Big data, risk classification, and privacy in insurance markets

Martin Eling (), Irina Gemmo, Danjela Guxha and Hato Schmeiser
Additional contact information
Martin Eling: University of St. Gallen
Irina Gemmo: HEC Montréal
Danjela Guxha: University of St. Gallen
Hato Schmeiser: University of St. Gallen

The Geneva Risk and Insurance Review, 2024, vol. 49, issue 1, No 5, 75-126

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.

Keywords: Big data; Digitalization; Privacy costs; Risk classification (search for similar items in EconPapers)
JEL-codes: D43 D81 D82 G22 I13 O31 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1057/s10713-024-00098-5

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