Implications of Unisex Assumptions in the Analysis of Longevity for Insurance Portfolios in Indonesia
Bibliographic record
Abstract
Unisex pricing in insurance policies eliminates the use of gender as a rating factor, and it is increasingly implemented by insurance companies around the world during the last decade. Also known as “gender-neutral” pricing, unisex pricing ensures that insurance premiums for man and woman with the same risk characteristics is the same. This change in pricing method drives us to look into the mortality rate calculations as it is well known that the experience of mortality differs between men and women. This study focuses on longevity risks; therefore, it focuses on the mortality experience in the elderly. This study replicates “Implications of Unisex Assumptions in the Analysis of Longevity for Insurance Portfolios” written by Ornelas et al. using Indonesia’s population data. Five possible scenarios of gender proportion in a portfolio were created and analyzed on the effect of gender proportion on quantifying longevity risks. This study uses Weibull distribution and Value at Risk to quantify the longevity risks. This study found that longevity risk is larger when the number of female policyholders is increased in the gender proportion of a portfolio. Through the findings, we recommend an insurance company in Indonesia to calculate the gender proportion in a portfolio accurately, or they may face a large loss.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".