Financial innovations and bank performance in Kenya: Evidence from branchless banking models
Bibliographic record
Abstract
Background: Kenya has become the epicentre of branchless banking financial innovations in the last decade, effectively attracting global research interest.Aim: This article examines the relationship between financial innovation and the financial performance of 42 commercial banks in Kenya.Setting: The financial innovations covered are the branchless banking models, which represent a departure from the traditional branch-based banking. More specifically, the financial innovations covered are: mobile banking, agency banking, internet banking and automated teller machines.Methods: We use the Koyck dynamic distributed lag model to estimate the relationship between financial innovations and bank financial performance. The model has been using dynamic panel estimation with system generalised method of moments.Results: The results show that financial innovations significantly contribute to bank financial performance, and that firm-specific factors are more important in determining the firm’s current financial performance than industry factors.Conclusion: We provide evidence that financial innovations generate good results for the shareholders, suggesting that shareholders are the primary beneficiaries of financial innovations used by commercial banks.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".