A Population Sampling Framework for Claim Reserving in General Insurance
Bibliographic record
Abstract
Claim reserving in insurance has been studied through two primary frameworks: the macro-level approach, which estimates reserves at an aggregate level (e.g., Chain-Ladder), and the micro-level approach, which estimates reserves at the individual claim level Antonio and Plat (2014). These frameworks are based on fundamentally different theoretical foundations, creating a degree of incompatibility that limits the adoption of more flexible models. This paper introduces a unified statistical framework for claim reserving, grounded in population sampling theory. We show that macro- and micro-level models represent extreme yet natural cases of an augmented inverse probability weighting (AIPW) estimator. This formulation allows for a seamless integration of principles from both aggregate and individual models, enabling more accurate and flexible estimations. Moreover, this paper also addresses critical issues of sampling bias arising from partially observed claims data-an often overlooked challenge in insurance. By adapting advanced statistical methods from the sampling literature, such as double-robust estimators, weighted estimating equations, and synthetic data generation, we improve predictive accuracy and expand the tools available for actuaries. The framework is illustrated using Canadian auto insurance data, highlighting the practical benefits of the sampling-based methods.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".