Exploring the landscape of financial inclusion through the lens of financial technologies: A review
Bibliographic record
Abstract
• This bibliometric analysis reveals five distinct research clusters, highlighting the transformative impact of FinTech innovations such as mobile money, blockchain, and digital banking on financial inclusion. • The study underscores the potential to bridge the gap between the unbanked population and formal financial services, promoting economic empowerment and sustainable development. • The article identifies future lines of research and provides a comprehensive map of the current stance of knowledge in the field, offering valuable insights for continued exploration and innovation in FinTech for sustainable development. The intersection of financial technology (FinTech) and financial inclusion is increasingly recognized in both academic research and policy as a powerful tool for addressing global challenges and supporting sustainable development. Financial inclusion is essential for achieving the United Nations' sustainable development goals (SDGs), with access to formal financial services playing a critical role in fostering economic growth and improving the livelihoods of underserved populations. FinTech innovations, including mobile money, blockchain, and digital banking, have transformed the financial landscape by providing scalable, cost-effective solutions to bridge the gap between the unbanked and formal financial services. This paper presents a bibliometric analysis of the intersection between FinTech and financial inclusion, identifying five distinct research clusters. The findings reveal the significant role of mobile money and blockchain in enhancing financial access in developing countries, the impact of digital banking in reducing transaction costs and expanding credit access, and the growing relevance of blockchain for transparency and security in financial systems. This study also highlights emerging research areas, such as FinTech's impact on gender disparities in financial inclusion and the role of artificial intelligence (AI) in financial services, offering a foundation for future research.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.047 | 0.059 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".