How to Build up the Loan - Evaluation System toward Small and Medium Enterprises between Taiwan and China’s Commercial Banks？ The Application for Multi Criteria Decision Making
Bibliographic record
Abstract
Because of the financial market fast development between Taiwan and Mainland China, commercial banks, sometimes, cannot have an efficient method to investigate loan credentials for small and medium enterprises. Without efficient methods, commercial banks are forced to undertake the unnecessary default risks. The article chooses the behaviors of commercial banks between Mainland China and Taiwan to become study target. The purpose is to investigate the assessment method and influence factor of commercial banks toward small and medium enterprises for loan. Through modified Delphi approach and Fuzzy Analytic Hierarchy Process (FAHP) in multi-criteria decision-making (MCDM), the article attempts to analyze the decision-making measure of loan for commercial banks toward small and medium enterprises. From the results of practical evidence, the loan assessment measure of commercial banks between Taiwan and Mainland China is quite different. During loan assessment, China commercial banks focus on the debt-paying ability; on the contrary, Taiwan commercial banks focus on profitability of enterprises. The practical approved result of the study has supplied the insufficient data for related study articles in the past. More, the study can also be an important reference for perform related tasks in Taiwan’s and China’s commercial banks.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".