Empowering the Shari’ah Committee towards Strengthening Shari’ah Governance Practices in Islamic Financial Institutions
Bibliographic record
Abstract
<p>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, <em>shari’ah</em> risk, i.e., non-<em>shari’ah</em> compliant risk is the main risk that IFIs must manage to maintain its distinguished status as <em>shari’ah</em> compliant institutions. <em>Shari’ah</em> governance is used as the guideline to mold the operational practices of IFIs to achieve the mission of <em>shari’ah</em> compliance. For this purpose, the <em>shari’ah</em> committee members are the main players for implementing good <em>shari’ah</em> governance practices. However, due to the limited authority of <em>Shari’ah</em><em> </em>committee members in performing their tasks, IFIs are voluntarily exposed to <em>Shari’ah</em> risk. This paper highlights the current <em>Shari’ah</em> governance problems and proposes that the authority of <em>Shari’ah</em> committee should be enhanced for better <em>Shari’ah</em> governance practices. Problems with current <em>Shari’ah</em><em> </em>governance practices are mostly due to <em>fatawa</em> variation, non-harmonization of <em>Shari’ah</em><em> </em>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 <em>Shari’ah</em> review process and audit. This paper is set to develop <em>Shari’ah</em> governance guidelines.</p>
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".