A Survey of Literature on Suspicious Transaction Monitoring: Anti-Money Laundering Compliance and Financial Performance of Commercial Banks in South Sudan
Bibliographic record
Abstract
This research delves into the nexus between anti-money laundering (AML) compliance and the financial performance of selected commercial banks in South Sudan, a country still on the FATF grey list despite substantial governmental investments in AML initiatives. Utilizing a cross-sectional and mixed-method design, the study specifically aimed to scrutinize the relationship between internal policies and the financial performance of commercial banks. Drawing from a sample of 105 participants across four banks, a comprehensive dataset comprising both quantitative and qualitative information was gathered. The findings underscore a noteworthy connection between internal policies and financial performance (r = 0.436, p = 0.000, n = 86), suggesting that improvements in internal policies may enhance financial outcomes. This study emphasizes the pivotal role of robust internal policies in fostering AML compliance and subsequently enhancing the financial well-being of commercial banks in South Sudan.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".