Russia's anti‐money laundering regime: law enforcement tool or instrument of domestic control?
Bibliographic record
Abstract
Purpose The purpose of this paper is to cut through the rhetoric that shrouds Russia's anti‐money laundering regime to uncover the reality that lies beneath. Design/methodology/approach This paper relies on both primary and secondary sources in Russian and English that deal with the problems of money laundering in the Russian context. Relevant sections of the Russian Criminal Code as well as Russia's anti‐money laundering regulations have been consulted. Findings Overall, the Russian anti‐money laundering regime has thus far proved ineffective in terms of meeting its stated purposes of combating organized crime and terrorism. Its limited success stems largely from structural weaknesses in the Russian banking system as well as that industry's lack of a culture of regulatory compliance. Moreover, Russian authorities have opportunistically seized on the current anti‐money laundering regime as a useful tool in the pursuit of ends unconnected to the fight against organized crime and terrorism. The Russian authorities have used the regime to attempt to reform the banking system and to extend their strategic control in the domestic political and business realms. The ineffectiveness of the anti‐money laundering regulations and their usage to achieve ulterior aims undermine the legitimacy of the regime as a whole. Originality/value The paper looks beyond the technical difficulties in applying the anti‐money laundering regulations and examines the misuses of the anti‐money laundering regime in the Russian context. However, the problems raised in the paper are not unique to Russia and have relevance to other jurisdictions, especially countries that are members of the Financial Action Task Force.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".