Development of Telematics Safety Scores in Accordance with Regulatory Compliance
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
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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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 | 0.001 |
| 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 it