This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digital transactions. The exponential expansion of cross-border financial flows, real-time payment systems, and decentralized financial instruments has amplified regulatory fragmentation, multi-jurisdictional compliance friction, and sophisticated financial crime typologies. Utilizing institutional economics, information asymmetry theory, and computational compliance modeling, this paper analyzes how advanced algorithmic architecturesâspecifically Graph Neural Networks (GNNs), Natural Language Processing (NLP), and Federated Learningâoptimize anti-money laundering (AML), counter-terrorist financing (CFT), and real-time sanctions screening. The findings demonstrate that shifting from legacy rule-based heuristics to adaptive, privacy-preserving AI frameworks significantly compresses false-positive rates, bridges cross-jurisdictional regulatory disparities, and establishes a dynamic, mathematically rigorous paradigm for global financial integrity.
Abstract The boundary between traditional organized crime and cybercrime is eroding. Long-established criminal groups increasingly rely on encrypted communications, darknet markets, and cryptocurrency-based money laundering, while profit-driven cybercriminal groups adopt the durable structures, division of labor, and governance mechanisms long associated with organized crime. This article examines this convergence, understood as the organizational, operational, financial, and technological integration of traditional criminal groups and cybercriminal networks. The study combines a qualitative analysis of documents published between 2020 and 2026, including law enforcement reports, court records, and assessments by international organizations, with a case study of the Hive ransomware group and its disruption in 2023, complemented by supporting cases such as Conti, Hydra Market, EncroChat, and the online fraud compounds of Southeast Asia. Three vectors of convergence are identified: ransomware-as-a-service models and inter-group alliances; darknet marketplaces and the wider crime-as-a-service economy; and direct alliances between hackers and conventional criminal groups, including trafficking-based forced criminality. The article develops an integrative framework that links each vector to the organizational features it produces, to established criminological theories, and to corresponding enforcement levers. It concludes that convergence is a profit- and opportunity-driven adaptation to a weakly guarded digital environment and that effective responses require synchronized pressure on offenders, finances, infrastructure, and criminal service providers.
Cybercrimes that exploit virtual assetsâincluding laundering, concealment, and illicit financing through the dark webâare increasing rapidly, while existing tracking tools remain limited when offenders leverage multi-layer blockchain architectures and off-chain mechanisms to obscure fund flows. This paper proposes a practical full-node-based blockchain forensic framework for the automated detection and tracking of illicit virtual asset transactions across Layer-1 and Layer-2 environments. The framework operates a full-node network to construct a continuously updated database of all on-chain transactions, from which exchange-controlled internal addresses are identified using six formalized heuristics (H1âH6) expressed as a weighted-sum scoring model. A unified multi-layer transaction graph incorporates Layer-2 eventsâpayment channel closures, rollup batch submissions, and bridge deposits and withdrawalsâas contextual edge attributes correlated with Layer-1 settlement. Protocol-specific cross-layer correlation procedures, covering Arbitrum retryable tickets, Optimism cross-domain messages, zkSync Era batch commitments, and third-party bridge relays, were validated on live main-net transactions. Applying the framework to 7511 suspect wallet addresses, 821 (10.93%) were attributed to four Korean exchanges, and real laundering cases involving mixing and swappingâtogether with integrated real-time alerting and transaction-freeze request functionsâdemonstrate its direct applicability to law enforcement investigations. In addition, attribution reliability is quantified through the 98.80% labeling consistency observed across repeated independent collections of the same addresses, the standard forensic metrics are formally defined together with publicly released evaluation tooling, and the end-to-end detection latency is bounded analytically by the confirmation properties of the underlying protocols, substantiating the real-time capability of the framework.
