17 Years of Fintech for Financial Inclusion: A Systematic Review and Critical Value Analysis
Bibliographic record
Abstract
The use of ICT tools and platforms to support the financial practices of underserved communities has been growing, but on-ground experiences have been mixed and sometimes detrimental. To achieve the potential benefits of technologies for financial inclusion, a deeper understanding of the design of digital financial interventions is essential. We systematically and critically reviewed studies that developed and implemented technological interventions for financial inclusion from 2007 to 2024. Our analysis examines the types of financial technologies developed, the devices and technical channels used, the research methodologies employed, and the target populations. Additionally, we conducted a reflexive thematic analysis to investigate the values underpinning these studies. By analyzing 30 articles, we interpreted a total of 13 value themes-including trust, accessibility, robustness, and compatibility. Our work highlights a complex interplay of values, structural considerations, technical attributes, financial practices, and economic factors that underlie the design of financial technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".