Noha E. El-Attar, Marwa Salama, Mohamed Abdelfattah, Sanaa Taha
Detecting, tracking, and preventing cryptocurrency money laundering within blockchain systems is a major challenge for governments worldwide. This paper presents an anomaly detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets. Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic Algorithm (GA) is further applied to determine the optimal hyperparameters for each machine learning model. The evaluations demonstrate the superiority of the XGBoost algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying GA, XGBoost’s performance metrics improved to 0.99 across all categories.
This dissertation investigates the crime of money laundering in the context of cryptoasset acquisition, from a functional-reductive perspective of criminal law. The central objective is to establish precise and coherent criteria for the incidence of criminal norms, aiming to limit the irrationality of punitive power and promote a counter-selective application, without compromising the accountability for complex criminal typologies. The study is pursued through a bibliographic review, analyzing national and international doctrine, legislation, and jurisprudence, complemented by interdisciplinary sources from economics, technology, and sociology. The first chapter establishes the theoretical premises of Zaffaroni's negative/agnostic theory of punishment and the reductive penal system. Criminal law, from this perspective, functions to limit punitive power. The second chapter delves into the crime of money laundering, analyzing its evolution, phases, the affected legal interest (socioeconomic order, with a focus on free competition and initiative), and its typical elements, based on the established theoretical framework. The third chapter comprehends the phenomenon of cryptoassets, detailing the functioning of Bitcoin and blockchain, the universe of decentralized finance (DeFi), forms of acquisition, and the Brazilian regulatory framework. The fourth chapter applies the dogmatic framework to the analysis of money laundering's typicality in different forms of cryptoasset acquisition and discusses the aggravating factor for the use of virtual assets. The research achieves its general objective by demonstrating a path for criminal dogmatics to establish precise criteria and limits for the incidence of money laundering, legitimizing judicial decisions by curbing punitive arbitrariness. Criteria are established to identify typical and atypical cases of money laundering involving cryptoasset acquisition, and requirements are set for the application of the special aggravating factor, conditioning it on the concrete demonstration that the use of cryptoassets intensified the harm to the legal interest. The interdisciplinary approach and consideration of the Brazilian reality are crucial to reject the trivialization of the institute and fulfill the counter-selective function of the penal system, preventing the automatic imputation of this serious crime without a proper technological understanding and knowledge of money laundering's limits
The rise of Web3 and Decentralized Finance (DeFi) has enabled borderless access to financial services empowered by smart contracts and blockchain technology. However, the ecosystem's trustless, permissionless, and borderless nature presents substantial regulatory challenges. The absence of centralized oversight and the technical complexity create fertile ground for financial crimes. Among these, money laundering is particularly concerning, as in the event of successful scams, code exploits, and market manipulations, it facilitates covert movement of illicit gains. Beyond this, there is a growing concern that cryptocurrencies can be leveraged to launder proceeds from drug trafficking, or to transfer funds linked to terrorism financing. This survey aims to outline a taxonomy of high-level strategies and underlying mechanisms exploited to facilitate money laundering in Web3. We examine how criminals leverage the pseudonymous nature of Web3, alongside weak regulatory frameworks, to obscure illicit financial activities. Our study seeks to bridge existing knowledge gaps on laundering schemes, identify open challenges in the detection and prevention of such activities, and propose future research directions to foster a more transparent Web3 financial ecosystem -- offering valuable insights for researchers, policymakers, and industry practitioners.
Benjamin Appiah, Daniel Commey, Winful Bagyl-Bac, Laurene Adjei · 5 authors
Maximal Extractable Value (MEV) presents a significant challenge to the fairness and efficiency of decentralized finance (DeFi). This paper provides a game-theoretic analysis of the strategic interactions within the MEV supply chain, involving searchers, builders, and validators. A three-stage game of incomplete information is developed to model these interactions. The analysis derives the Perfect Bayesian Nash Equilibria for primary MEV attack vectors, such as sandwich attacks, and formally characterizes attacker behavior. The research demonstrates that the competitive dynamics of the current MEV market are best described as Bertrand-style competition, which compels rational actors to engage in aggressive extraction that reduces overall system welfare in a prisoner’s dilemma-like outcome. To address these issues, the paper proposes and evaluates mechanism design solutions, including commit–reveal schemes and threshold encryption. The potential of these solutions to mitigate harmful MEV is quantified. Theoretical models are validated against on-chain data from the Ethereum blockchain, showing a close alignment between theoretical predictions and empirically observed market behavior.
