A study on Islamic finance as an approach for financial inclusion in India
Bibliographic record
Abstract
This paper endeavors to investigate whether the Islamic financial system can tackle the issue of financial exclusion in India or not. The present study has made an earnest attempt to explore the discriminating factors behind choosing of the institutes (conventional or Islamic), in decreasing order of their importance. Data for the study are collected from 635 respondents, who are customers of Islamic and traditional financial institutes. The area selected for the survey is the state of Kerala, which is considered as the Islamic finance hub in India. The data collected are analyzed by employing the discriminant analysis along with drawing inferences from descriptive statistics. The study finds various factors in descending order of their importance. The factors are type of employment, religion (Muslim/Non-Muslim), income and gender. These are discriminating factors for choosing particular institutes (conventional or Islamic). The study shows that Islamic finance system was chosen by those, particularly Muslims, who did not have good employment and sufficient income. Hence, it is recommended that extensive formal beginning of Islamic finance in India, will lead to higher financial inclusion, since generally the financially excluded individuals belong to the said segments of the society, furthermore, Islamic finance is highly fascinated by the mentioned groups, the planners should think accordingly. The study is novel in its’ approach as it evidently illustrates that Islamic financial system is chosen by those, who do not have good employment, Muslims and those who earn less. Thus, there should be extensive formal commencement of Islamic finance in India to kick off higher financial inclusion.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".