Big data, risk classification, and privacy in insurance markets
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
Abstract The development of new technologies and big data analytics tools has had a profound impact on the insurance industry. A new wave of insurance economics research has emerged to study the changes and challenges those big data analytics developments engendered on the insurance industry. We provide a comprehensive literature review on big data, risk classification, and privacy in insurance markets, and discuss avenues for future research. Our study is complemented by an application of the use of big data in risk classification, considering individuals' privacy preferences. We propose a framework for analyzing the trade-off between the accuracy of risk classification and the discount offered to policyholders as an incentive to share private data. Furthermore, we discuss the conditions under which using policyholders' private data to classify risks more accurately is profitable for an insurer. In particular, we find that improving the accuracy of risk classification, if achieved by requiring the use of private data, does not necessarily provide an incentive for insurers to create more granular risk classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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