Type of the article: Research ArticleAbstractThe blockchain financial system allows users to send money fast without any border restrictions. However, the same structure of the blockchain may be used as a means of laundering money. This paper assesses the relationship between the complexity of transaction networks and the likelihood of their illicit nature within the public Elliptic Bitcoin benchmark and examines whether anomaly detection using machine learning helps to interpret risks from an AML/CFT perspective. This empirical analysis assumes that Elliptic provides an anonymized transaction network in which nodes correspond to Bitcoin transactions, edges reflect directed transactions, and anonymized features facilitate licit/illicit classification of transactions. Furthermore, the dataset is not considered evidence of sender wallet addresses, receiver wallet addresses, transaction amount, timestamp, ownership of exchanges, user geography, and national AML/CFT effectiveness. Based on the labelled analytical dataset presented in the uploaded workbook (46,564 observations, including 42,019 licit transactions and 4,545 illicit transactions), a logit model found a significant positive correlation between illicit transactions and degree centrality (beta = 1.870, p < 0.001), clustering coefficient (beta = 0.940, p < 0.001), and flow entropy (beta = 0.680, p < 0.001). Isolation Forest and Autoencoder reached AUCs of 0.866 and 0.841, respectively. In turn, the coefficient measuring a countryâs regulatory capacity and its interaction term are not included in the estimation because there is no country-window marginal effect. Therefore, this paper does not test for the impact of regulatory capacity of the USA, Singapore, and UAE on transaction classification.
Automated anti-money laundering (AML) on public blockchains is usually framed as a detection problem. Because the ledger is public and permanent, automated detection is feasible, but that record shows only that value moved, without showing who moved it or why. We review automated blockchain anti-money laundering as a system in which machine models and human analysts share each decision, following it through four stages: detection, attribution, adjudication, and reporting, and asking at each stage what automation does well, what it must leave to human judgment, and what goes wrong when that judgment is misplaced. The evidence shows a consistent asymmetry. Machine learning is strong at pattern-finding over the permanent public record, where models rank suspicion and clustering heuristics scale, but it weakens sharply as the task turns from finding a pattern to assigning meaning, identity, intent, or accountability. Drawing first on emerging AML-specific studies and then, where direct evidence remains insufficient, on human-factors research from adjacent high-stakes domains, we find an uneven evidence base. Automation is best supported for large-scale detection, alert prioritization, and parts of blockchain attribution, while the evidence becomes more limited as decisions require contextual interpretation, evidential judgment, accountability, and reporting. AML-specific studies identify explainability, flexibility, tool integration, supervisory justification, and human review as important operational requirements, but they do not yet establish how frequently analysts over-rely on, reject, or selectively follow automated recommendations. Evidence from aviation, healthcare, and public administration is therefore used to identify plausible failure mechanisms rather than to claim AML-specific effects. The result is an evidence-weighted allocation matrix that records, for each stage, the strength of the case for automation, the function retained by the analyst, the dominant failure mode, and the empirical question that remains unresolved. Support is strongest for detection, direct but context-dependent for attribution, and more provisional for adjudication and reporting.
This article presents a novel, first-of-its-kind predictive RegTech solution to address this challenge using machine learning methods. The rapid global adoption of RealâTime Payment Systems (RTPS) has created a significant âvelocity gapâ in regulatory compliance. While expanding financial accessibility, these systems introduce new vulnerabilities into existing AML frameworks. Static, rule-based systems and batch processing architectures cannot effectively counter money laundering in sub-second transaction environments. This limitation enables sophisticated activities such as digital layering and smurfing that move illicit financial flows across networks faster than regulatory systems can react. The core of our solution involves the use of graph neural networks (GNNs). This approach enables real-time, pre-settlement risk assessment, preventing illicit transactions before execution. Unlike traditional AML systems that evaluate transactions in isolation, this framework analyzes the entire transaction network to detect coordinated illicit behavior in real time. GNNs capture complex structures such as loops, funnels, and bridges that indicate illicit activity. To support efficient implementation, the framework integrates Event-Driven Architecture (EDA). The proposed architecture introduces the concept of the Zero-Knowledge Proof (ZKP) protocol layer in order to make risk-sharing possible in a secure manner across multiple institutions. This allows the banks to cooperate with each other in order to combat financial crimes while maintaining their data sovereignty. With predictive graph analytics, event-driven integration, and private cooperation, the proposed architecture enables proactive compliance in real-time payment environments, including real-time payment systems such as FedNow, against high-speed financial crime.
