ASSESSMENT OF UNPROFITABILITY OF COMPULSORY EMPLOYEE ACCIDENT INSURANCE TARIFFS IN KAZAKHSTAN
Bibliographic record
Abstract
Compulsory insurance of employees against industrial accidents is an important social protection tool in Kazakhstan, but the current rates are insufficiently stable and do not cover the actual risks, which leads to financial imbalances among insurers and reduces the level of compensation for employees. The purpose of this study is to identify the reasons for the unprofitability of existing rates and to develop proposals for their modernization based on modern risk assessment methods. The study used actuarial calculations, including analysis of loss ratios, payments, and total expenses, as well as the construction of linear trends to forecast future losses. In addition, statistical modeling methods were used, including probability distributions and Markov chains, which made it possible to justify the introduction of a differentiated tariff system and a bonus-malus mechanism for enterprises depending on the level of industrial injuries. The results of the analysis showed an increase in the loss ratio from 11.87 percent in 2020 to 35.33 percent in the second quarter of 2024 and an increase in the aggregate loss ratio from 65.6 to 73.1 percent. The calculations determined a new base rate of 0.6842 percent compared to the current level of 0.59 percent, reflecting the need to revise insurance rates to ensure the financial stability of the system. A two-tier tariff model has been proposed, taking into account industry and occupational risks, as well as a bonus-malus mechanism that creates economic incentives for employers to invest in improving occupational safety and reducing the number of accidents. The practical significance of the study lies in the possibility of applying the results obtained by insurers, government agencies, and employers in forming a balanced and sustainable compulsory insurance system capable of simultaneously strengthening the financial stability of the insurance market and increasing the social protection of workers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".