Empowering the Shari’ah Committee towards Strengthening Shari’ah Governance Practices in Islamic Financial Institutions
Bibliographic record
Abstract
The Islamic finance industry is growing at a rapid rate. Its products and services are widely offered all over the world. The ultimate vision of the emergence of Islamic finance industry is to avoid the prohibited practices of conventional financial institutions such as interest, uncertainty, gambling, and investment in prohibited items. If Islamic Financial Institutions (IFIs) manifest by excluding this vision, then they have failed in their mission. Consequently, shari’ah risk, i.e., non-shari’ah compliant risk is the main risk that IFIs must manage to maintain its distinguished status as shari’ah compliant institutions. Shari’ah governance is used as the guideline to mold the operational practices of IFIs to achieve the mission of shari’ah compliance. For this purpose, the shari’ah committee members are the main players for implementing good shari’ah governance practices. However, due to the limited authority of Shari’ah committee members in performing their tasks, IFIs are voluntarily exposed to Shari’ah risk. This paper highlights the current Shari’ah governance problems and proposes that the authority of Shari’ah committee should be enhanced for better Shari’ah governance practices. Problems with current Shari’ah governance practices are mostly due to fatawa variation, non-harmonization of Shari’ah governance practices and products, variance in the four schools of thought, and limited support from IFI management in discharging their full responsibilities such as their involvement in the Shari’ah review process and audit. This paper is set to develop Shari’ah governance guidelines.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".