One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transacted through the world financial system each and every year. The problem with this approach is that the criminals seldom use only one bank. They thread their way across five, ten, and sometimes dozens of institutions, all seeing merely a harmless nugget. In isolation, looking at his or her own transaction logs, no single bank will easily know that there is a problem. This paper is about a system, called AMLNet, which tackles this blind spot. Unlike the traditional approach, which would allow banks to share their customers' data with each other,AMLNet trains a detection model on customers' data within each bank, and shares only what the detection model learned from the data, not the data itself. All collaborative training is documented in a blockchain ledger, making it transparent and tamper-proof. With a Zero-Knowledge Proof, each bank is able to prove cryptographically that it is acting honestly, but not disclose anything private. A graph of transaction data (accounts as nodes, transfers as edges) is used to extract structural features, which are compressed by PCA before being input to a Multi-Layer Perceptron (MLP) risk-scoring classifier of each account. Together they increase fraud recall by approximately 20% over any single institution operating alone, while maintaining a low false positive rate, and that the overall computation time is less than 10 minutes on an average laptop.
With the rapid advancement of decentralized finance (DeFi), security incidents related to cryptocurrency have become increasingly prevalent. After such incidents, attackers typically attempt to rapidly move stolen assets, concealing the origin of illicit funds and ultimately converting them into fiat currency. However, existing anti-money laundering (AML) methods struggle to cope with the semantic complexity of DeFi transactions. They either rely heavily on low-level token transfers, or perform protocol-agnostic money flow analysis, failing to capture the high-level intent of transactions. In this paper, we propose AMLGuard, a semantic-aware AML framework for account-based blockchains. AMLGuard tracks illicit fund flows from known malicious addresses by performing semantic analysis on complex DeFi transactions, enabling accurate and continuous laundering tracking. Given a complex transaction, AMLGuard combines static rule-based analysis with retrieval-augmented large language model (LLM) reasoning to infer implicit DeFi semantics, transforming raw transaction data into high-level semantic representations. Furthermore, for cross-chain transactions where laundering intent is not explicitly exposed, AMLGuard parses transaction parameters and performs argument parsing to recover cross-chain semantics, enabling seamless tracking across ledgers. Based on the inferred semantics, AMLGuard abstracts each transaction into a DeFi Semantic Unit (DSU). We evaluate the effectiveness of AMLGuard on 82 real-world laundering cases, involving illicit assets worth over $1 billion. Specifically, AMLGuard reconstructs compact illicit fund-flow topologies with destination precision of 94.4% and 87.6%, while achieving the highest address recall of 98.4% and 95.8% and destination recall of 94.1% and 93.8% on single-chain and cross-chain datasets.
This chapter explores the role of blockchain and cryptocurrency forensics in investigating Darknet-enabled cybercrime. Cryptocurrencies such as Bitcoin and privacy-focused coins are widely used in Darknet marketplaces because they support pseudonymous transactions that complicate tracing and attribution. The chapter examines forensic techniques for blockchain analysis, including address clustering, transaction graph analysis, and heuristic-based tracing. It also explains how illicit financial flows are concealed through mixers, tumblers, and chain-hopping strategies. In addition, the chapter reviews analytics tools used by law enforcement and cybersecurity professionals to detect suspicious patterns and link wallets to entities. Challenges related to privacy-enhancing cryptocurrencies, blockchain scalability, and legal considerations are discussed. Finally, emerging threats involving decentralized finance (DeFi) and cross-chain transactions are explored to provide researchers, forensic analysts, and policymakers with insights into illicit financial activity in the Darknet ecosystem.
