The Legal Combat of Financial Crimes: A Comparative Assessment of the Enforcement Regimes in Nigeria and South Africa
Bibliographic record
Abstract
Abstract Financial crimes are debilitating problems for economies, especially emerging ones. The scourge of financial crimes includes money laundering, fraud, drug and human trafficking, terrorism financing, bribery, embezzlement, market manipulation, tax evasion, identity theft, forgery and cybercrime. These problems are so intractable and potentially destructive that the collective effort to prevent or contain them has gone global. The imperative of enhanced transparency and financial system integrity, not only in national financial systems but also in the international financial order, has become inevitable. This has resulted in the landmark frameworks of the United Nations Convention against Illicit Traffic in Narcotic Drugs and Psychotropic Substances and the G7’s Financial Action Task Force. This paper discusses the legal combat of financial crimes in two major African economies: Nigeria and South Africa, with particular emphasis on money laundering and terrorism financing due to their direct negative macro-economic implications for any economy. The focus on the twin problems in those two economies is based on their pre-eminent position in Africa. The paper examines the legal frameworks for the prevention or containment of the scourge in the two countries and interrogates measures that could engender their effective control.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".