Dealing with Low Interest Rates in Life Insurance: An Analysis of Additional Reserves in the German Life Insurance Industry
Bibliographic record
Abstract
Interest rates have been very low for several years, which is particularly challenging for life insurers. Since 2001, German life insurers have had to set an additional reserve due to low interest rates to ensure the protection of policyholders. However, the method introduced at that time to calculate these reserves was criticized, therefore, the German Federal Ministry of Finance replaced it with a new approach. In this article, we investigated the effects of the different methods on a typical German life insurer in various future interest rate scenarios and from various perspectives. For this purpose, we modelled such a life insurer holistically, considered its asset liability management and projected its future development in different interest rate scenarios using simulation techniques. Taking into account dependencies between assets, liabilities and interest rates, we analyzed and discussed our results from the life insurer’s, equity holders’, policyholders’ and regulators’ perspectives. The results show that the new method eliminated the weaknesses of the previous one and seems to be a suitable alternative to determine the additional reserve.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".