Credit Risk Prediction to Individuals
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
Loans to individuals become the most vulnerable segment of commercial banks’ investment in a volatile financial environment. Searching safe methods for modelling refund loans reliability is one of the methods for credit losses risk reduction in commercial banks. In this article, such problems as a credit risk increase and an effective means of its evaluating are considered. In addition, it is proposed to solve these problems by developing a mathematical model describing dependence between loan defaults and the factors characterizing the financial reliability of the borrower through the credit transactions example with individuals of a particular bank. The purpose of this model is to identify the relationship between the independent variables. The development of regression models to estimate losses from repayment risk from individuals is described in this article. The model reflects the relationship between significant independent factors characterizing the degree of the borrower’s financial reliability according to the component analysis method based on the model of David Cox.
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.
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 | 0.009 | 0.002 |
| 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 | 0.001 | 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