The effectiveness of Anti-Money Laundering policies and procedures within the Banking Sector in Bahrain
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the anti-money laundering (AML) policies and procedures applied by the banks operating in Bahrain and assess the effectiveness of these policies. Design/methodology/approach Data for the study came from semi-structured interviews with compliance officers in Bahrain’s banking sector. A total of 22 interviews were conducted with Bahraini money laundering reporting officers and bankers. Findings The findings indicate that the banks in Bahrain comply with international AML procedures in combating money laundering. Despite Bahrain being ranked as having strong compliance policies and AML procedures among the Gulf Cooperation Council region, there are still issues with regulatory technology that needs to be addressed. Practical implications While there has been a positive impact of AML procedures, there are always more procedures that can be taken into consideration by banks in Bahrain to have more robust mechanisms to mitigate against the threat of money laundering. Originality/value To the best of authors’ knowledge, this paper is among the first to conduct an informed study of the effectiveness of compliance in the Bahrain’s financial sector. It can be used as a foundation paper for more mix-research on money laundering threats facing Bahrain’s banks.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".