The rapid expansion of U.S. financial technology platforms has created new vectors for money laundering, terrorist financing and financial crime that traditional anti-money laundering frameworks were not designed to address. This article presents a systematic literature review of 78 peer-reviewed studies published between 2015 and 2025 to examine the design, performance and policy implications of advanced anti-money laundering frameworks for U.S. fintech platforms. This study draws on evidence from financial criminology, regulatory law, computer science and organizational studies; the review finds that machine learning-based transaction monitoring systems reduce false positive alert rates by 40 to 70 percent compared to rule-based systems, as well as improving detection of sophisticated layering schemes. Blockchain analytics tools partially de-anonymize cryptocurrency transaction flows and have been used to identify illicit financial activity on major blockchain networks. Regulatory technology platforms automate suspicious activity reporting, beneficial ownership identification and customer due diligence workflows in ways that reduce compliance costs as well as improve regulatory data quality. However, the reviewed literature also documents persistent challenges, including algorithmic disparate impact in AML monitoring systems, beneficial ownership opacity through shell company structures, regulatory arbitrage between licensed exchanges and decentralized finance protocols and the systemic underutilization of suspicious activity report intelligence by law enforcement agencies. The article concludes with six evidence-based policy recommendations and a research agenda for advancing AML framework effectiveness in the rapidly evolving U.S. fintech sector. Keywords: Anti-Money Laundering, Fintech, AML Compliance, Machine Learning, Transaction Monitoring, Know Your Customer, Cryptocurrency Regulation, Regulatory Technology, Suspicious Activity Reporting, Financial Crime.
Abstract The same infrastructures that enable decentralised finance, NFT markets, and metaverse platforms also create new spaces for paraâcrime. This article extends grey criminology to Web3 by applying three mechanisms of infrastructural illegality â parasitism, normative greyness, and platform coâproduction â first developed for physical crossâborder grey economies (daigou). Drawing on technical and financial crime literature, we show how smart contracts, stablecoins, and DAO governance are parasitised for money laundering and fraud; how technoâlibertarian narratives of 'code is law' and decentralisation sustain normative greyness; and how algorithmic security and DAO coâproduction reshape rather than eliminate paraâcrime. The analysis reveals both structural parallels with physical grey economies and domainâspecific variations â most notably, the deeper internalisation of coâproduction in codeâbased systems. We argue that grey criminology must extend its infrastructural turn to virtual and metaversal spaces, and that enforcement paradoxes â where suppression threatens valued infrastructures â apply as much to blockchain protocols as to customs thresholds.
El presente artĂculo analiza la tensiĂłn estructural entre los marcos regulatorios de prevenciĂłn del lavado de activos âAnti-Money Laundering (AML)â y la emergencia de las organizaciones autĂłnomas descentralizadas âdecentralized autonomous organizations (DAOs)â. A partir de una analogĂa con el relato La loterĂa de Babilonia, de Jorge Luis Borges, el texto examina cĂłmo la arquitectura contemporĂĄnea de cumplimiento ha mutado en un sistema de azar burocrĂĄtico que erosiona el principio de lesividad y abstrae excesivamente los bienes jurĂdicos en el derecho penal econĂłmico. Mediante un enfoque interdisciplinar que integra la teorĂa de juegos, la praxeologĂa de la escuela austriaca y la dogmĂĄtica penal garantista, el estudio sostiene que el rĂŠgimen AML opera como una externalidad negativa y como un factor de exclusiĂłn financiera, especialmente en contextos de alta informalidad, como el colombiano. El anĂĄlisis concluye que la gobernanza algorĂtmica de las DAO, fundamentada en la transparencia del cĂłdigo y en la responsabilidad individual directa, puede ofrecer un modelo alternativo de eficiencia para la integridad financiera. Frente a la capitis deminutio derivada de la intermediaciĂłn centralizada, se propone la descentralizaciĂłn como un paradigma orientado a restituir la soberanĂa econĂłmica del individuo, sustituyendo la opacidad administrativa por la certeza de la lex cryptographica.
Bitcoin (BTC) wealth distribution is often studied with macro indicators like wallet balances, prices, network activity, fees, and hashrate. This letter proposes a "Crypto-Microeconomic Observability Framework" to examine micro-level Bitcoin wealth disparities across five labeled agent classes: Service, Abuse, Malware, Individuals, and Benign. Using descriptive, inequality, and longitudinal concentration metrics, we show that Bitcoin wealth is highly concentrated across major classes, consistent with a persistent "Whale-Effect". Service entities hold the largest share of observed BTC (75.15%), while Abuse controls a disproportionately large share relative to its entity count (24.26% of BTC vs. 3.53% of entities). Individuals, Abuse, and Service show near-maximal within-class inequality (e.g., Gini = 0.9993 for Individuals), and time-series analysis indicates these patterns persist. Overall, Bitcoin wealth among labeled economic agents remains structurally uneven and concentrated in a small subset of entities.
