Credit risk in Islamic banking: evidence from the GCC
Bibliographic record
Abstract
Purpose This study aims to investigate the differences in the credit profiles of Islamic and conventional banks in the Gulf Cooperation Council (GCC) region and attempts to identify the factors responsible for those differences. Design/methodology/approach Financial data sourced from the Bankscope database for a sample of 25 Islamic and 56 conventional banks headquartered in the GCC region between 1987 and 2014 are used. The credit risk of Islamic versus conventional banks is compared using a variety of univariate (mean difference test and correlation analysis) and multivariate tests (pooled ordinary least squares (OLS) regressions with robust standard errors and year fixed effects, regressions with interaction variables and logistic regressions). Findings Pooled OLS regressions find that Islamic banks have lower credit risk than conventional banks. Robustness checks using logistic functions and interaction variables confirm this result. Using multiple econometric specifications, we also find that higher capitalization, greater liquidity and cost inefficiency contribute to the lower risk profile of Islamic banks. Research limitations/implications The study is unable to disaggregate data for banks offering both Islamic and conventional banking services and hence does not include conventional banks with Islamic windows. In addition, there are differences across countries even within the GCC region as to what is considered Sharia’h -compliant and what is not. Practical implications The results are of potential interest to not only researchers, but also market participants, regulators and legislators. The methods used in this study could be extended to other two-tiered banking systems and, in the case of Islamic and conventional banking, to other markets. Originality/value The authors use a unique sample of banks headquartered in the GCC countries, whose banking markets are similar, if not homogeneous, thus excluding operations of multinational banks. By focusing on the Gulf region, differences in the credit profiles of Islamic and conventional banks can be examined without the confounding effects of unobserved factors like culture, accounting regime or regulatory environment.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".