Future Risk Analysis for Bank Investments using PROMETHEE
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
Professional management of financial activities requires a good understanding of the inherent future risks of those activities. Despite the use of complex models for this purpose, significant risks often remain under-appreciated. This article develops techniques that will assist banking institutions at understanding the risks involved in micro-finance and related operations. The analysis is carried out by identifying financial operations that are typical of banking on a smaller scale, such as investing and extending credit, and the types of risk inherent in these activities, which are then grouped into the categories of operational risk, financial risk, management risk, and external risk. Analysis is conducted using the PROMETHEE multi-criteria decision methodology. Based on our new approach to risk analysis, many institutions will improve the management of their financial operations, including micro-finance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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