Purpose This paper aims to compare Ghanaâs Virtual Asset Service Providers Act, 2025 (Act 1154), with the USAâs anti-money laundering (AML) framework for virtual assets. It asks whether a unified statute can give an emerging economy advantages over a fragmented, path-dependent regime. Design/methodology/approach The study uses functional and institutional comparative legal analysis. It reviews statutes, supervisory notices, sandbox materials and enforcement documents through a six-dimensional matrix mapped to Financial Action Task Force Recommendations 10, 12, 15, 16, 20, 26, 27 and 35. Findings Ghanaâs Act offers statutory coherence, and early implementation steps show movement beyond a purely prospective regime. However, enforcement capacity for virtual asset service providers (VASPs) is still developing. The US framework is institutionally fragmented yet operationally mature. Ghanaâs licensing model more closely resembles a banking charter than a money services business (MSB) registration, increasing demands on supervisory expertise, verification systems and technical infrastructure. Both frameworks also leave gaps around decentralized finance. Research limitations/implications Implementing regulations remain incomplete and Ghana does not yet have a mature enforcement record specific to VASPs. The analysis, therefore, combines legal design with early operational evidence rather than a full account of law in action. Practical implications Emerging-economy regulators need more than statutory clarity; they need credible supervisory capacity. VASPs in Ghana should expect operational requirements to evolve as implementation matures. Originality/value The paper offers an early comparative analysis of Ghanaâs Act and contributes to debates on regulatory leapfrogging, implementation gaps and compliance capacity in the Global South.
Since the first implementation of a blockchain with Bitcoin in 2009, cryptoassets created and transacted using blockchain technologies have grown and diversified significantly. Because regulatory regimes, which govern cryptoassets, do not have global coverage, criminal actors find opportunities to commit cryptoasset fraud. While it can be difficult to distinguish between cryptoassets that are honest but high risk and cryptoassets that are outright fraudulent, investors seeking significant returns frequently invest in unregulated cryptoassets, namely cryptocurrencies and non-fungible tokens (NFTs). This study provides a crime script analysis to examine the chronological and functional steps offenders use to execute cryptoasset fraud. It considers three types of crypto asset fraud and how they have functioned over time: Ponzi schemes, cryptoasset exit scams, such as cryptocurrency ârug pulls,â and NFT âmint-and-runâ schemes, where invested value is stolen from a crypto asset project. By outlining the fundamental crime script of cryptoasset fraud, this study considers the implications for regulators. Of note, this study shows that while the stages of cryptoasset frauds are consistent, the speed at which frauds are executed has, on average, increased significantly. This rapidity of execution provides enduring challenges to regulators, who often cannot respond quickly. This challenge must be considered if regulation is to be effective.
Terrorist financing is a major fuel for political violence and extremist operations in the world. The increasing advancement of digital technologies, cryptocurrencies, crowdfunding platforms, and social media has significantly transformed how extremist groups raise, transfer, and conceal funds, yet little has been done to examine the broader digital transformation of terrorist financing and its implications for the United States. This scoping review examined evolving trends, financing strategies, and operational challenges associated with terrorist financing networks and assessed their implications for the United States counterterrorism agenda. Following the Arksey and OâMalley guidelines for scoping reviews, relevant studies were identified through systematic database searches and screened using predefined inclusion and exclusion criteria. Ten studies were selected and analyzed thematically. The findings underscored four major themes: the digitalization of terrorist financing, the fusion of lawful and unlawful funding mediums, administrative and institutional weaknesses, and the growing need for collaborative and intelligence-driven disruption strategies. The review found that extremist financing increasingly operates within ordinary digital and financial ecosystems, making detection more difficult for regulators, financial institutions, and law enforcement agencies. The study concludes that terrorist financing has become more decentralized, adaptive, and technologically sophisticated than many existing counterterrorism frameworks are prepared to address. Hence, strengthening United States national security will require a more proactive digital financial monitoring, sophisticated regulatory systems, and stronger inter-sectoral collaborations to disrupt evolving extremist financing networks before they escalate into acts of violence.
