An Examination of Property & Casualty Insurer Solvency in Canada
Bibliographic record
Abstract
This paper provides both a qualitative and empirical analysis of insolvency experience in the Canadian property and casualty insurance industry. First, we provide a qualitative analysis of the differences between Canada and the U.S. that may help to explain the lower incidence of insolvency experience in Canada. These include differences in regulation and monitoring, such as the presence of a federal regulator and higher capital requirements, and differences in the environment, such as lower legal liability risk and less exposure to catastrophic risk. Second, we use logistic regression methodology and variables commonly used in U.S. studies of insurer insolvency prediction to test whether such models are able to predict insolvency for Canadian insurers. We include variables that attempt to capture some of the important differences between the Canadian and U.S. markets. The results suggest that only the profitability measure, return on assets, is found to be a statistically significant predictor of insolvency, and that result holds only one year prior to insolvency. This relationship is consistent with many previous studies on U.S. property and casualty insurer insolvency.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".