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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".