Actuarial study and statistical analysis of extreme wildfire insurance claims
Bibliographic record
Abstract
The wildfire season of 2023 was one of the most devastating in Canadian history and caused significant losses to the insurance industry. This study focuses on the large wildfire losses exceeding CAD 25 million in the last 16 years (2008-2023). For severity analysis, mainly due to the largest amount of wildfire losses reaching CAD 4 billion, heavy-tail distributions have been applied to find the best fit for the loss claims history. We also estimate loss amounts in different return periods. Traditional actuarial ratemaking methods in non-life insurance are based on the assumption of the absence of correlation between the frequency and severity of claims. In this thesis, for frequency analysis, logistic regression is used to investigate the relationship between the maximum temperature and the probability of extreme wildfire losses. Consistent with common sense, the probability of a large wildfire occurring is found to increase with increasing temperature. According to the requirements of Solvency II, we derive the 99.5% confidence interval for the claim frequency. Overall, there are not many published papers and available data on Canadian wildfire insurance losses. Therefore, this thesis lists some possible research directions in property and casualty insurance, reinsurance, and life insurance in the future research directions section.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".