Merging Waqf and Takaful for Sustainable Death Compensation at FWD Takaful Berhad: What Do the Experts Say?
Bibliographic record
Abstract
The waqf-takaful death compensation concept merges two Islamic principles: waqf and takaful. In this model, death benefits from a takaful plan are directed to religious institutions as waqf, ensuring ongoing community support. This approach not only provides financial security to the deceased’s family but also creates a lasting charitable impact, turning a one-time payout into a perpetual source of funding for social and religious causes. Despite facing challenges such as inconsistent implementation standards and management difficulties, some takaful operators in Malaysia have begun integrating death compensation waqf into their products. FWD Takaful Berhad, as one of the key players in the takaful industry, holds considerable potential for collaboration in further developing this product. Therefore, this study aims to propose a waqf-takaful death compensation product for FWD Takaful Berhad. A qualitative approach was employed, with data gathered through Focus Group Discussions (FGDs) involving academics and industry experts in Malaysia. The data were analyzed using thematic analysis via ATLAS.ti software. The study identified several potential strategies for developing the waqf-takaful death compensation product for FWD Takaful Berhad. This research offers valuable insights for academics and industry stakeholders interested in proposing waqf-takaful death compensation products in Malaysia.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".