Performance Evaluation of Islamic Banking Services Industry: Evidence from GCC
Bibliographic record
Abstract
This study documents the comparative financial performance of the Islamic Banking Services Industry (IBSI) in the Gulf Cooperation Council (GCC) region. After drawing the performance evaluation framework (based on the CAMEL framework), the research conducted data analysis of the Islamic Banking Services Industry (IBSI) in the GCC region for 31 quarters (2013Q4–2021Q4). The analysis examines capital adequacy, asset quality, management performance, earnings, and liquidity management. Objectively classified data trends are reported through graphs. Additionally, the research documents internal determinants of financial performance. Findings suggest that the GCC-IBSI has shown overall progress in achieving primary objectives (commercial performance), including healthy capital adequacy, cost control, equity returns, and liquidity management. Capital adequacy, cost control, and liquidity management significantly contribute to financial performance. Managerial implications include cost control, reduction in non-performing loans, and prudent liquidity management. There exist opportunities in the GCC-IBSI for investors, given the mismatch in demand and supply of Islamic financial services. This study contributes to the literature by documenting findings on the achievements of the primary objective of IBSI in multiple GCC-IBSI markets comparatively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".