Beyond Banks: A Case for Interagency Collaboration to Combat Trade-based Money Laundering in Africa
Bibliographic record
Abstract
Abstract Regulation regarding trade-based money laundering (TBML) has focused mainly on documentary trade financing arrangements, which are bank intermediated. Yet, African countries predominantly employ alternative forms of trade financing models that span beyond banks’ usual purview. These alternative models are supported by many actors across the supply chain that are not holistically supervised given the fragmented regulatory framework at the global and domestic levels. In contending that TBML significantly undermines intra-African trade and therefore amounts to a non-tariff barrier (NTB) to trade, this article challenges the need for globally transplanted solutions to address TBML. Rather, it argues for the introduction of new approach: a country focused experimental legislation that facilitates inter-agency collaboration beyond banks. This approach would ensure a homegrown, responsive, and legitimate framework that encompasses currently un-supervised actors. It contends that if the experimental legislation works at a country level, it may then be cascaded to the African Union level for contextual adaptability across other African countries.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".