TWO INCOMPATIBLE OBJECTIVES WITH INDIVIDUAL RESERVE \nMODELS: AN APPROACH WITH MULTIVARIATE ADAPTATIVE \nREGRESSION SPLINE MODELS
Bibliographic record
Abstract
Although individual techniques are usually proposed as alternatives to collective methods for the computation of actuarial liabilities in financial statements, individual methods could also be used to dynamically track the liabilities of an insurance company at any time, or to have a precise estimate of ultimate costs of all opened claims in a portfolio.However, in this paper we show that reserves methods used to estimate the overall liability on an insurer cannot be similar to individual techniques used to have a precise estimation.Indeed, when the insurer expect that the average cost of a claim will increase over time since its opening, granular reserving methods cannot be used to satisfy these two objectives.Simulations are used to expose and show the overall problem.To illustrate the situation with real insurance data from a major Canadian insurance company, we develop a new granular reserving model based on Multivariate Adaptive Regression Spline (MARS) models, which are well known to have an interesting bias-variance trade-off.We show that the hinge functions used in the MARS model are useful for obtaining an analytical form of each individual reserve at any time.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".