Data-Driven Services in Insurance: Potential Evolution and Impact in the Swiss Market
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
Using real-time customer data holds great potential for the insurance industry. The frequency and relevance of interactions can be improved to provide assistance in real time. Better prevention and risk management can significantly improve pricing and reduce losses. These changes, however, hold the potential for structural changes in the industry. This research aims at understanding the potential path of the development of services in insurance and the challenges faced by insurers. A panel of industry experts provided the industry’s view, which was then compared with the responses of 1542 Swiss retail customers. We find that customers have high trust in insurance companies and are open to purchasing additional services, particularly for prevention and assistance. Insurance companies, however, are currently focusing on cost improvement measures. Customers are open to sourcing services from other providers, suggesting that insurance companies need to evolve their approach to take advantage of the current market window.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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