Global Anti-Money Laundering and Combating Terrorism Financing Regulatory Framework: A Critique
Bibliographic record
Abstract
Money launderers prefer to use financial services as the ideal medium for laundering. This study aimed to provide an overview of the global AML/CFT regulations, application and how they should evolve in this dynamic environment. To gather more insight, a qualitative study was undertaken with relevant documents analysed. The main finding was that country implementation of the global AML/CFT regulations differed due to political and economic factors, amongst others. While the various AML/CFT enforcements done by sampled countries were mainly cease and desist orders and monetary penalties that were publicised, the drawbacks of global AML/CFT regulations centred on the application of these regulations and emerging trends. These include, among other definitions of money laundering, reference to the three stages of money laundering, the link between penalty and violations, technological innovations and regulation paradigm shift, cyber-attacks, and data privacy. This study contributes to the application and growing body of knowledge in that the advent of technology has resulted in better consumer experiences, new payment platforms, products and services. However, these innovations have broadened emerging money laundering risks and risks to the financial system in general. Hence, there is a need to conduct research-based FATF recommendations, as risk is dynamic and not static.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".