownership. This paper proposes a service model to solve a problem with the existing investment service model, identifies security threats to the service model, and specifies security requirements countering the identified security threats for privacy protection and anti-money laundering (AML) involving security tokens. The identified security threats and specified security requirements should be taken into consideration when implementing the proposed service model. The proposed service model allows users to invest in tokenized tangible and intangible assets and trade in blockchain-based security tokens. This paper discusses considerations to prevent excessive regulation and market monopoly in the issuance of and trading in security tokens when implementing the proposed service model and concludes with future works.

This study examines factors influencing Ghanaian banks' compliance with anti-money laundering (AML) legislation. Drawing upon institutional, compliance, and dynamic capability theories, the study identifies the interplay of organisational, regulatory, and employee factors influencing compliance outcomes. A mixed methods approach was used to collect data from 23 universal banks, 9 local and 14 foreign, in Ghana, focusing on experienced managers and employees in risk, legal, operations, compliance, and business development departments. The findings show that employee characteristics like due diligence and moral involvement have a positive relationship with compliance with AML regulations; however, contrary to expectations, effective AML/CFT programs did not significantly impact banks' adherence to these regulations. The association between moral engagement, an innovative culture, and AML compliance is weakened by normative power and an innovative culture acting as negative moderators. This study contributes empirical evidence to the literature on AML compliance in emerging markets and offers practical implications for policymakers, regulators, and banking professionals seeking to boost regulatory effectiveness and mitigate financial crime risks. This study provides a foundation for targeted interventions and strategic initiatives aimed at strengthening the AML regulatory landscape in Ghana and other countries. [ABSTRACT FROM AUTHOR]

Criminal networks continue to utilize the global financial system to launder their proceeds of crime, despite the broad enactment of anti-money laundering (aml) laws and regulations in many countries. Money laundering consumes capital resources and the tax revenue needed to fund infrastructure development and alleviate poverty in developing market economies. This paper, therefore, expands on the tools available for enabling privacy-preserving data mining in multi-dimensional datasets to combat cross-border money laundering. Most importantly, this paper develops a novel measure for detecting anomalies in cross-border financial networks, allowing financial institutions and regulatory organizations to identify suspicious nodes. The research used a sample dataset comprising international financial transactions and a hypothetical dataset to demonstrate the measure of node importance and the symmetric-key encryption algorithm. The results support the argument that the proposed network measure can detect node anomalies in the cross-border financial flows network, enabling regulatory authorities and law enforcement agencies to investigate financial transactions for suspicious activity and criminal conduct. The encryption algorithm can ensure adherence to information privacy laws and policies without compromising data reusability. Hence, the proposed methodology can improve the proactive management of money laundering risks associated with cross-border fund flows for the global financial system’s benefit.

Blockchain-based cryptocurrencies, such as Bitcoins, are increasingly popular. However, the decentralized and anonymous nature of these currencies can also be (ab)used for nefarious activities such as money laundering, thus reinforcing the importance of designing tools to effectively detect malicious transaction misbehaviors. In this paper, we propose TMAS, a transaction misbehavior analysis scheme for blockchain-based cryptocurrencies. Specifically, the proposed system includes ten features in the transaction graph, two heuristic money laundering models, and an analysis method for account linkage, which identifies accounts that are distinct but controlled by an identical entity. To evaluate the effectiveness of our proposed indicators and models, we analyze 100 million transactions and compute transaction features, and are able to identify a number of suspicious accounts. Moreover, the proposed methods can be applied to other cryptocurrencies, such as token-based cryptocurrencies (e.g., Bitcoins) and account-based cryptocurrencies (e.g., Ethereum).

Money laundering is the process by which criminals move large sums of illicit money to hidden locations and integrate them as legal funds through existing financial services. The United Nations (UN) estimates that 2 to 5% of global GDP, which is approximately 0.8 to 2.0 trillion dollars, is laundered globally every year. Therefore, accurately identifying such globally alarming activities is crucial for enforcing anti-money laundering (AML) measures. Numerous techniques have been proposed to detect money laundering from transaction graphs of money transfers between bank accounts by analysing the structural and behavioural dynamics of their corresponding dense subgraphs. However, these techniques often do not consider that money laundering usually involves high-volume flows of funds through chains of bank accounts. Moreover, most AML approaches either result in lower detection accuracy or incur higher computational costs, making them less reliable and suitable for real financial systems. Consequently, only a fraction of money laundering activities can be detected and prevented. In this paper, we propose an efficient approach to AML by employing semi-supervised graph learning techniques on a large-scale financial transactional graph in both pipeline settings (i.e., graph embedding models are first trained to generate node embeddings that are combined with additional topological graph features to train binary classifiers) and end-to-end settings (i.e., node classification is performed by training SkipGCN, FastGCN, and EvolveGCN without requiring separate classifiers) to identify nodes involved in potential money laundering activities. We evaluate our approach on four datasets: AMLSim, Elliptic, IBM AML, and SynthAML, with a view to scalability and practicality for real financial systems. Further, we provide local (e.g., how money is laundered between nodes) and global (e.g., what factors contribute to money laundering) explanations of the predictions by highlighting the predominant factors in money laundering cases and elucidating the mechanisms of illicit fund transfers between nodes to enhance the interpretability and transparency of the AML models. Experimental results suggest that our approach is scalable and effective at detecting money laundering from real and synthetic transaction graphs.