Abstract By converting between currencies, cryptocurrency exchanges provide access between the traditional and cryptocurrency ecosystem, making them susceptible to money laundering. The European Union extended the scope of the 5 $$^{\text {th}}$$ Anti-Money Laundering Directive (AMLD5) to include cryptocurrency exchanges, requiring them to obtain a registration, conduct customer due diligence, and report unusual transactions. It is, however, unknown whether the measures introduced by the implementation of AMLD5 lead to less risk exposure and what impact it has on cryptocurrency exchanges. This paper uses a mixed-methods approach to explore the effects of the Dutch implementation of AMLD5 measures on cryptocurrency exchanges active in the Netherlands. We analyzed over 335,000 transactions and complemented them with seven qualitative interviews with Dutch cryptocurrency exchanges and the supervisory authority. We find that the Dutch implementation of AMLD5 imposed high administrative burdens and substantial fees on relatively small exchanges that do not pose high money laundering risks. This raises questions about the alignment of the goals and consequences of the regulation.
Ioannis Sfyrakis, Paolo Modesti, Lewis Golightly, Minaro Ikegima
Blockchain and smart contracts have transformed industries by automating complex processes and transactions. However, this innovation has introduced significant security concerns, potentially leading to loss of financial assets and data integrity. The focus of this research is to address these challenges by developing a tool that can enable developers and testers to detect vulnerabilities in smart contracts in an efficient and reliable way. The research contributions include an analysis of existing literature on smart contract security, along with the design and implementation of a lightweight vulnerability detection tool called LightCross. This tool runs two well-known detectors, Slither and Mythril, to analyse smart contracts. Experimental analysis was conducted using the SmartBugs curated dataset, which contains 143 vulnerable smart contracts with a total of 206 vulnerabilities. The results showed that LightCross achieves the same detection rate as SmartBugs when using the same backend detectors (Slither and Mythril) while eliminating SmartBugs’ need for a separate Docker container for each detector. Mythril detects 53% and Slither 48% of the vulnerabilities in the SmartBugs curated dataset. Furthermore, an assessment of the execution time across various vulnerability categories revealed that LightCross performs comparably to SmartBugs when using the Mythril detector, while LightCross is significantly faster when using the Slither detector. Finally, to enhance user-friendliness and relevance, LightCross presents the verification results based on OpenSCV, a state-of-the-art academic classification of smart contract vulnerabilities, aligned with the industry-standard CWE and offering improvements over the unmaintained SWC taxonomy.
In recent years, surveys on vulnerability detection tools for Solidity-based smart contracts have shown that many of them display poor capabilities. One of the causes for such deficiencies is the absence of quality benchmarking datasets, where bugs typically found in smart contracts are present in quantity and accurately labeled. VulLab’s main aim is to help tackle this issue as a framework that incorporates both, state-of-the-art vulnerability insertion and vulnerability detection tools. Such capabilities empower users to seamlessly generate benchmark capable datasets from collected contracts and employ them to validate novel analysis tool and obtain an accurate comparison with current state-of-the-art solutions. The framework was able to, from 50 smart contracts collected from the Ethereum mainnet, generate an annotated dataset more than 300 entries which included 20 unique vulnerabilities, and use them to compare 14 analysis tools in approximately 24 hours. VulLab is open-source and is available at https://github.com/lsRyan/vullab.
A segurança de contratos inteligentes continua sendo um desafio na blockchain Ethereum. Este artigo investiga a evolução de ferramentas de análise de segurança por meio de dois experimentos com a estrutura SmartBugs. O primeiro analisa 215 contratos do Etherscan verificados recentemente, focando nas vulnerabilidades detectadas. O segundo replica um estudo de 2020, usando o mesmo conjunto de contratos com vulnerabilidades, mas com ferramentas atualizadas. Resultados indicam defasagem da taxonomia DASP Top 10 e uma queda na precisão de detecção (de 41,7% para 24,3%), levantando dúvidas sobre o real progresso das ferramentas.
