Shari’ah Audit in Islamic Financial Institutions: The Postgraduates’ Perspective
Bibliographic record
Abstract
Islamic finance industry is claimed to be one of the fastest growing industry and allegedly immune to any financial turmoil. With a growth of between 15 to 20% for the past decades, it surely provides an interesting avenue for research. One aspect that is still under-researched is the shari’ah audit. In the absence of a proper framework and standards, this could dampen the future of the Islamic finance industry. There is an impending need for the Islamic financial institutions to provide reasonable and sufficient assurance that they have really followed the shari’ah through shari’ah audit. The absence of proper shari’ah audit framework is worrying. This study is embarked to get the perspectives of the postgraduate students on shari’ah audit in a country relatively new to Islamic finance industry. Postgraduate students from two universities are chosen as samples. Survey questionnaires are distributed by hand and the results are presented using frequency tables. It is found that they have a low level of awareness and understanding of the term and concept of shari’ah audit. They are also in vague as to whether the shari’ah audit is the same as conventional audit. Moreover, they feel that the Islamic financial institutions are not doing enough to promote shari’ah audit. They agree that the mass media has a big role to play in promoting shari’ah audit. Finally, they agree that the shari’ah audit has a very good potential to be developed in the future.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".