Why do Islamic Banks Concentrating Finance in Murabaha Mode? Performance and Risk Analysis (Sudan: 1997-2018)
Bibliographic record
Abstract
During 1997-2018, Islamic Banks (IBs) in Sudan provided finance by Murabaha mode to their clients with more than 45% on average. This position raises questions of why do IBs concentrating finance in Murabaha Mode rather than other modes? is this concentration implying risk and does it have influence on the financial performance of IBs? This study aimed to discusses the reasons and answer these questions. Nonperforming loan(NPL), Murabaha to gross finance, Musharaka to gross finance, Mudabaha to gross finance and Salam to gross finance were used to indicate the credit risk. Return on Equity (ROE) was used to indicate the financial performance of IBs. Ordinary least squares technique was employed to determine the trend of relations between the variables. The main results of the study show that there is an important positive relationship between the NPL and provision finance by both Murabaha and Mudaraba modes. Whereas were a negative with both Musharaka and Salam. Moreover, it’s found that there is strong negative relationship between NPL and ROE. The main reason for the expansion granting finance by Murabaha mode is that IBs are heavy rely on collaterals and in case of clients’ failure to pay, they sell collaterals to keep their financial performance safety. The study strongly recommends IBs importance of diversify the granting finance among Islamic modes of finance to avoiding the risk of concentration the finance by Murabaha mode. Furthermore, monetary authority in Sudan need to keep IBs aware with the risk associated with Islamic modes, especially Murabaha.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".