Effective factors on optimizing banks’ balance sheet using fuzzy analytical hierarchy process
Bibliographic record
Abstract
Every bank seeks methods to optimize its assets and liabilities, thus the main subject is managing assets-liabilities in the balance sheet and the main question is by which factor banks will be enabled to have an optimized combination of assets and liabilities in a common level of risk to get the most return. This case study is dedicated to Refah bank and is an applicable study. The data has collected from the headquarter by a questionnaire and finally effective factors weight on optimizing bank balance sheet determined by using Fuzzy analytical hierarchy process. Results showed that revenue has more effect on optimizing for %39.5 and also loan to deposit ratio for %.74, regarding revenue as a symbol of efficiency in banks, it seems to be the most important factor and goal in banking industry. Furthermore banks need to have some liquidity to respond customers demand to cover one of the most important risks of banking. This factor importance determined to be %18 in Refah Bank by using model and experts view.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".