The Association between Complexity and Managerial Discretion in the Property and Casualty Insurance Industry
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates whether the setting of loss reserves depends on an insurer’s complexity, which is defined by the number of business lines an insurer underwrites and on the insurer’s expertise in those lines. Our results suggest that insurers with higher levels of complexity tend to over-reserve. We also find that, as complexity increases, insurers that are financially weak and smooth their earnings, tend to under-reserve (i.e., bias their loss reserves upward). Further, we find that as complexity increases, insurers with high tax liabilities tend to bias their loss reserves downward (i.e., over-reserve), suggesting that tax strategies are important issues for insurers. An insurer’s degree of complexity is particularly salient when determining the extent to which loss reserves can be aggressively set.
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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) | 0.000 | 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 it