The success of terrorist organizations in maintaining traditional resources to finance terrorist operations is sufficient to push them away from virtual currencies and their usual risks, as long as they are able to sell oil and transfer funds between their territories, and as long as their funds remain safe from attacks and persecution by the international community. Encrypted virtual currencies are characterized by high degrees of secrecy, privacy, and decentralization - and extremist religious groups And terrorism that adopts violence as a means of operation and expansion, and studying indicators indicating the growing importance of these currencies in circulation, exchange, and commercial transactions, nd in financing extremist religious groups and organizations, and financing the purchase of weapons and equipment used by these groups. It is a decentralized currency with no competent authority, and no central bank responsible for issuing it, and it is not subject to the restrictions of international banking and monetary institutions. This is a significant advantage that has attracted many individuals and groups to its circulation. Had international institutions and organizations been able to subject this currency to international oversight, or to a central authority, it would have lost its most important advantage, and terrorist and extremist organizations would have been unable to exploit it further.
Dramatic price swings and the possibility of extreme returns have made Bitcoin a hot topic of interest for investors and researchers alike. With the help of advanced neural network models including CNN, RCNN, and LSTM networks, this paper has delved deep into the intricacies of Bitcoin price behavior. We will study different time intervals—close-to-close, close-to-open, open-to-close, and day-to-day—to find a pattern that we can use to develop an investment strategy. The average volatility over a year, six months, and three months is compared with the predictive power of volatility versus a traditional buy-and-hold strategy. Our findings point out the strengths and weaknesses of each neural network model and provide useful insights into optimizing cryptocurrency portfolios. This study contributes to the literature on the price prediction and volatility analysis of cryptocurrencies, thus providing useful information to both researchers and investors to execute strategic steps within the volatile cryptocurrency market.
This article examines how the design features of retail central bank digital currencies (CBDCs) influence the detection and prevention of crypto-enabled money laundering. Drawing on a comparative analysis of Russia, the European Union, the United States, and Malta, it evaluates the effectiveness of CBDC-integrated anti-money laundering (AML) mechanisms in addressing the three key stages of illicit finance: placement, layering, and integration. Using primary sources, including pilot program data, legislative texts, and policy consultations, alongside secondary academic literature, the Study explores how design elements such as ledger visibility, programmable transaction limits, sanctions screening, and tiered privacy structures can be embedded into CBDC infrastructure. The findings reveal significant variation in enforcement capacity, privacy protection, and governance transparency across jurisdictions, shaped by political economy, legal traditions, and technological architectures. The article argues that while CBDCs offer unprecedented opportunities to embed compliance at the core of payment systems, their legitimacy and adoption depend on the careful balancing of enforcement effectiveness with constitutional safeguards, civil liberties, and public trust. Policy recommendations emphasize jurisdiction-specific typology mapping, programmable safeguards, stakeholder engagement, tiered anonymity, cross-border interoperability, and independent oversight. The analysis concludes that CBDCs, if responsibly designed, can modernize AML frameworks and strengthen financial integrity without undermining democratic principles.
This study delves into the differences between traditional financial markets, as proxied by their corresponding future contracts, and the cryptocurrency market, focusing on Bitcoin, during major global events: the COVID-19 pandemic, the Russia-Ukraine war, and the Israel–Palestine conflict. It reveals Bitcoin’s increased trading volume post-COVID-19, highlighting its appeal as a digital safe haven. This trend persists during subsequent crises, suggesting a strategic shift towards cryptocurrencies as diversification tools. Despite volume fluctuations, Bitcoin’s price stability reflects investor confidence in its long-term viability. The significant change in EuroStoxx 50 returns during the Israel–Palestine conflict, highlights localized geopolitical influences on markets. The study underscores the importance of considering both global and regional factors in investment decisions. It emphasizes cryptocurrencies’ growing significance in the global financial market, particularly during crises, and suggests further exploration into investor behavior and regulatory effects. Understanding these dynamics is crucial for navigating the evolving financial landscape.
