Study Note on the Actuarial Evaluation of Premium Liabilities Prepared by:
Bibliographic record
Abstract
In Canada, appointed actuaries are required to opine on the adequacy of the policy liabilities for property-casualty insurers. Policy liabilities include both claims and premium liabilities. Several papers have been written and actuarial techniques have been developed to estimate claims liabilities. Premium liabilities, however, have received little, virtually no attention in the actuarial literature. To date, we believe that only Canadian actuaries have been evaluating these liabilities. However, other countries are following that lead. We understand that in some states, regulators will soon require actuarial opinions on the adequacy of unearned premiums for policies with terms exceeding twelve months. The evaluation of premium liabilities consists of examining all related assets and liabilities to ensure that the anticipated net costs to discharge an insurer's obligations with respect to its insurance and reinsurance contracts, except its claim liabilities, are provided for. This paper intends to provide the practicing actuary with some guidelines on the evaluation of the premium liabilities. We will review the individual components of the premium liabilities and the related regulatory requirements and CIA recommendations. Finally, we will present an actuarial approach to evaluate the equity in the unearned premium, the unearned premium deficiency and the deferred policy acquisition expenses. Acknowledgement The authors would like to express their gratitude to Jean-Luc Allard, Richard Belleau, Jean Côté, Bernard Dupont and Betty-Jo Hill for their thorough review and constructive comments.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".