Financial Inclusion and Its Ripple Effects on Socio-Economic Development: A Comprehensive Review
Bibliographic record
Abstract
This study provides an overview of the different dimensions of financial inclusion, its socioeconomic impacts on society’s sustainable development, and future research agendas. Initially, 620 studies were identified using Scopus and other databases, employing keywords such as financial literacy, financial inclusion, financial capability, women’s empowerment, fintech, artificial intelligence, financial accessibility, sustainable development goals, and economic growth. After refinement based on focus and relevance, 325 papers were analyzed in detail for review, primarily focused on India and emerging economies. This review highlights that access to finance by untouched segments of society is essential for sustainable and socio-economic development in developing economies. The official banking system, an effort by the government to assist the financially disadvantaged, can incorporate the impoverished into a formal financial system through campaigns and credit system reforms. Socioeconomic programs reinforce one another and foster the development of children, women, families, and society. This research paper undertakes a systematic literature review primarily focused on relevant articles in broad areas of financial inclusion and its impact analysis and offers a valuable agenda for future research.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".