Financial Stability of European Insurance Companies during the COVID-19 Pandemic
Bibliographic record
Abstract
The European Insurance and Occupational Pensions Authority suggests that as the coronavirus disease 2019 (COVID-19) pandemic has caused significant disruption to the economy, businesses, and people’s lives, national supervisory authorities should mitigate the pandemic’s impact on the European insurance sector. The functioning of insurance companies is in danger as they must balance a drastic increase in the number of claims with their capital and solvency stability. In this study, we evaluate the effects of the COVID-19 pandemic on insurance companies using European insurance companies’ financial statement data from 2010 to 2020. The results unambiguously demonstrate that the pandemic has negatively affected the functioning of the insurance sector. In particular, the return on assets decreased in German and Italian insurance companies during the pandemic. Furthermore, the solvency ratio decreased in the Belgian, French, and German insurance sectors. Conversely, the Polish insurance sector was unaffected. Moreover, we did not find any effects on the Z-score ratio in our sample. Lastly, the value of receivables owed to Belgian insurance companies increased. Based on this evidence, we argue that European legislators should discuss how to manage the probable financial problems of insurance companies during the COVID-19 pandemic.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".