International Comparison of the Profit Distribution of Life Insurance Products and Earning Cycles of Life Insurance Companies——A Perspective of Financial Reporting Standards-based Liability Valuation
Bibliographic record
Abstract
Under the background of globalization,the process of integration of financial reporting standards among countries is not that smooth mainly because of discrepancies in liability valuation. The paper compared the liability valuation methods under life insurance financial reporting standards of China,the United States,Canada and Australia. It first simulated profit distribution modes under different standards using the cash flow model,and then constructed the analysis framework for earning cycles of insurance companies. It further simulated and compared earning cycles for newly-opened companies under different standards. The main finding was that the distinctive liability valuation methods of each country and the different actuarial practice standards fundamentally decided their profit distribution modes and earning cycles. For China,the current life insurance financial reporting standard reflected the concept of combining rules and principles. The double-margin design has showed initial effect and the earning cycle of medium-sized life insurance companies was reasonable. For the long earning cycles of some small life insurance companies,it was mainly due to their irrational product structure. Suggestions for liability valuation in the life insurance industry were provided at the end of the paper.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".