Bibliographic record
Abstract
The study explores the determinants of financial inclusion, barriers to financial inclusion, and the motivation for saving and credit through the formal financial sector.It further points out how Fintech could be used to enhance financial inclusion.The study uses the World Bank Global Findex Database 2017 survey.The survey is based on the feedback of more than 1000 individual participants.The probit estimation technique is employed to achieve the study objectives.Being male, educated, and rich are financially inclusive, especially high income and old age group.Financial inclusion has not been successful to eradicate inequality among various groups.Among individual characteristics, education significantly reduces the barriers to financial inclusion, the females are less motivated to save or borrow from financial institutions.Young individuals are likely to borrow for the purchase of a house or land but not for business.Elderly people are motivated to save for their old age.The distance and the cost of formal financial services along with the lack of documentation are the main barriers to financial inclusion.As per our knowledge, it is the first study that explores the various aspects of financial inclusion in the country, along with the review of the Fintech system.And suggesting how the Fintech system could enhance financial inclusion in the country.More comprehensive study including the Fintech variables and comparative studies with other regional economies considering the latest available data is suggested.The findings can help the policymakers, to formulate policies that can enhance financial inclusion through Fintech.The diversification and expansion of financial services could enhance financial inclusion, particularly for businesses at individual levels, and for SMEs.More importantly, it will contribute to achieving the financial sector objectives of Vision 2030.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".