ABSTRACT This study aims at bridging critical gaps in the existing cryptocurrency research by exploring combinations of technological, macroeconomic and behavioural factors, namely, economic agents' expectations and the size of influence that each of them has on the Bitcoin price movements. In contrast to the existing studies that focused on individual determinants and estimated aggregate effects thereof, in this study, fuzzy‐set qualitative comparative analysis (fsQCA) is applied to determine configurations of drivers to determine the Bitcoin price and used necessary condition analysis (NCA) to quantify the magnitude of the effects using the monthly data between 2011 and 2022. Findings show that economic agents' expectations such as OECD's Business Confidence Index, Consumer Confidence Index and Composite Leading Indicator emerge as influential variables of Bitcoin, surpassing traditional drivers like Gold and Financial Stress Index. Among these, Business Confidence Index and Composite Leading Indicator exhibit a very large effect on Bitcoin prices, and from the technology variable group, Average Block Size exhibits a very large effect on Bitcoin prices. fsQCA indicates that nine distinct configurations contribute to high Bitcoin prices and eight configurations lead to low Bitcoin prices, thus depicting equifinality in Bitcoin price determination. These insights can provide policymakers and investors with a better understanding of the Bitcoin price dynamic by finding out necessary variables and equifinal pathways towards either high or low prices, thus promoting better risk management activities, as well as regulatory approaches to this highly dynamic asset class.
The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) and graph-based entity mapping (GraphSAGE) to detect illicit financial flows with unprecedented precision. By leveraging data from South Africa’s FIC, Zimbabwe’s RBZ, and SWIFT, FALCON achieved 98.7%, surpassing Random Forest (72.1%) and human auditors (64.5%), while reducing false positives to 1.2% (AUC-ROC: 0.992). Tested on 1.8 million transactions, including falsified CTRs, STRs, and Ethereum blockchain data, FALCON uncovered $450 million laundered by 23 shell companies with a cross-border detection precision of 94%, directly mitigating illicit financial flows in Southern Africa. For regulators, FALCON met FAFT standards, yielding 92% court admissibility, and its GDPR-compliant design (ε = 1.2 differential privacy) met stringent legal standards. Deployed on AWS Graviton3, FALCON processed 2 million transactions/second at $0.002 per 1000 transactions, demonstrating real-time scalability, making it cost-effective for financial institutions in emerging markets. As the first AI framework tailored for Southern Africa’s financial ecosystems, FALCON sets a new benchmark for ethical AML solutions in emerging economies with immediate applicability to CBDC supervision. The transparent validation of publicly available data underscores its potential to transform global financial crime detection.
The financial world is at the crossroads, and digital monies, decentralized privacy, and asset tokens recreate centuries-old constructs. Blockchain options are challenging conventional clearing houses as never before, by operating outside of the set parameters. This article examines the complex interaction of old-world clearing systems with new-fangled, crypto settlement mechanisms, deconstructs prickly issues and precious opportunities facing Central Counterparty Clearing Houses. The cryptocurrency environment has developed different settlement methods, but advanced investors are eager to have safe and regulated access to digital assets. Its essence is that blockchain promises to render bypassing middlemen through direct transactions a reality, but, in the meantime, it poses a threat to current systems and presents a new way to envision clearing. This article shows how new clearing corporations can help solve the problem of finance, and even support better market performance and transparency along with stability alongside key protections because innovative hybrid enterprise models can actually become a bridge between old-fashioned finance and digital networks and even increase their reliability, integrity, and stability in the long-term future.
Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017–2020) and Biden (2021–2024) governments. The 48-week return forecast of the Bitcoin–Gold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes.
