Strategies for Banks Anti-Money Laundering/Counter-Terrorism Finance Compliance Programs to Protect Financial Systems
Bibliographic record
Abstract
Ineffective implementation of anti-money laundering (AML) compliance programs exposes the vulnerability of banks’ and increases the threats of money laundering and terrorist financing. The banking community must address the threat of money laundering and terrorism finance to protect the global financial system from abuse. Grounded in the fraud management lifecycle theory, the purpose of this qualitative multiple case study was to explore strategies to reduce threats of money laundering and terrorist financing. Data were collected from semistructured interviews, a review of bank policy documents, and previous Bank Secrecy Act (BSA) cases. The participants comprised six BSA/AML compliance officers at banks in the United States and Canada with experience implementing successful AML compliance programs. Thematic data analysis revealed three themes: effective internal and external communications, enhanced human/technological collaboration, and consistent internal compliance training. A key recommendation is to incorporate external communications with law enforcement. Potential, positive social changes include better educated bank compliance personnel, improved transactional monitoring, and enhanced employee training to reduce illicit and fraudulent financial activity which could result in weakened cartel operations, increased tax revenues, and more prosperous and safer communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".