Credit Management in Full-Fledged Islamic Bank and Islamic Banking Window: Towards Achieving Maqasid Al-Shariah
Bibliographic record
Abstract
The percentage of non-performing loans (NPL) and non-performance financing (NPF) in Malaysia commercial banks is under control and does not increase in its percentage since the year 2014 to 2016. These scenarios are the result of the stringent credit management procedures in the commercial banks which if not properly followed could affect banks’ profitability and liquidity. However, there are critics by the public on how Islamic banks normally tend to punish their customers who are the real traders or businessman without fixed monthly income when they slightly late in paying their periodical payment for the bank’s facility or financing. Islamic bank is supposed to help the customer in order for it to achieve maqasid al-Shariah (objectives of Shariah). Thus, this study intends to compare the credit management’s procedure in one of the full-fledged Islamic banks and one of the conventional banks which also offer Islamic banking window. This study also aims to identify the achievement of maqasid al-Shariah through the procedures of credit management in the two banks. This study adopted the qualitative methodology where semi-structured interviews are conducted with 2 bankers from one of the full-fledged Islamic banks and one of the conventional banks which also offer Islamic banking window. Results from this study indicated that each banks has their own strategies and procedures with regard to credit management. Their credit management plans were structured to help customers to secure their loans, financing and assets as well as protecting them from bankruptcy and insolvency which basically comply with maqasid al-Shariah. From this study, it is recommended for commercial banks to apply a strict approval process for loan and financing as well as a strict credit monitoring system to avoid NPL and NPF.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".