Evaluating the Financial Soundness of Small and Medium-Sized Commercial Banks in Kenya: An Application of the Bankometer Model
Bibliographic record
Abstract
The study investigated the financial soundness of small and medium-sized commercial banks in Kenya over the four-year period, 2014 to 2017, using the bankometer model and further compared the financial health of the two bank categories. The study employed secondary data from a census of Twelve (12) medium-sized and Sixteen (16) small banks, with the financial soundness being proxied by the overall solvency score (S-Score) in order to achieve its objective. A total of six (6) different financial ratios namely, Capital to Assets ratio, Equity to Assets ratio, Capital Adequacy Ratio, Non-Performing Loans ratio, Operating Cost to Operating Income ratio and the ratio of Loans to Assets were used in the study to measure the degree of financial health of the banks. One of the key findings of the study was that both the small and medium-sized commercial banks in Kenya were financially sound during each of the four (4) years studied, with no significant difference in the financial soundness of the two bank categories. Other findings were that all the banks studied experienced poor performance in loans and operations while two banks had below the benchmark capital adequacy ratio. The findings of the study are important in that, they can be used to formulate policies and strategies for promoting improvement in the financial performance of the banking sector in particular and the business sector at large in the country.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".