Viability of Islamic Insurance (Takaful) in India: SWOT Analysis Approach
Bibliographic record
Abstract
Takaful (Islamic insurance) has been widely accepted as an alternative to conventional insurance and offered in many Muslim and non-Muslim countries. The unique feature of Takaful is that is suitable and acceptable for anyone regardless of the religion to our surprise, Takaful has not been introduced in India. India has the third largest Muslim population after Indonesia and Pakistan and second largest population after China. In terms of economic development, India’s GDP growth rate is 6.3% and it is expected that in coming years and it is believed that India will be one of the leading countries for the world economy. Thus, the objective is to examine the viability of Takaful in India by using SWOT analysis approach. Questionnaire has been distributed to both Muslim and non-Muslims to find out the awareness, acceptability, prospects and challenges of Takaful products. Interviews have been conducted to examine the opinions of ten insurance operators, fifteen Shari’ah advisors and five consultants regarding the prospects and challenges of introducing Takaful in India. The findings from 333 respondents show that awareness of Takaful is still at the minimum level. However, they are willing to participate if Takaful is offered in India. In addition, the findings of the interviews highlight that the Takaful has a good potential in India. However, it can be offered if the government supports it. Due to time limitation, the opinion of the regulators has not been examined and thus, future research should focus on it.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".