Kostiantyn Orobets, V. I. Shkolnikov, Tetiana Batrachenko, Тетяна Василівна Барановська · 5 authors
Introduction: The legal regime of cryptocurrency in different countries of the world is heterogeneous. In some, it is not defined at all, which leads to legal conflicts, including when qualifying crimes committed with cryptocurrency use. The situation is further complicated because such crimes can occur in the territories of several states where cryptocurrency has a different legal regime. Traditional legislation and mechanisms for combating money laundering and terrorist financing are practically ineffective in the landscape of crimes involving the use of cryptocurrency.Objectives: The aim of the study is to systematise the main patterns of crimes related to the use of cryptocurrency, as well as analyse existing vectors of their legal assessment, appropriate design and application of effective methods of combating these crimes.Methods: Based on the methods of analysis and synthesis, qualitative data analysis, using content analysis as the primary research tool, it is shown that the main problem in preventing the use of cryptocurrency in predicate crimes lies in the technical difficulty of identifying a person or group of persons who carry out cryptocurrency transactions for illegal purposes. Such goals may be aimed at legalising funds, i.e., concealing their illegal origin, making payments in a hidden network, organising various fraudulent schemes, financing terrorism, and other crimes.Results: The article argues that given the technical specifics of cryptocurrency transactions and the technical capabilities of "masking" the origin of cryptocurrency funds, it is necessary to develop methods for studying trace formation and develop an algorithm for establishing and consolidating forensically significant information for this type of crime. The results indicate that the future of law enforcement in the fight against cryptocurrency-related crime will require a multifaceted approach. Agencies must adopt a proactive approach by foreseeing emerging criminal strategies. To protect the public from crimes using digital assets, law enforcement must be flexible, progressive, and technologically savvy as cryptocurrencies continue to develop. The development of provisions on cryptocurrency also determines the theoretical significance of the work as an object and means of committing crimes, a surrogate means of payment during the commission of certain crimes.Conclusions: The practical significance of the work lies in the possibility of using its results to solve problems arising in the law-making activities of state authorities and law enforcement activities, as well as in developing recommendations for improving criminal legislation in the field of cryptocurrency-related crimes.
This paper addresses the problem of detecting money laundering in the Bitcoin network. Money laundering is the process of handling the proceeds of crime to conceal their illegal source, these illicit transactions have complex features, similar to those of legal transactions. It is well known that transactions can be represented as topological graph structure data, and many GCN-based methods have been developed for Anti-Money Laundering (AML) tasks. However, existing methods have not performed as well in dynamically assigning weights to neighboring nodes and extracting information from global nodes in the Bitcoin network. Therefore, we identify three major challenges: Firstly, GCNs can be misled by concealed illegal transactions due to uniform node representation weights. Secondly, current node-level GCNs cannot handle varied methods of concealing illegal transactions because they fail to extract global information. Thirdly, the costliness of data labelling necessitates the effective use of limited but rich domain-specific labelled data. To address these challenges, we propose the Transformer-enhanced Graph Attention Network (TFGAT) with a Global-Local Attention Mechanism (GLATM) that uses Transformers to extract global information and selectively focus on local information from connected nodes. Due to the limited availability of labelled data from expensive data labelling processes, we introduce a Deep Cyclic Pseudo-Label Updating Mechanism (DCPLU) to enhance data distribution and model robustness, which does not rely on manifold structure or Euclidean distance assumptions. DCPLU can enhance model performance while preserving the model's existing parameters, enabling it to maintain its current faster response time in the application scenario. Experimental results show that our methods outperform existing models across various metrics.
El Salvador's adoption of Bitcoin as legal tender in 2021 represented a bold but ultimately flawed experiment in national cryptocurrency integration.This paper critically examines the key challenges that led to the policy's failure, including public rejection, technological deficiencies, market volatility, and international financial pressure.Despite government incentives, Bitcoin adoption remained minimal, with security issues in the Chivo wallet further eroding trust.The collapse of cryptocurrency markets in 2022 exposed the country's economic vulnerability, forcing El Salvador to scale back its Bitcoin strategy under IMF loan conditions.The findings highlight critical lessons for other nations considering similar policies, emphasizing the need for phased implementation, robust financial infrastructure, and regulatory clarity.Future research should explore alternative digital currency models, particularly Central Bank Digital Currencies (CBDCs), and the socio-economic implications of large-scale cryptocurrency adoption.
