Foreign Experience in the Legal Regulation of Virtual Assets in the Field of Anti-Money Laundering and Countering the
Bibliographic record
Abstract
The article provides a comparative legal analysis of the experience of selected foreign states (outside the European Union) in the legal regulation of virtual assets in the field of anti-money laundering and countering the financing of terrorism, and determines its significance for the criminal-law policy of Ukraine. The empirical basis is a consolidated review of the legislation of 25 jurisdictions; the study employs comparative-legal, systemic-structural and functional methods, grouping states by legal families and regions. It is established that in all the states examined the anti-money laundering regime, built upon the standards of FATF Recommendation 15, constitutes the foundational framework for the legal protection of virtual assets, while national models differ primarily in the intensity of supervision. A spectrum of authorisation regimes for service providers is identified - from registration (the United Kingdom, Norway, the United States, Canada), through licensing with quasi-prudential requirements (Japan), to a dedicated statute with a phased transition from registration to licensing and a regulatory sandbox (the Cayman Islands), and a self-regulatory-organisation model complemented by infrastructure licensing (Switzerland). It is argued that the best models combine legal precision with the technological neutrality of definitions and universal, exemption-free anti-money laundering coverage. Particular attention is paid to the interaction between the anti-money laundering regime and criminal law: most states rely on general criminal-law provisions combined with regulation, whereas the United States relies on robust special provisions and extraterritorial enforcement. The principal conclusion for Ukraine’s criminal-law policy is formulated: criminal-law measures should be applied as a last resort (ultima ratio) within a coordinated regulatory and preventive mechanism. Drawing on cautionary lessons (regulatory uncertainty in India, inter-agency conflict in Nigeria), the need for legal certainty and a clear allocation of powers between supervisory authorities is substantiated. Priority directions for improving national legislation are identified, taking into account the non-entry into force of the dedicated law and the trajectory of harmonisation with EU law.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".