Marketing Complexes as a Mechanism for Managing the Financial Activities of Insurance Companies in Ukraine
Bibliographic record
Abstract
The purpose of the study is to analyze the market of insurance products in Ukraine, to determine the effective implementation of marketing complexes in the activities of insurance companies and their impact on the management of financial activities of insurance companies.The analysis of the insurance market in Ukraine is carried out and, taking into account the diversity of external and internal financial relations of insurers, increased competition, the mechanism of effective financial management at the micro level is proposed using world experience and taking into account domestic peculiarities.The legal framework and the mechanism of state regulation of financial services markets, the current state of the insurance market in Ukraine, the dynamics of changes in the number of insurance companies, the factors that negatively affect the activities of insurers are analyzed.For insurance companies that continue to operate, it is proposed to intensify the use of marketing mechanisms in insurance, which will allow insurance companies to adapt to their business activities in conditions of uncertainty in order to increase sales of insurance products and meet customer needs.To put this idea into life a simulation model has been developed that will achieve the appropriate level of marketing strategy and determine the most attractive alternative from a variety of alternatives to effectively manage the financial activities of the insurance company, which will ensure a positive financial result in the long run.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".