Optimization of the Formation of the Capital Structure of the Insurance Company, Taking into Account the National Specifics of Insurance
Bibliographic record
Abstract
The study proposed an economic model of capital optimization of the insurance company, based on the actuarial method of calculating insurance tariffs. Features of the national insurance system are considered. A mathematical model and actuarial calculation of insurance tariffs for partners in the implementation of joint activities are proposed. As the algorithm implementation calculations were made for the model of joint life insurance of spouses, which has their own practical interest. A lump-sum net rate has been calculated for a contractual partner in the event of the death of one of the partners (spouses) before the retirement age, depending on the interest rate, the age of the spouses, their residual times to pensions, the death rates and the maximum permissible ages. Average time and variance of the Treaty were calculated. It is practically important for the insurance company when planning the investment of assets under the agreement. The results of the research allow us to calculate the insurance tariffs in the form of a lump sum payment to the insured persons and to evaluate the numerical characteristics of the validity period of the contract.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".