International experience in conducting financial investigations as positive practice for Ukraine
Bibliographic record
Abstract
This paper deals with the essence of the concept of «financial investigation». The key tasks of a financial investigation are highlighted. Thus, a financial investigation includes collection, comparison and analysis of all available information in order to facilitate criminal prosecution and deprive criminals of their income and means of committing an offense. The principles of financial investigation are defined. Organizational models for the distribution of powers between fiscal and regulatory authorities to combat financial crimes in different countries are considered. Attention is paid to the experience of such countries as: Italy, the USA, the UK, Denmark, Norway, Ireland, the Netherlands, Portugal, Germany, Switzerland, and Canada. The author emphasizes that, in the light of the analysis of international experience in conducting financial investigations, we can conclude that this practice has significant potential for Ukraine in the context of combating economic crime and ensuring financial stability. International experience provides valuable tools and methods for effective investigation of economic crimes, use of advanced technologies and international cooperation. However, it is important to take into account the specifics of the Ukrainian situation and adapt foreign experience to the domestic needs and realities of the country. The application of international experience in the field of financial investigations can be an important step towards improving Ukraine's financial security and strengthening the rule of 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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".