A critical analysis of Somalia’s current antimoney laundering and counter financing of terrorism regime: a comparative study with Malaysia
Bibliographic record
Abstract
Purpose Concerns on money laundering (ML) and terrorist financing increased, as ML accounted 2%–5% of the global GDP, with Switzerland, the USA, Canada, India and Russia having high laundering rates. Banks were fined over US$320bn in 2008, but money laundering still accounted for 3.6% of global GDP in 2009, thereby indicating the need for effective regimes. Therefore, this study aims to critically analyze the antimoney laundering (AML)/CFT regime of Somalia, identify loopholes in the regime, raise awareness and propose recommendations for regime improvement. Design/methodology/approach The qualitative research approach is used to compare Somalia’s AML/CFT regime with the corresponding regime of Malaysia through the black letter method combined with document analysis. Malaysia is selected as a benchmark for two reasons: firstly, it is an Islamic country like Somalia, and secondly, Malaysia has complied with integrity-related standards. Findings This study revealed that an impactful AML/CTF regime is reached by closing loopholes in the law, reevaluating and improving regulatory agencies and measures, facilitating formal financial services and collaborating with regional and international standard setters. According to the results, Somalia AML/CFT regime is counterproductive in criminalizing offenses; regulating digital currencies and mobile money, disclosures and nonfinancial business and provisions; and governing training requirements for regulatory agencies and financial institutions. Originality/value To the best of the author’s knowledge, this paper is the first of its kind in the study of Somalia’s regime building. Also, this study incorporates rich scholarly discourse on effective regime building.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".