Money laundering conviction rate and capital formation in Nigeria
Bibliographic record
Abstract
Drawing from the Financial Action Task Force (FATF, G7) recommendations and the Nigeria Anti-Money Laundering Act that provides the penance and dissuasion for crimes in Nigeria, this study sought to interrogate the efficacy of money laundering conviction rate as an instrument of anti-money laundering policy on capital formation in Nigeria. The study is hinged on contemporary deterrence theory. The study adopted the ex-post facto research design and used quarterly data from 1Q 2010 to 4Q 2019, which was sourced from the Nigerian Financial Intelligence Unit (NFIU), Economic and Financial Crime Commission (EFCC) and CBN statistical reports. Hence, Error Correction Model (ECM) was utilized to analyze the data. The findings indicate that the current money laundering conviction rate (MLCR) has a negative and non-significant effect on capital formation in Nigeria. Therefore, the study concludes that the current conviction rate is too weak to deter the act. Hence, it is recommended that the judicial system be rejigged with enabling legislation and autonomy to strengthen it to hasten the trial of such cases and ease conviction of culpable individuals, without necessarily putting innocent victims at jeopardy. Such autonomy should also be granted to the EFCC, ICPC, NFIU etc. and inter-agency synergy should be encouraged.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".