Solving the Bank Credit Decision Problem via Revised Group Multi-Role Assignment
Bibliographic record
Abstract
The Bank Credit Decision-Making Problem (BCDMP) is one of the main issues that bank operations need to face. To obtain the maximum profit value and optimal loan plan of the bank as much as possible, this article suggests converting BCDMP into a Many-to-Many Assignment Problem, which can be specified by the Multi-Role Assignment (GMRA). GMRA is a sub-model of the E-CARGO. By revised GMRA, the relationship between the enterprises and loans is converted into the relationship between agents and roles, and a multi-dimensional and multi-index evaluation method is used to evaluate the matching degrees between enterprises and loans. We use the Entropy Weight Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to obtain the enterprises' score for a loan through four indicators: profits, inventory, turnover ability, and credit. Then, we considered the impact of different loan interest rates on bank profits, obtained enterprise scores under different loan interest rates, and used linear programming to solve the problem, achieving good results (The bank achieved a profit margin of 5.43176% via revised GMRA).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".