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Foreign Experience in the Legal Regulation of Virtual Assets in the Field of Anti-Money Laundering and Countering the

2024· article· W7165855241 on OpenAlex
Volodymyr Nohin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueActual problems of improving of current legislation of Ukraine · 2024
Typearticle
Language
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMoney launderingLegislationStatuteDeterrence theoryPrincipal (computer security)HarmonizationService providerField (mathematics)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.330
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it