The rapid adoption of Non-Fungible Tokens (NFTs) has revolutionised the digital art and collectibles markets. NFTs present novel opportunities for creators and investors alike. However, with such opportunities also comes the risk of money laundering through NFTs. The South African digital art market has not been spared from the rising phenomenon of NFTs. This rising phenomenon has brought with it questions regarding whether the South African anti-money laundering (AML) regime can adequately counter the challenge of money laundering through NFTs. This is particularly so if one considers that, generally, the AML regulatory framework for NFTs is still nascent, not only in South Africa, but also globally. Thus, this contribution comparatively examines the AML regulation of NFTs in South Africa to establish the adequacy and efficacy of the country’s AML regime. The paper concludes that while NFTs are still new, they can be dealt with under the blanket regulation for crypto assets and in specific use cases, AML regulations may be applied to them.
Rug pulls present a critical threat in Decentralized Finance (DeFi), causing substantial financial losses and eroding ecosystem trust. Despite research advances, effective detection remains hampered by fragmented taxonomies, limited datasets, and inadequate tool evaluations. Through systematic analysis of academic and industry sources, we develop a comprehensive taxonomy of 35 distinct rug pull types, including 9 previously undocumented variants. Our analysis reveals significant detection gaps: existing datasets cover only 20% of known types, leading us to create an enhanced dataset of 2,391 instances that increases coverage to 82.9%. Evaluation of 13 detection tools shows substantial capability variation (25.7% to 62.9%), with 9 types completely undetectable. Most critically, tool performance degrades significantly when facing complex attacks, with maximum detection rates dropping from 55.6% for single-vector cases to 31.3% for compound scenarios. These findings provide essential insights for developing more robust security testing approaches for smart contract vulnerabilities in decentralized systems.
DeFiisrevolutionizingthefinancialworldbyprovidingopen,approval-free,peer-to-peerwaysto transact,thankstoblockchain.DeFiallowsmorefinancialopportunitiesanddifference,butitalso presentsproblemsforAMLandcompliance due tohowitisdecentralized,usespseudonymsand is available in different countries. It describes in detail the barriers faced in DeFi within the EU duetotechnologyandregulations.ItdiscusseswhytraditionallawsareoftenunsuitableforDeFi, leading to questions about regulations, regulatory boundaries and any gaps in enforcing them. StudyingspecificcasesintheEU,thearticleexploresthejourneyofAMLregulationsandpoints out some of the obstacles inside the regulatory sphere due to swift changes in decentralized technology.Movingon,ithighlightsthatitisdifficulttoenforcethelawindecentralizednetworks. The study puts forward a group of guidelines in policy, law and technology to improve AML compliance in DeFi without hindering its advancements. For example, EU regulators may align theirrulesformemberstates,createbetterframeworksforliabilityofdecentralizedmarketactors, utilizeregtechandencourageteamworkbetweenregulators,technologistsandindustrymembers. Based on the findings, rigid and uncooperative regulations will not only fail to tackle issues in DeFibutalsoslowdowninnovation.Thus,thisarticleoffersideasforfuturediscussionsandrules on safeguarding money matters in the growing world of decentralized finance.
Money laundering has long been a major issue for governments, law enforcement agencies, and financial institutions around the globe. As technology advances, so too do money laundering methods, presenting new challenges for authorities and financial entities. Organised Crime Groups (OCGs) are increasingly exploiting digital platforms, cryptocurrencies, and virtual assets to disguise illicit funds while maintaining anonymity and complicating their transactions. This article analyses the problem of cryptocurrency laundering by the OCGs and various tactics employed by the OCGs to cover their trails. This article also in-depth discusses the international instruments such as the United Nations Convention against Transnational Organised Crime and Financial Action Task Force recommendations on the prevention of cryptocurrency laundering. The special focus of this paper is on the legal framework regarding cryptocurrency laundering in the United States, European Union and Malaysia. The findings of the paper suggest that there is a regulatory framework present in these jurisdictions but their regulations are not subject specific and regulatory powers have been granted to the authorities that are not specialised and skilled to tackle the problem of combating cryptocurrency laundering by OCGs.