Financial Inclusion through Digital Financial Services (DFS): A Study in Uganda
Bibliographic record
Abstract
This study unravels trends and momentum in banking and mobile money channels and uptake of select services and thereafter draws implications for enhancing financial inclusion through Digital Financial Services (DFS). The Rate of Change (ROC) approach was applied to analyze the growth momentum in banking and mobile money channels in Uganda. Implications for growth momentum in banking and mobile money channels for DFS and financial inclusion was drawn from observing and making informed interpretation of such observed trends and momentum. The findings of this study imply that banks must innovate to increase their contribution towards enhancing financial inclusion. Additional channel innovations, which may infuse banking and mobile money channels, are needed for banking to leverage on growth of mobile money and regain its role in enhancing financial inclusion. Leveraging the application of digital innovations in services such as payments and digitizing alternative channels such as agent banking are likely to increase efficiencies in physical channels and the provision of banking services and thereby increase overall reach and penetration of banking. The fast pace of mobile money penetration is good for speeding up financial inclusion. However, this calls for better regulatory approaches for DFS risk reduction, consumer protection, and protecting mobile money against integrity and financial crimes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".