INTERNATIONAL EXPERIENCE OF FUNCTIONING OF THE CREDIT SERVICES MARKET
Bibliographic record
Abstract
Introduction. The country's economy is still in a state where its development is hampered by the absence of clearly defined, priority programs to stimulate business and economic sectors, including through credit. Bank lending to business entities is an effective way of getting out of the crisis. Strengthening the role of credit relations as a means of stimulating the development of production is manifested in various aspects. Methods. Fundamental economic theories, lending theories, financial studies, as well as research by scholars and foreign scientists are investigated in the research. They used the generally accepted principles of scientific research to make their decision. A number of general scientific methods were used in the process of research, in particular: analogies and logical generalization (to study the prospects of bank lending); systematic analysis (to study the organization of the process of bank lending in the market of credit services), etc. Results. Based on the isolated problems, the foreign experience of countries such as the USA, Germany, Argentina, Mexico, Poland, Canada and Italy was explored, on the basis of which the prospects for improvement of the bank lending mechanism in Ukraine and credit monitoring of the borrower in particular were outlined. Discussion. Given the overseas experience of developed countries, it is possible to distinguish: a combination of different methods of restructuring problem loans; when assessing the potential risk of default on a loan, use different methods of determining it ("SAMRARI", "PARSEL" or "Rule 5 C"); when making class calculations, make corrective adjustments to the credit score of the borrower. Prospects for further studies of the credit services market may be the intensification of bank lending, which certainly implies further liberalization of refinancing policy, taking into account foreign experience. Keywords: credit, credit relations, credit policy, credit services market, bank lending.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".