Is there a commendable regime for combatting money laundering in international business transactions?
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.
Bibliographic record
Abstract
Purpose Money laundering and grand business corruption continue to plague the global economy, accounting for 2%-5% of the global gross domestic product. Illicit funds, produced through grand corruption, are laundered using complex layering schemes that cloak them in legitimacy by concealing their origins. Lamentably, weak anti-money laundering (AML) frameworks promote economic instability, unjust commercial advantages and organized crimes. This study aims to highlight the need for comprehensive anti-corruption and AML frameworks by critiquing the exploitable gaps in the global AML regime created by heterogeneous state-level AML regimes to date. Design/methodology/approach This study welcomes the United Nations Convention against Corruption (UNCAC) and the financial action task force (FATF) recommendations but underscores the limitations of their effectiveness by investigating state-level enforcement mechanisms to determine these instruments’ true impact or lack thereof. The mutual evaluation reports (MERs) and state-level AML regimes in the UK, the USA and Canada are analyzed to illustrate the distinct implementation of international soft law in domestic legislation. Findings This study finds that UNCAC and the FATF recommendations are pivotal steps towards the establishment of a global AML regime for international business, albeit, one that remains imperfect because of the inconsistency of state-level AML frameworks. Consequently, international cooperation is needed to navigate and improve the discrepancies in varied AML legislation. Originality/value The author provides an in-depth and balanced analysis of current state-level AML developments and relies upon the recent 2016-2018 MERs to indicate the successes and flaws of various AML legislation. Therefore, this critique may guide stakeholders to construct robust AML frameworks and contributes to academic research in AML.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it