Developments in Risk Management in Islamic Finance: A Review
Bibliographic record
Abstract
The purpose of this study is to review recent developments pertaining to risk management in Islamic banking and finance literature. The study explores the fundamental features of risks associated with Islamic banks (IBs) as compared to those associated with conventional banks (CBs) in order to determine the extent to which IBs engage in effective risk mitigation. The study includes a consideration of the major studies in which the fundamental features of Islamic banks and finance (IBF) and the main characteristics of risk management in IBs are analyzed in comparison with those of CBs. Specifically, these two kinds of banks are compared in relation to the types of risks faced, the characteristics of those risks, and the nature and extent of exposure to those risks. A tabular methodology approach is used in concert with a comparative literature review approach for the analysis. The results show that there is weak support for Shariah-based product development due to the lack of risk mitigation expertise in IBs. The conclusion presented is that in comparison with CBs, IBs are more risk-sensitive due to the nature of their products, contract structure, legal costing, governance practices, and liquidity infrastructure. Furthermore, the determinants of the credit risk of Islamic banks in Malaysia (MIBs) are examined. Overall, bank capital and financing expansion have a significant negative impact on the credit risk level of IBs in Malaysia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".