Loss Reserves and the Employment Status of the Appointed Actuary
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Property/casualty (P/C) insurers are required to establish loss reserves for unpaid losses at the time that the loss has occurred or is reasonably expected to have occurred. We examine factors that may impact the accurate setting of loss reserves. These include the level of rate regulation faced by the insurer and the incentives to underestimate or overestimate reserves to improve financial ratios or improve solvency scores, to reduce earnings, to defer taxes, or to smooth earnings volatility in order to meet shareholder expectations. The employment status of the Appointed Actuary, that is, whether the Appointed Actuary is an employee of the firm or a consultant, may also impact reserve accuracy. Using a variety of regression models with data from 1995 to 2010, we examine the impact of these factors on the accuracy of reserves posted by Canadian P/C insurers. Our results provide no evidence of systematic differences in the magnitude or direction of loss reserve errors between insurers that use company actuaries versus those that use consultant actuaries. However, we find that for both consultant and company actuaries positive reserve errors are associated with increases in global stock market returns and decreases in unanticipated inflation. The insurance market cycle impacts reserve errors for company actuaries and not consultant actuaries. As well, our results indicate that as the proportion of short-tailed business increases in a company, consultant actuaries are more likely to over-reserve. Similar to many previous studies using U.S. data, we do not find strong evidence regarding insurers’ incentives to deliberately overstate or understate reserves: Loss reserves are relatively unbiased estimates of the true losses paid. Thus these findings should be welcome news to the actuarial profession in Canada and to the prudential regulator: The Appointed Actuary, regardless of employment status, provides objective and unbiased estimates of insurers’ largest liability.
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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 | 0.001 | 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 | 0.001 |
| 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