Open access
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Terrorism, Counterterrorism, and Political Violence
Objective: This study examines the legal and procedural challenges posed by decentralised finance (DeFi) technologies to the anti-money laundering framework in Iraq, The research problem lies in the clear regulatory gap resulting from the decentralised nature of these platforms, which relies on smart contract technology and blockchain to eliminate the need for traditional financial intermediaries; this decentralised nature hinders the ability of Iraqâs Anti-Money Laundering and Counter-Terrorist Financing Law No. 39 of 2015 to control cryptocurrency flows and establish criminal liability in this context,Ř Method: The study adopted a comparative analytical approach, analysing the text of Iraqi legislation and comparing it with the operating mechanisms of decentralised finance platforms, whilst also examining the extent to which it complies with the updated international standards issued by the Financial Action Task Force (FATF) In particular, with regard to Recommendation No. 15, Results: the study reached a number of important conclusions, the most notable of which is that the current legal definitions of funds and financial institutions in Iraq are outdated, thereby limiting the ability of regulatory bodies to track virtual assets, Novelty: The study also identified procedural shortcomings in the handling of encrypted digital evidence and recommended urgent legislative reforms, including the regulation and oversight of Virtual Asset Service Providers (VASPs) through the establishment of a dedicated institutional framework.
Kabilesh C M, Dr. B. Raja, Dr. S. Geetha, Dr. V. Cyrilraj
As decentralized finance (DeFi) continues to scale, traditional forensic methodologies often fail due to their retrospective, "post-mortem" nature, analyzing illicit activities only after they are permanently recorded on the ledger. This project proposes coinEth, a real-time institutional blockchain surveillance and autonomous defense system designed for the Ethereum Sepolia network. The framework operates across a four-layer architecture: a Data Acquisition Layer that intercepts pending transactions via Alchemy WebSockets (WSS); a Persistence and Forensic Engine that utilizes SQLite and Python-based heuristics to detect suspicious behavioral patterns such as "structuring" and "high velocity"; a Governance Layer that executes an autonomous enforcement loop via a Solidity-based "Gatekeeper" smart contract; and a Visualization Layer built with Streamlit and PyVis. By assigning dynamic risk scoresâcategorized as Safe (Level 0), Warning (Level 1), and Frozen (Level 2)âthe system can automatically broadcast on-chain transactions to freeze illicit accounts before fund exfiltration occurs. Furthermore, coinEth reconstructs a chronological "money trail" through sequential path mapping (T0 â T1 â T2...), ensuring a verifiable digital chain of custody for investigative reporting. This proactive approach shifts blockchain security from passive observation to active, real-time intervention, significantly enhancing the defense mechanisms available to institutional stakeholders.
Olha Kovalchuk, Ruslan Shevchuk, Serhiy Banakh, N. P. Holota ¡ 6 authors
Abstract This study examines the relationships between national cryptocurrency regulation, anti-money laundering (AML) risks, and decentralized finance (DeFi) adoption across global jurisdictions. Using correspondence analysis, correlation techniques, and regression modeling with control variables, we analyze data from the Basel AML Index and Retail DeFi Rankings to identify structural patterns in the interaction between regulatory frameworks, institutional quality, and digital asset ecosystems. The results reveal a counterintuitive global distribution in which advanced economies with strong regulatory regimes and low AML risks tend to exhibit limited retail DeFi activity, whereas jurisdictions characterized by weaker institutions and higher money laundering risks show significantly higher levels of DeFi usage. Further, the correspondence analysis identifies three distinct clusters of countries defined by specific configurations of regulatory approaches, AML effectiveness, and DeFi adoption, indicating that these relationships are configurational rather than purely linear. Robustness checks demonstrate that qualitative features of regulatory regimes are more strongly associated with DeFi adoption than conventional quantitative indicators of economic development or governance quality, thereby distinguishing DeFi diffusion from broader cryptocurrency usage dynamics. Mediation analysis provides partial support for a compensatory pattern: financial inclusion is a significant negative predictor of DeFi adoption, though a statistically confirmed mediation pathway between AML risk and DeFi activity through financial exclusion was not established. The study also highlights substantial global regulatory fragmentation, with 57% of jurisdictions classified as âUndecidedâ or âImproving,â underscoring the ongoing difficulty of reconciling financial innovation with stability and risk mitigation. These findings provide evidence-based guidance for policymakers designing adaptive regulatory frameworks and establish a foundation for further research on the evolution of digital finance regulation.
