THE EXAMINATION OF THE MATHEMATICAL CALCULATION ON PREMIUM RESERVE MODIFICATIONS FOR ENDOWMENT-LIFE INSURANCE PRODUCTS
Bibliographic record
Abstract
Insurance offers numerous advantages in managing the diverse dangers it encounters. The level of compensation provided is contingent upon the magnitude of the premium remitted. A fraction of the premium collected by the company must be allocated as a premium reserve to ensure that the company will not have any challenges in settling future claims. The calculation of premium reserves is performed through the utilization of prospective and retrospective reserve methods, which rely on net premiums as the foundation for the computation. The premium reserve calculation approach employs both the Canadian and Full Preliminary Term methodologies. The objective of this study is to calculate the premium reserves for a dual-purpose life insurance policy using both the Canadian approach and the Full Preliminary Term technique. The policyholder in question is a participant at an insurance company. This study utilizes the 2011 Indonesian Mortality Table (TMI) to calculate premium reserves. The process involves determining the annuity value, net annual premium, adjusting the annual premium using the Canadian method, and calculating the premium reserves at the end of year t for dual-purpose life insurance. Calculations with different interest rates demonstrate that the value of the premium reserve will decrease as the interest rate employed increases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".