Bank-specific determinants of credit risk in Islamic banks: Evidence from Middle East
Bibliographic record
Abstract
Credit risk affects the work and reputation of banks. Islamic banks' heavy reliance on debt financing has led to increased interest in credit risk and its management. This paper aimed to identify the bank-specific factors affecting credit risk in Islamic banks, expressed as Non-Performing Finance NPF in the Middle East for the period 2011-2022. The study was based on a panel data analysis of 30 Islamic banks. The findings of study show a significant negative impact of Return On Assets ROA, Capital Adequacy Ratio CAR and size Z on the credit risk. The findings show a significant positive impact of Finance Loss Provision FLP on the credit risk. The findings also show no impact of Finance Expansion FEX, Finance to Deposit Ratio FDR and Capital Ratio CPR on the credit risk. The study showed that increasing the provision for financing losses and capital adequacy helps banks to reduce the impact of credit risks. The study recommends applying cautious lending policies and carefully selecting clients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".