Evaluating Loan Performance for Bank Offices: A Multicriteria Decision-Making Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper aims at evaluating loan performance of bank offices. For this purpose, a multicriteria decision support model given uncertainty is developed, in which the criteria to evaluate loan performance are the different discount rates which can be potentially used to compute the net present value (NPV) of each loan outflow and repayment inflow. This proposal is motivated by the fact that the true discount rate (opportunity cost of capital) is uncertain. The bank offices are classified in non-dominated and dominated by other bank offices in terms of loan performance from the multiple NPV criteria. As a further result, a complete ranking of the bank offices from their performance indexes is obtained. This ranking relies on a principle of moderate pessimism to solve uncertainty tables. Although the proposed method is applicable to various types of loans, the special class of personal loans is emphasised in the paper. As an actual case, a set of bank offices are evaluated from their loans and repayments during the period 2001–2010. Numerical data are tabulated together with the computing process and the results.
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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 | 0.035 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it