Innovations in Risk Management as Exemplified by the Polish Insurance Market
Bibliographic record
Abstract
The era of knowledge based economy, which the world is dynamically entering, reveals many new factors and necessities which a modern company needs to consider and cope with if it wishes to stay on in business. The innovativeness of business activity, in a broad meaning of the word, is an area of significant factors. Holistically, one may ask about the borders of innovation applied in business. Are they determined by: the actual needs of end consumers and as a consequence the needs of companies participating in the chain of value delivered to these customers or primarily the actual needs to earn on corporate investments on the customer value chain?This paper analyses the problem of management of innovation programmes and projects introduced by companies from the microeconomic perspective. Its main aim is to show a new approach to innovation cost management and the innovation activity of the insurance sector in Poland. The paper consists of two sections. The first one describes innovation cost management. Innovative companies should be supported by the insurance sector. They should also apply knowledge and appropriately analyse and allocate costs within the algorithms of behaviour compliant with the risk management. The second section analyses the Polish insurance sector with the Multivariate Statistical Analysis. The paper ends with the conclusions with regard to the insurance sector. The examination of the sector shows that the insurance market in Poland in the analysed period was not innovative and it did not create innovation supporting services (and it is where the insurance risk appears due to the financial aspects of innovation).
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".