Insurtech, sensor data, and changes in customers' coverage choices: Evidence from usage‐based automobile insurance
Bibliographic record
Abstract
Abstract In this paper, we examine the role of usage‐based auto insurance on customers' decisions to change their insurance coverage at the renewal. Using a sample of 135,540 customers, we study whether usage‐based insurance (UBI) can facilitate the upselling and cross‐selling efforts of the firm, possibly leading to higher coverage choices and additional insurance product purchases. Our results suggest that UBI customers are more likely to change their coverage choice than non‐UBI customers at first (but not second) renewal. Both price discounts and the information provided by UBI affect the customers' coverage changes. Among UBI customers, those who get higher UBI discounts are more likely to both increase their insurance coverage (upselling) and add the comprehensive coverage option (cross‐selling), which is not directly related to driving behavior at the time of first (annual) renewal. Moreover, customers who have received more negative feedback (daily hard brakes) are more likely to increase their insurance coverage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".