The role and importance of financial intelligence in identifying and tracing criminal assets
Bibliographic record
Abstract
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.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".