Prosecuting money laundering the FATF way : an analysis of gaps and challenges in South African legislation from a comparative perspective
Bibliographic record
Abstract
South Africa is a member of the Financial Action Task Force (FATF), an intergovernmental body which is responsible for setting international standards to combat 'money laundering' and countering the financing of terrorism. In addition, the FATF has developed structures and mechanisms to ensure that these money laundering standards are effectively complied with by its members and non-member countries. Compliance is enforced by conducting evaluations of domestic legal systems and assessing the extent to which these systems are effectively implemented. A country is required to comply with these standards both in criminalising money laundering as well as applying proactive measures to prevent money from been laundered into the financial systems and to punish those who have benefited from such laundering. In 2009, the FATF has conducted a third round of evaluation in South Africa and identified specific gaps that require attention. Against this background, this article aims to analyse gaps identified by the FATF and certain challenges that prosecutors face in their prosecutions of money laundering. This is done in comparison with similar systems in the United States of America and Canada. Systems in these particular countries are relevant for this article because both countries were rated highly by the FATF with regards to their prosecution of money laundering offences. With its fourth round of evaluations around the corner, the question is whether South Africa has improved in its prosecution of money laundering and whether those gaps and challenges in question have been addressed as required by the FATF. This article is not intended to statistically evaluate any improvement in the number of money laundering cases that were brought before the court since the FATF's last evaluation. The aim of this article is to analyse these gaps and recommend few initiatives on how they can be addressed and be overcome.
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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 | 0.000 | 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 | 0.000 |
| 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".