Risk management practices: A comparative study of Islamic and conventional banks in the MENA region
Bibliographic record
Abstract
The study sought to determine how bank financial performance (BFP) was affected by credit risk (CR), liquidity risks (LR), operational risks (OR), financing risks (FR), market risks (MR), in the presence of risk management (RM) as a moderator in conventional and Islamic banks in the Middle East and North Africa. To this end, stratified random sampling and systematic sampling methods were used, with a sample size of thirty conventional banks and thirty Islamic banks from the Kingdom of Saudi Arabia and the Arab Republic of Egypt acting as the unit of analysis. 344 participants that were targeted had completed questionnaires that could be analyzed. The database of the target banks was used to quickly and affordably choose samples. Structural equation modeling was done in conjunction with a tool named Smart PLS 4 (SEM). A 92% reliability coefficient was used to evaluate the instrument's dependability. By assessing study variables using commonly used terminology and consulting with subject matter experts on the research issue, the content validity of the findings was confirmed. PLS 4 was one of the clever analytical approaches used to characterize the study's findings. The following describes the relationship between risk management practices and BFP when utilizing a modified variable (RM): "The study showed that CR does not positively affect BFP in conventional banks when employing a modified RM variable. The study demonstrated that the risk ratio had no positive influence on BFP in Islamic banks using a modified RM variable. It has been established by study that LR has no positive impact on BFP. The study also demonstrated that the LR has no positive effects when the variable RM rate is used in conventional banks. The study's findings demonstrated that the OR does not change when the variable RM rate is used. It is advantageous for BFP in traditional banks. The study discovered that there is a negative correlation between OR and BFP in Islamic banks and that OR has no beneficial effect on BFP when the RM rate variable is included. The study's findings demonstrated a favorable correlation between OR and BFP. The research indicates that in typical banks, FR does not positively increase BFP when employing the adjusted RM variable. The study discovered that there is no correlation between FR and BFP in Islamic banks when the modified RM variable is used. Rather than suggesting a good association between FR and BFP, the results pointed to a negative investigation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".