arbiter
JournalsPricingAccount

Big data, risk classification, and privacy in insurance markets

Martin Eling et al.

The Geneva Risk and Insurance Review2024https://doi.org/10.1057/s10713-024-00098-5article
AJG 2ABDC B
Weight
0.74

What the paper says

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.

11 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1057/s10713-024-00098-5

Or copy a formatted citation

@article{martin2024,
  title        = {{Big data, risk classification, and privacy in insurance markets}},
  author       = {Martin Eling et al.},
  journal      = {The Geneva Risk and Insurance Review},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1057/s10713-024-00098-5},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Big data, risk classification, and privacy in insurance markets

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.74

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact1.00 × 0.4 = 0.40
M · momentum0.78 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.