The shifting environment of money laundering, exacerbated due to the war, calls for scrutiny of Ukraine's reaction to this fundamental challenge. This paper thus helps widen the knowledge about how Ukraine is trying to fight against money laundering and protect financial system integrity by shedding light on legislative transformations and their results in a much more complex geopolitical setting. Therefore, it provides an overview about the main elements of Ukrainian anti- money laundering policy and an analysis of reforms passed in this sphere of law since the full-scale invasion of Russia into Ukraine 2022. It focuses on the main changes of legislature in terms of anti-money laundering and terrorism financing prevention as well as first identifiable results of the new legislatorial and executive changes.

The decentralization and anonymity of blockchain have attracted significant attention. However, in recent years, there has been a rise in blockchain money laundering incidents, and anti-money laundering efforts have become crucial within the blockchain space. Blockchain money laundering differs from traditional financial money laundering as it does not provide account information, particularly in the case of Bitcoin. This absence of information makes it challenging for researchers to detect money laundering activities based on transaction data. We propose LB-GLAT, a novel Long-Term Bi-Graph Layer Attention Convolutional Network, to effectively capture the topological structure and attribute characteristics of money laundering on the blockchain transaction graph. LB-GLAT utilizes the transaction graph and the reverse transaction graph to solve the no-loop problem that results in the inability to capture the destination of blockchain transactions and designs a long-term layer attention mechanism to alleviate the over-smoothing problem. We implemented a series of experiments to evaluate LB-GLAT, which achieved state-of-art performance compared with other methods, presenting an accuracy of 0.9776, a precision of 0.9317, a recall of 0.8494, an F1−score of 0.8887, and an AUC of 0.9806. [ABSTRACT FROM AUTHOR]

Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this paper, we focus on anti-money laundering in banks and provide an introduction and review of the literature. We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging. We find that client risk profiling is characterized by diagnostics, i.e., efforts to find and explain risk factors. On the other hand, suspicious behavior flagging is characterized by non-disclosed features and hand-crafted risk indices. Finally, we discuss directions for future research. One major challenge is the need for more public data sets. This may potentially be addressed by synthetic data generation. Other possible research directions include semi-supervised and deep learning, interpretability, and fairness of the results.

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address–transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others.

This study considers the impact of the FATF's greylisting process from a market perspective. The results are intended to inform the development of public policy and improvement of market signalling. The study develops a theoretical market impact model and identifies the indicators which may impact banking operations and institutional decisions. It is explicitly market-oriented in that the model seeks to reflect how stakeholders in financial and non-financial markets typically respond to signals sent out by the FATF. The authors find that the FATF's greylisting signalling has changed over time and distinguish four broad periods reflecting different messaging. The study uses financial, trade, and other variables derived from the World Bank's 'World Development Indicators' databank to explore evidence of impact in each of the different phases of the FATF's approach to greylisting. The study uses a pooled cross-section and time series approach with fixed effects, based on a sample of 177 countries and 3540 country-years of data from 2000 to 2020. The study examines impacts on net official development assistance, the banking environment (non-performing loans, risk premiums), net foreign assets, indebtedness, and market capitalisation. It finds significant correlations between many financial variables and FATF listing events, including an apparent reduction in development assistance during greylisting periods which endured after the country was delisted. This is of significant concern, as such reductions may impact disproportionately on developing economies. [ABSTRACT FROM AUTHOR]

This study examined the current developments in customer due diligence (CDD) in the financial technology (FinTech) era. The study of anti-money laundering (AML) and combating financing of terrorism (CFT) gained prominence after the 2007–2009 global financial crisis (GFC), in which administrative penalties were issued to financial institutions. Faced with AML regulatory compliance issues, technological solutions were or are still being developed. Thus, several technological innovation developments have shaped the future direction of the CDD aspects in the AML/CFT sphere. A bibliometric review and meta-analysis was employed for the study. The Scopus database was utilised to generate the dataset for the study, while SciVal was applied for research metric analysis. The major findings revealed that the key research themes in this area include anti-money laundering, banks and crime, and cryptocurrency, as well as blockchain and corruption. It was also established that most of the research done in this area is focused on the United Kingdom, the United States, and China. The integration of CDD with FinTech is still an emerging area that requires interdisciplinary collaborations. [ABSTRACT FROM AUTHOR]

In this paper, starting from the "holistic" approach of the European Banking Authority (EBA) which reinforces the relevance of antimoney laundering in the prudential assessment of banks, a conceptual scheme is proposed for the calculation of the Economic Value Added of banking products showing how, in the face of various activities required for AML purposes (from onboarding to alert management to constant monitoring), the economic convenience of a relationship can be determined ex ante, at least for each product. The results confirm that for the same creditworthiness and cost of capital, the AML variable strongly affects the economic convenience of the individual products. Considering the partially inelastic nature of AML costs, the size of the operation is equally fundamental in determining whether to initiate relationships with certain types of customers.

