The Determinants of Credit Risk: An Evidence from ASEAN and GCC Islamic Banks
Bibliographic record
Abstract
In less than a decade, the Islamic Banking (IB) industry has become an essential part of the global financial system. During the last ten years, the IB industry has witnessed changes in economic conditions and proved to be resilient during the periods of financial crisis. This paper aims to examine the important issues related to credit risk in selected Islamic banks in nine countries from Association of South East Asian Nations (ASEAN) and Gulf Cooperation Council (GCC) regions. It employs the generalized least squares panel data regression, to estimate the ratio of non-performance financing to total financing as dependent variables and bank specific variables (BSV) to determine the credit risk. It uses 12 years of unbalanced panel data from 40 different Islamic banks. The overall findings show that financing quality has a significant positive effect on credit risk. It is observed that the larger IBs owned more assets with lower credit risk compared to smaller banks. The bank’s age is also an important factor influencing the credit risk level. Moreover, regulatory capital significantly reduces the credit risk exposure adherence to the minimum regulatory capital requirements which help IBs to manage their credit risk exposures. It was also observed that IBs were not affected by the global financial crisis due to less credit risk compared to the conventional banks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".