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.
In cryptocurrency markets, credibility may be created before it can be verified. Promotional signals from influencers, online communities, exchanges, and project actors can create an impression of legitimacy before consumers are able to independently assess the quality, risks, or underlying value of the asset. This systematic review examines how promotional activity contributes to this condition and how the literature connects it with ethical concerns, consumer consequences, and governance. Following PRISMA 2020, 54 empirical studies published between 2019 and 2025 were identified through Scopus and analyzed using thematic synthesis. The findings indicate that promotion is distributed across social media, influencers, communities, exchanges, and project actors, making the boundary between marketing, personal opinion, and financial advice difficult to maintain. Ethical problems arise not only from fraud, but also from selective information, hidden incentives, artificial attention, and market signals that give uncertain assets an appearance of legitimacy. Consumer vulnerability is similarly situational. Knowledge and experience may offer some protection, but trust, technological complexity, social influence, and speculative expectations continue to shape judgement. Regulatory and educational responses remain fragmented because responsibility is dispersed across actors and jurisdictions. The review connects these relationships through an ethical marketing cycle that conceptually organizes how ethical concerns may develop across communication, market activity, consumer interpretation, and governance. This perspective extends ethical marketing beyond the accuracy of individual claims and locates responsibility within the structures through which cryptocurrency credibility is produced. The review is limited by its reliance on Scopus and the absence of a formal risk-of-bias assessment of the included studies.
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.
Cybercrime investigation in India has entered a new legal phase following the commencement of the Bharatiya Nyaya Sanhita, 2023 (BNS), the Bharatiya Nagarik Suraksha Sanhita, 2023 (BNSS), and the Bharatiya Sakshya Adhiniyam, 2023 (BSA). These enactments operate alongside the Information Technology Act 2000, the Digital Personal Data Protection Act 2023 (DPDP Act), sectoral regulation and specialised cybercrime institutions. This article argues that the principal weakness of the present framework is not a lack of offences, but fragmentation across legal classification, investigative procedure, digital-evidence rules, forensic practice, privacy governance and institutional coordination. Using doctrinal legal research supplemented by official policy and institutional material, the article develops an investigation-chain framework linking complaint triage, offence classification, preservation, lawful acquisition, forensic examination, attribution, financial tracing, cross-border evidence, prosecution and adjudication. It evaluates the continuing interaction between general criminal liability under the BNS and technology-specific provisions of the Information Technology Act, while examining the evidentiary significance of electronic records under the BSA. Particular attention is given to cloud evidence, cryptocurrency, ransomware, artificial intelligence and deepfakes. The article contends that technological traceability must not be equated with human attribution and that evidentiary reliability depends on the entire acquisition-to-trial chain. It proposes harmonised investigative protocols, accredited forensic capacity, specialist prosecution and judicial training, auditable access to personal data, improved cross-border preservation mechanisms, and a human-verification requirement for significant AI-assisted investigative outputs. The analysis contributes an India-specific but internationally relevant model for assessing whether contemporary cybercrime law can produce reliable, rights-compliant and trial-ready investigations.
Cryptocurrency began as a marginal experiment among cypherpunk technologists and has since become an asset class worth trillions of dollars globally, and its rise has been matched by a parallel rise in criminal activity that feeds off the pseudonymity, decentralisation and borderless movement these assets allow. India counts among the countries with the largest populations of crypto holders anywhere, yet Parliament has still not enacted any statute dedicated to virtual digital assets; oversight instead comes piecemeal, through tax provisions, anti-money-laundering notifications and rulings handed down case by case. This article sets out the scale of crypto-related crime worldwide, reviews the current statistics, and examines the jurisdictional and enforcement difficulties that follow from them, with particular attention to how those difficulties play out inside India. India's liability framework is then measured against a handful of foreign models, the Indian judiciary's engagement with the resulting legal gaps is assessed against international standards and model policy approaches, and the article closes by setting out a proposed regulatory framework.
Through this independent concept, the study introduces a fresh new perspective to the world of modern forensic accounting via a theory called âThe Decentralized Fraud Matrixâ (DFM). This conceptual research was developed specifically as an analytical tool to dissect the modus operandi of financial crimes in the digital-cyber eraâincluding Web3 environments, blockchain architecture, DeFi protocols, and autonomous DAO systems. The focus of the DFM theory completely breaks away from the basic assumptions of the conventional fraud triangle, which has long been overly preoccupied with measuring human emotions. Mechanically, the originality of this theory rests on the testing of three interlocking cyber indicators in the field. These three indicators include the level of opacity in an actorâs digital identity concealment; technological engineering designed to break the audit trail of fund flows; and the exploitation of loopholes in physical national sovereignty boundaries, as well as cyber âjurisdictional evasionâ tactics aimed at neutralizing the enforcement power of on-ground regulations, thereby rendering perpetrators immune to formal legal prosecution
Abstract Digital financial fraud and the financial-literacy defences meant to counter it have become a fast-growing, cross-disciplinary research concern, yet the conceptual and intellectual structure of this combined field has not been systematically mapped. This study presents a bibliometric analysis of research at the intersection of digital fraud susceptibility and financial literacy, using metadata retrieved from the open bibliographic database OpenAlex and analysed with VOSviewer. A dataset of 523 documents published between 2015 and 2026 was examined through publication-trend analysis, citation and source analysis, co-authorship analysis, term co-occurrence mapping, and bibliographic coupling. Annual output grew steeply, with roughly 60% of the corpus appearing in 2023â2025, indicating a young and rapidly expanding field. Co-occurrence mapping of 176 concepts produced six substantive thematic clusters: accounting, auditing and forensic controls; financial literacy and consumer psychology; artificial-intelligence and machine-learning fraud detection; digital payments, fintech and cyber-enabled fraud; law, regulation and consumer protection; and information security, privacy and identity. The digital-payments and fintech cluster was the most recent on average, and fintech, digital literacy, financial inclusion and blockchain surfaced as research fronts. Collaboration was highly fragmented, with no large connected co-authorship component, and direct-citation linkage within the corpus was sparse, although bibliographic coupling revealed greater thematic cohesion. The analysis charts the contours of an emerging field and identifies under-integrated areasâparticularly the gap between behavioural financial-literacy research and technical fraud-detection researchâthat warrant coordinated attention. Limitations relating to the OpenAlex concept classifier and single-database coverage are discussed.
Abstract - The rise of digital technology has led to an increase in cybercrime. This has made the management of digital forensic evidence more complicated. Traditional evidence management systems utilize manual methods and centralized databases. Methods like these are vulnerable to data tampering, unauthorized access, and human error. These issues threaten the integrity of the evidence and the chain of custody during the investigation process. In this paper, we introduce a system that utilizes blockchain technology, smart contracts, and a decentralized system for the tracking of forensic evidence. Security and transparency will be guaranteed. In our system, evidence records are stored as ERC-721 Non-Fungible Tokens. A private Ethereum blockchain was developed using Ganache and combined with wallet-based authentication and Role-Based Access Control to ensure that only authorized personnel have the ability to view and manage evidence. Smart contracts facilitate the registration, verification, transfer, and auditing of evidence, thus, considerably reducing the manual work and greatly increasing the trustworthiness of the system. We proposed a hybrid system of storage whereby evidence and its forensic files are stored off chain, and the evidence metadata and its forensic files are stored on chain. This paper presents the design and architecture of the system,implementation and evaluation are in progress.Our system will be a trusted, efficient, and effective system of evidence management.