Analysis of Premium Reserve Using Zillmer Method and Canadian Method for Endowment Joint Life Insurance
Bibliographic record
Abstract
Several life insurance companies are unable to compensate policyholder prompting financial losses, the situation can be foreseen if the insurance company has a properly established and calculated reserve value. Endowment life insurance is one types of life insurance. Life insurance provides protection for one person (single life) or two or more people (multiple life). According to the insured death status, there are two terminologies used in multiple life insurance: joint life and last survivor. The Zillmer Method and Canadian Method used in this study for 3 age cases for a couple of husband and wife whereas a husband is older than wife, a husband has the same age as wife, and a husband is younger than wife to determine the amount of reserves adjusted for endowment joint life insurance. Researchers first determine the benefits, then calculate the annuity, and finally calculate the annual premium in order to compute reserves. The Zillmer Method premium reserve value is minus in the beginning year to cover cost for the company, meanwhile Canadian Method is not. According to the result of this study, the case that the age of wife is same as the husband have lesser reserve than any cases which represent this is beneficial for the company to cover several costs for the policy in the beginning of period. Based on data analysis, the period of the insurance contract and the age of the insured define the reserve value. The older the insurance participant, the lesser the value of reserve.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".