Financial Inclusion, GDP and Credit Risk of Commercial Banks in Kenya
Bibliographic record
Abstract
This paper provides an empirical analysis of the synergies and trade-offs between financial inclusion and credit risk of commercial banks in Kenya. The paper analyzed the effect of financial inclusion on credit risk and the moderation effect of GDP on commercial banks in Kenya. Financial inclusion was measured using three dimensions of bank availability, bank accessibility and bank usage, while credit risk was represented by the non performing loans ratio. The study was anchored on financial intermediation theory supported by finance growth theory and asymmetry information theory. The target population was all the 43 commercial banks in Kenya. The study used secondary data collected from Central Bank of Kenya annual reports; commercial banks of Kenya published audited financial statements and annual data from Central Bureau of statistics of Kenya for the period between 2007-2015. Data was analyzed using descriptive statistics and panel multiple regression analysis. The results obtained found that bank availability, bank accessibility and bank usage had significant effect on credit risk of commercial banks in Kenya. GDP growth rate was found to partially moderate the relationship between financial inclusion and credit risk. From the findings the study concluded that financial inclusion has a significant effect on credit risk of commercial banks in Kenya. The study also recommended that commercial banks in Kenya to negotiate with Central Bank and the Ministry of Finance to put policies which support favorable macroeconomic variables especially GDP which influences the level of financial inclusion and bank credit risk.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".