Comparative analysis of Saudi sharia compliant banks: A CAMEL framework
Bibliographic record
Abstract
A vibrant banking sector remains instrumental to the stability of every economy. Islamic banks are now considered as iris spuria of the banking industry. Countries such as Saudi Arabia have been hailed as the Islamic banking basin. Nevertheless, how well is this sector growing and performing in Saudi Arabia itself? This motivates us to carry out this study. The current study's key objective was to measure Sharia-compliant banks' efficiency on the CAMEL Framework, a commonly accepted framework for banks' financial health. CAMEL is fundamentally an acronym for which the first letter from the five primary segments of a bank operation is jumbled, i.e. “|C|apital adequacy, |A|sset quality, |M|anagement quality, |E|arnings ability and |L|iquidity”. The system is popularly being used for determining the financial soundness and stability of banks. The current study employs this framework to judge the financial performance of four fully Sharia compliant banks or Islamic banks in Saudi Arabia. The publicly accessible audited data of these banks over ten years was taken for analysis. From the final results of the analysis, it is found that all the banks performed stupendously well on the CAMEL framework. AlRajhi Bank was rated number one of all four Sharia-compliant banks. However other three banks namely Alinma Bank, AlBilad Bank, and Aljazeera bank have also done well and overachieved all the criterion of CAMEL's ranking. However, the study proposes a comparison of Sharia-compliant banks with conventional commercial banks. Moreover, it recommended that more banks should engage in offerings of Sharia based products.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".