The variability and clustering of Financial Intelligence Units (FIUs) – A comparative analysis of national models of FIUs in selected western and eastern (post-Soviet) countries
Bibliographic record
Abstract
Financial intelligence units (FIUs) are crucial pillars of international anti-money laundering (AML) law as national authorities that gather, analyze, and disseminate financial intelligence to law-enforcement authorities (LEAs). This paper offers an in-depth insight into national models of financial intelligence units (FIUs) in ten selected Western (Canada, Denmark, Netherlands, Luxembourg, United States) and Eastern (post-Soviet) countries (Estonia, Latvia, Lithuania, Poland, Ukraine). A hypothesis that FIUs’ characteristics cluster along the West-East dimension is tested, based on a development of an analytical framework involving twelve potentially discriminative properties of FIUs, that characterize how a given FIU falls on a hypothetical continuum from primarily administrative to primarily law-enforcement organization and modi operandi. The findings are that, despite several decades of legislative efforts to harmonize FIUs worldwide, they are not homogenous. In fact, a West-East dichotomy between FIUs styles can be seen to the extent that Lithuania, Estonia and Poland form one cluster. On the other hand, Denmark, Netherlands and Luxembourg form another cluster. The exception that proves the rule, in this case, is Latvia, that falls within the Western cluster, not the Eastern one, due to a recent restructuring along “Western” lines. This analysis contributes to the knowledge-gap about FIUs governance in two ways. It provides a new theoretical framework of FIUs governance that hinges upon the “input-output” dynamics among FIUs, AML supervisory bodies, and law-enforcement authorities (LEAs). Furthermore, it demonstrates that blindly issuing new European AML legal standards, without regard for the idiosyncratic nature of these national bodies, is unlikely to produce substantive further harmonization results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".