Applications of Population Sampling to Insurance Ratemaking and Reserving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper explores the underutilized application of population sampling in the realm of actuarial science, a field where these statistical methodologies have been traditionally overlooked. Focusing on two distinct applications within insurance ratemaking and reserving, we unveil innovative approaches to address challenges in actuarial contexts and provide valuable insights into advancing methodologies in the field. The first application introduces population sampling as a solution to the computational complexities inherent in credibility premium calculation, particularly under Bayesian regression models. By combining population sampling with surrogate modeling, we present a method to manage computation challenges effectively. The second application delves into incurred but not reported reserves, challenging the conventional Chain–Ladder method and individual reserving models by incorporating population sampling. Proposing a reserve estimator based on inverse probability weighting techniques, we demonstrate a statistically robust, distribution-free method for IBNR reserving, emphasizing the integration of granular policyholder information
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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 | 0.003 |
| 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