Consumer protection framework in the Kenyan financial services sector: Current state, deficiencies, lessons from the world and possible solutions
Bibliographic record
Abstract
This paper discusses the current state of the market conduct regulatory framework in the Kenyan financial services industry. The paper then progresses to carry out a comparative analysis of the Kenyan regulatory framework with the Canadian, UK and US market conduct regulatory frameworks. The comparative analysis will include the gaps in the Kenyan regulatory framework vis-à-vis the Canadian, UK and US markets. The paper also assesses the data protection legal framework in Kenya, its deficiencies and how this affects consumer protection. It then provides recommendations on how the Kenyan government agencies can cure the deficiencies noted. One of the crucially important learning points in this paper is the importance of data protection laws in ensuring consumer protection. A vital conclusion is how absolutely essential proactive regulator action on market conduct is in ensuring a robust market conduct protection framework. Such regulator action would be market conduct themed inspections that would lead to recommended actions to cure deficiencies to enhance the data protection framework. This paper also recommends that regulators in collaboration with government policymakers look at the emerging area of FinTech and examine the market conduct framework that should be put in place.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".