Specific Features of the Application of IFRS 17—Valuation of Insurance Contracts and Profit and Loss Management
Bibliographic record
Abstract
The scope of this topic stems from the change in insurance companies and the subsequent transition to IFRS 17. The new code came into force on 1 January 2023. Therefore, the purpose of this article is to compare the two standards in terms of methodology and process logic. To highlight the new aspects of the new standard and to present the author’s view that IFRS 17 provides more opportunities for timely action and intervention by company management in the processes and improvement of results compared to IFRS 4. To examine how the application of the standard has affected the strategy for recognising, measuring, and reporting liabilities under insurance contracts, as well as financial results in the insurance sector in China. The study uses a mixed approach, combining a comparison of IFRS 4 and IFRS 17 with examples illustrating actual practice in the sector to examine differences in accounting treatment. It cites examples from European and Asian traders to assess how things will develop in practice. Contribution: This study adds new evidence on the impact of IFRS 17 on value and profit management. Our study found that the new standard introduces a single model for measuring insurance contracts, which significantly increases transparency and comparability in financial statements. Furthermore, one of its most important findings is that, with the equalisation of the margin on contractual services and the recognition of profits over the entire term of insurance contracts, the balance sheets for all years will show more consistent reports of profits and losses. It also calls for attention to the challenges insurers met in developing cash flow discounting methods or putting the general measurement model into effect. Overall, the report found that search engine IFRS 17 has made comparability and transparency better while making suggestions to industry stakeholders about what problems came out when they were discovered afterwards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".