Development of Telematics Safety Scores in Accordance with Regulatory Compliance
Bibliographic record
Abstract
The paper proposes a ratemaking framework for claim frequency that uses informative telematics data and complies with a “discount-only” regulatory requirement of the sort proposed in the 2023–2024 session of the New York State Assembly. The proposed framework uses a feedforward neural network to extract a one-dimensional safety score from multidimensional telematics features and integrates that score with traditional features in generalized linear models (GLMs). To meet the discount-only requirement, we impose constraints on the safety score and its regression parameter. The results show that the proposed models, with a suitable safety score function, can outperform a standard GLM in both in-sample goodness of fit and out-of-sample prediction performance. Furthermore, the analysis reveals that while the discount-only constraint may drive insurers to raise base premiums to offset revenue losses from the relativity cap, the regulation could achieve its intended goal in scenarios with strong favorable selection. This work was supported by a 2024 Individual Research Grant from the Casualty Actuarial Society. Address for Correspondence: himchan_jeong@sfu.ca
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".