Bibliographic record
Abstract
The banking activity represents a strategic sector of sustainable economic development in Tunisia. Hence, Tunisian banks have the status of financial institutions that earn profits by providing financial services to customers by dealing with risks. Therefore, lending decisions for these establishments are strategic as they can avoid the risk of loan recourse. However, the assessment of borrowing sanctions in Tunisian banks is based on credit rating models. Consequently, it is important to assess the riskiness of the banking sector in Tunisia. Indeed, Tunisian banks have kept voluminous data concerning their clienteles which can be considered as critical knowledge assets which can be processed via underwritten credit management tools. This tools denote a recent development of statistical techniques and promising tools of data mining and data processing. The current study attempts to develop the rating model as a decision support system to credit approval evaluation at Tunisian banks based on applicant’s characteristics; the proposed model is mainly based on quantitative and qualitative criteria can be used to help credit officers make better decisions when evaluating future loan applications. A real-world credit application of cases of both granted and rejected applications from BTE bank was employed to develop the rating model. The experimental outcomes showed that this approach area promising addition to the existing classification methods. It therefore requires a high responsibility and commitment of managers in the process of evaluation and decision-making to reduce both the risk of default and the risk of debt distress.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".