Cryptocurrencies have the potential to enable socioeconomic growth throughout the world by offering easier access to capital and financial services. However, many virtual asset service providers (VASPs) that offer cryptocurrency services lack identity management and can be accessed anonymously, which has led to their services being exploited by criminal activities such as money laundering and illegal foreign exchange. Such crimes have a negative impact on socioeconomic sustainability. Building identity systems on blockchains can help VASPs improve their identity management to combat cryptocurrency-based crimes so VASPs can better serve the social economy and achieve their sustainability goals. However, existing solutions have privacy problems because the identity provider can associate users' identities with their wallet accounts. In addition, there is currently no solution that can support all public blockchains unconditionally, as current solutions can only support EVM-compliant blockchains or require additional work to support new blockchains. This article proposes a KYC (know your customer)-compliant identity scheme based on Ethereum using Merkle trees and smart contracts. The identity and wallet accounts are linked by the user rather than the KYC provider so, in general, no one but the user knows the association between the wallet accounts and the identity, which protects privacy. For suspicious accounts, supervisors can trace their identities and thus achieve supervision. In addition, the scheme supports identifying accounts on all public blockchains by using Merkle trees and smart contracts to bind accounts on multiple blockchains to one identity and no extra work is required. Moreover, the scheme supports users to prove that their attributes meet the requirements of VASPs by adopting the BBS+ signature and the Sigma protocol. [ABSTRACT FROM AUTHOR]

Monitoring financial transactions is a critical Anti-Money Laundering (AML) obligation for financial institutions. In recent years, machine learning-based transaction monitoring systems have successfully complemented traditional rule-based systems to reduce the high number of false positives and the effort needed to review all the alerts manually. Unfortunately, machine learning-based solutions also have disadvantages: while unsupervised models can detect novel anomalous patterns, they are usually characterized by a high number of false alarms; supervised models, instead, usually offers a higher detection rate but require a large amount of labeled data to achieve such performance. In this paper, we present Amaretto, an active learning framework for money laundering detection that combines unsupervised and supervised learning techniques to support the transaction monitoring processes by improving the detection performance and reducing the compliance management costs. Amaretto exploits novel selection strategies to target a subset of transactions for investigation, making more efficient use of the feedback provided by the analyst. We perform the experimental evaluation on a synthetic dataset provided by the industrial partner, which simulates the profiles of clients trading in international capital markets. We show that Amaretto outperforms state-of-the-art solutions by reducing money laundering risk and improving detection performance. In particular, we compare state-of-the-art unsupervised and supervised techniques commonly used in the AML domain with the ones implemented in this work. We show that the Isolation and Random Forests of Amaretto perform best in the task under analysis, with an AUROC of 0.9 for the first (20% better on average) and a detection rate of 0.793 for the second (30 % better on average). In addition, they are characterized by lower investigation costs computed in terms of the daily number of transactions to be examined and the number of false positives and false negatives. Finally, we compare Amaretto against a state-of-the-art active learning fraud detection system, achieving better detection performances and lower costs in all the analyzed scenarios. Worth mentioning, Amaretto improves the detection rate up to 50 % and reduces the overall cost by 20% in the most realistic scenario under analysis.

Money laundering is a global threat to society nowadays. Governments and governmental authorities fight money laundering, in part, by regulating banks and financial institutions. Financial institutions, in turn, are obligated to implement mechanisms to prevent money laundering. Usually, these prevention mechanisms include automated monitoring systems. In this paper, we propose an Anti-Money Laundering monitoring system based on machine learning techniques, which addresses three requirements: (i) generating accurate and non-redundant alerts; (ii) generating timely alerts; and (iii) associating explanations and risk estimates to each alert. The first requirement is addressed by training a machine learning classification model on different snapshots of customers’ history and then feeding the classification scores generated by this model into an alerting policy designed to prevent redundant alerts. The second requirement is addressed via custom metrics for assessing the performance of classification models, which take into account the timeliness requirements that arise in the Anti-Money Laundering domain. Finally, the third requirement is addressed by applying a method for class probability calibration and by an interpretability layer based on Shapley values. The proposed monitoring system has been designed based on requirements provided by an investigation unit at a financial institution and evaluated using real-life data as well as multiple rounds of feedback from specialized subject-matter experts.

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. [ABSTRACT FROM AUTHOR]