Talent Management for Shariah Auditors: Case Study Evidence From the Practitioners
Bibliographic record
Abstract
The environment for most financial institutions today is complex, dynamic, highly competitive, and extremely volatile, and such condition is likely to remain for years to come. In addition to these external situations, most financial institutions also faced the challenge to manage talents flow in particular, a shortage of needed competencies. One measure to overcome this condition is to be systematic in managing their human capital if they wish to gain and sustain a competitive advantage in years ahead. This paper postulates to explore the competency criteria as one of talent management for shariah auditors in the Islamic Financial Institutions (IFIs) in Malaysia. A qualitative design was adopted by conducting interviews with 30 practitioners consisting of the Heads of Shariah audit departments (HSA) and shariah auditors (SAR) from the IFIs and the Head of Islamic banking department from the Central Bank of Malaysia. This is followed by a focus group discussion to validate the findings. The study found that there was mixed practices on talent management in terms of competency aspect required for shariah auditors. Generally, participants agreed that certain skills, knowledge and characteristics added with years of experience in the field, are pre-requisite to become competent shariah auditors. This study is unique as it explores the case from the qualitative stance. Opinions were elicited from the parties directly involved in preparing guidelines to the IFIs as well as those practitioners executing the shariah audit function within their institutions. IFIs will have better guideline on recruiting competent future shariah auditors, as part of their internal audit team to uphold the shariah precept.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".