The determinants of corporate disclosures of anti-money laundering initiatives by Kenyan commercial banks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aims to examine the extent and drivers of anti-money laundering (AML) disclosures in the audited annual reports of regional listed banks in Kenya. Design/methodology/approach Using the Financial Action Task Force recommendations and other guidelines, the authors develop an AML disclosure index that is used to score the extent of AML disclosures by banks. A sample of 15 listed regional banks in Kenya over the period of 2007-2017 is used. Using this sample, the authors performed fixed-effects regressions to identify the significant determinants of AML disclosures. Findings The study establishes a low level of AML disclosures in the audited annual reports of sampled banks. The extent to which the AML disclosures improved across three distinct regulatory regimes over the period of 2007-2017 is reported. The authors find that the AML disclosures are largely driven by corporate governance (board size and audit committee size) and the ratio of diaspora remittances to GDP. Practical implications Owing to the global nature of money laundering activities, the study suggests that the Central Bank of Kenya needs to internationalize AML regulations and follow internationally accepted best practices in AML to respond to emerging trends in money laundering and related crimes. Originality/value To the best knowledge of the researchers, this is perhaps the first study to examine the drivers of AML disclosures by banks in a developing economy in the East and Southern African region. Given the global nature of money laundering, the study makes an important and original contribution to the body of knowledge with potential for replication in other jurisdictions. The findings will also form a basis for developing an AML reporting or disclosure framework.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it