Self-Assessment of Driving Style and the Willingness to Share Personal Information
Bibliographic record
Abstract
The availability of better behavioral information about their customer portfolios holds the promise for different and more accurate pricing models for insurers. Changes in pricing, however, are always fraught with danger for insurers, as they enter long-term commitments with incomplete historical information. On the other hand, sharing personal information is still viewed with skepticism by consumers. Which type of personal information are consumers willing to share with insurers, and for what purpose? How would they like to be rewarded for this openness? For insurers, how will the transition shift their risk portfolios? This paper addresses these questions for auto insurance, particularly how the self-assessment of one’s driving style impacts this dynamic. In a survey of approximately 900 Swiss residents, we found that offering a compensation, especially premium discounts, but also services, significantly improves willingness to share information. Higher trust in insurance increases sharing. Women and younger people are more willing to share information. On the other hand, customers are less willing to disclose, to insurers, information not traditionally associated with insurance. The self-assessment of driving style also plays a significant role. More risk-averse driving styles are correlated with higher sharing. Conversely, riskier driving styles are correlated with lower sharing. This result is significant for insurers, as new data-driven pricing and services models should tend to attract less risky customer portfolios.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".