MétaCan
Menu
Back to cohort
Record W4402332392 · doi:10.37634/efp.2024.6.6

The role and importance of financial intelligence in identifying and tracing criminal assets

2024· article· en· W4402332392 on OpenAlex
Roman Rudyi

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

VenueEconomics Finances Law · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsTracingBusinessComputer science

Abstract

fetched live from OpenAlex

The paper examines the basic principles of improving efficiency and providing law enforcement officers, as well as authorized bodies, in whose management the seized property is transferred, with instructions on the legal grounds, procedural and tactical features of detection, search and seizure of property. It was determined that financial intelligence is a complex system aimed at detecting operations related to the legalization of proceeds obtained through crime. This system can act as a component of the pre-trial investigation in criminal proceedings related to the research. Asset search activities should include elements of economic and monetary intelligence. It has been established that in Ukraine, the State Financial Monitoring Service, subordinate to the Ministry of Finance of Ukraine, is entrusted with the functions of financial intelligence, which implements the state policy in the field of prevention and countermeasures against the legalization (laundering) of proceeds obtained through crime, the financing of terrorism, and the proliferation of weapons of mass destruction. It is argued that financial intelligence is not a function of the National Agency of Ukraine for detection, search and management of assets obtained from corruption and other crimes. In turn, the DSFM collects, processes and analyzes information on financial transactions subject to mandatory financial monitoring and other transactions related to money laundering. It was investigated that the bodies of foreign countries are similar: Financial Crimes Enforcement Network (FinCEN) - the US financial intelligence agency; Financial Transactions and Reports Analysis Center (FINTRAC), Canada's financial intelligence agency; Zentralstelle für Verdachtsanzeigen – German financial intelligence agency; TRACFIN – financial intelligence unit of the French Republic; The Inland Revenue Service NCIS/ECU is the UK's financial intelligence agency. Having analyzed the relevant legislation of ARMA and the State Financial Monitoring Service, we come to the conclusion that the terms "asset discovery" and "asset search" are explicitly defined at the legislative level only for ARMA. The profile legislation of the State Financial Monitoring Service does not reflect or explain the essence of these terms, however, in the course of implementing measures to prevent and counter the legalization (laundering) of proceeds obtained through crime, the State Financial Monitoring Service conducts financial investigations with the aim of finding information about the assets of questionable origin.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.024
GPT teacher head0.293
Teacher spread0.269 · 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