Bibliographic record
Abstract
This study explores the transformative role of financial technology (FinTech) within the broader context of innovation ecosystems and its interconnections with financial inclusion. Financial technology has grown significantly in recent years. It encompasses sectors such as digital payments, blockchain, artificial intelligence, regulatory technology and insurance technology, reshaping the operational dynamics of financial systems, improving efficiency, competition, and transparency. In turn, the rapid adoption of data-intensive technologies introduces new regulatory and ethical challenges. Following an approach based on an examination of scientific literature the study first emphasizes the need to move beyond a technology-centric view and adopt an ecosystem-oriented perspective. This view highlights the interaction between technological advances, institutional frameworks, and market structures, as well as their impact on innovation. The analysis also shows that global connections and collaborations within FinTech ecosystems favor the development and diffusion of new innovations. Furthermore, FinTech and its ecosystem promote financial inclusion by reducing barriers to entry, making access to services affordable, and tailoring solutions to diverse customer needs. This benefits disadvantaged and underbanked populations, thereby expanding economic participation and reducing inequality. Given that current literature does not explicitly highlight the interrelationship among FinTech, innovation ecosystems, and financial inclusion, this study — without pretending to be exhaustive — underscores their interconnection. It demonstrates how collaborative technological innovation can expand access to financial services, despite the challenges and problems that remain to be solved. The article concludes that financial technology should be understood as a systemic force with profound implications for economic transformation, regulatory adaptation, the democratization of access, and the reduction of inequality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".