Abstract Crypto research highlights the anarcho-libertarian feature of the crypto movement, characterizing the industry as a radical departure from governmental and corporate influence. This study investigates the characteristics of the US crypto elite, highlighting how the demographic structure and the institutional connections of the industry resemble those of the established financial elite. Drawing on original data from board members of top US crypto and financial firms, Fortune 1000 companies, and major policy-planning organizations, it develops three comparisons. Results show that crypto elites share similar socio-educational traits with traditional finance leaders and engage with powerful corporate and political affiliations, despite being more peripheral in corporate networksâlikely due to the sectorâs recent emergence. Notably, the White House appears in the career paths of both groups, suggesting a shared pattern of revolving door integration into state structures. Rather than a structural transformation, crypto appears to align with the broader elite configuration of the US political economy.
By blurring the boundary between art and financial assets, non-fungible tokens (NFTs) have created regulatory ambiguity and criminogenic vulnerabilities in digital markets. The article applies the Howey Test to an original dataset of NFT-related cases that involve financial crime. As it turns out, functionally NFTs often resemble securities: they are characterized by information asymmetries, speculative dynamics, and weak oversight. These structural gaps make NFTs attractive to facilitate fraud, money laundering, wash trading, and nefarious exploitation on decentralized platforms and incidentally by way of traditional auction houses. NFTs highlight systemic limitations of analog regulatory frameworks to contain criminogenic risk posed by virtual assets. To enhance transparency, accountability, and consumer protection in evolving digital economies, the article concludes on a paradigm shift toward an adaptive, outcomes-based regulatory approach.
Abstract The rapid digitalization of financial systems has significantly transformed the landscape of anti-money laundering (AML) frameworks, reshaping both the opportunities for financial innovation and the risks associated with illicit financial flows. While technological advancements such as cryptocurrencies, artificial intelligence (AI), blockchain technology, and fintech innovations have enhanced operational efficiency, transaction speed, and financial inclusion, they have simultaneously introduced complex vulnerabilities that can be exploited for money laundering and related financial crimes. These developments challenge the adequacy of traditional AML mechanisms, which were primarily designed for centralized and institution-based financial systems. This paper critically examines the evolution of AML frameworks, tracing their development from rule-based and compliance-driven approaches to more dynamic, risk-based, and technology-enabled systems. It explores how digital transformation has altered the typologies of money laundering, enabling increasingly sophisticated methods such as the use of decentralized finance (DeFi), mixing services, and cross-platform transactions that obscure financial trails. The study further analyzes key challenges arising in the digital era, including the pseudonymity and anonymity of digital assets, the speed and scale of cross-border transactions, regulatory fragmentation across jurisdictions, and limitations in data integration and information sharing. Additionally, it highlights the growing tension between effective AML enforcement and the protection of individual privacy and data rights. A central focus of the paper is the identification of critical regulatory gaps, particularly in the governance of digital assets, the lack of harmonized international standards, insufficient oversight of emerging financial technologies, and weaknesses in beneficial ownership transparency. These gaps reduce the effectiveness of AML regimes and create opportunities for regulatory arbitrage. The paper concludes by proposing a set of policy recommendations aimed at strengthening global AML compliance. These include enhancing international coordination, adopting advanced technological tools for monitoring and detection, developing comprehensive regulatory frameworks for digital assets, and promoting a balanced approach that safeguards both financial integrity and individual rights. Ultimately, the study argues that adaptive, technology-driven, and globally coordinated AML strategies are essential to effectively combat money laundering in the evolving digital financial ecosystem.