The current state of Ukrainian credit union’s development
Bibliographic record
Abstract
The article is dedicated to the analysis of the contemporary situation on Ukraine’s market of credit unions during 2005-2012 years. In some developed countries in the world the position of credit unions as financial institutions is strong enough. As well credit unions are spread in Scandinavian and Balkan peninsular countries, Canada, USA, France, Belgium and other modern economics countries. The aim of the article is to draw the modern situation on the domestic credit union’s market, to dispose the possible ways of improving their activity and to define the stop factors and prospects to the development of the credit unions. Also the round up of the latest legal framework is actual task for the article. The situation with Ukrainian credit unions can be characterized by unstable dynamics. The absence of escalation of the main systems rates last four years shows the poor prospects. It is necessary to provide the modern models of development: increasing general quantity of clients and main rates of the credit system. The main disadvantage of the credit unions in Ukrainian market is regional type of sale distribution. Practically every credit union is based on certain circle of enterprises or factories in different regions. The lack of systemic operation without typical service models is another stop-factor in the increasing of their development. Undefined clients target segment and the absence of marketing policy is also the reason of distrust to the credit unions. Nevertheless credit unions are rather perspective financial institutions, which can make strong competition to the banks on the consumer credit market. The basis rates of credit union’s activity were analyzed: evolution of the credit union’s member’s quantity, evolution of the general credit union’s quantity, evolution of the liabilities and assets level over a period of 10 years, some proposals to changes to the legal framework. (All the main rates came down for 25-30 % every year after crisis had started, but 2012 can be called the year of stabilization on the market and starting of slow rate increasing). There have been analyzed some fundamental basis of appearance and development of credit cooperation, determined the place and importance of credit unions in financial-economical system of Ukraine and motivated the necessity of forming of united national organizationally-coordinating model of functioning of credit union. After analyzing the financial potential of credit unions, there has been suggested basic ways of their improvement, such as: the introduction of the systemic format of production wide credit union distribution on the domestic market providing general models of service and reference standards ready to define the clients segment policy (specially smallscale business) governmental guarantee for deposit operations.
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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 | 0.000 | 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 | 0.001 |
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".