Pricing weekly motor insurance drivers' with behavioral and contextual telematics data.

Autor:	Guillen M; Departament d'Econometria, Estadística i Economia Aplicada, Universitat de Barcelona (UB), Av. Diagonal, 690, 08034, Barcelona, Spain.; RISKcenter-Institut de Recerca en Economia Aplicada (IREA), Universitat de Barcelona (UB), Av. Diagonal, 690, 08034, Barcelona, Spain., Pérez-Marín AM; Departament d'Econometria, Estadística i Economia Aplicada, Universitat de Barcelona (UB), Av. Diagonal, 690, 08034, Barcelona, Spain.; RISKcenter-Institut de Recerca en Economia Aplicada (IREA), Universitat de Barcelona (UB), Av. Diagonal, 690, 08034, Barcelona, Spain., Nielsen JP; Bayes Business School. City, University of London, 106 Bunhill Row, London, EC1Y 8TZ, United Kingdom.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2024 Aug 18; Vol. 10 (16), pp. e36501. Date of Electronic Publication: 2024 Aug 18 (Print Publication: 2024).
DOI:	10.1016/j.heliyon.2024.e36501
Abstrakt:	Telematics boxes integrated into vehicles are instrumental in capturing driving data encompassing behavioral and contextual information, including speed, distance travelled by road type, and time of day. These data can be amalgamated with drivers' individual attributes and reported accident occurrences to their respective insurance providers. Our study analyzes a substantial sample size of 19,214 individual drivers over a span of 55 weeks, covering a cumulative distance of 181.4 million kilometers driven. Utilizing this dataset, we develop predictive models for weekly accident frequency. As anticipated based on prior research with yearly data, our findings affirm that behavioral traits, such as instances of excessive speed, and contextual data pertaining to road type and time of day significantly aid in ratemaking design. The predictive models enable the creation of driving scores and personalized warnings, presenting a potential to enhance traffic safety by alerting drivers to perilous conditions. Our discussion delves into the construction of multiplicative scores derived from Poisson regression, contrasting them with additive scores resulting from a linear probability model approach, which offer greater communicability. Furthermore, we demonstrate that the inclusion of lagged behavioral and contextual factors not only enhances prediction accuracy but also lays the foundation for a diverse range of usage-based insurance schemes for weekly payments. Competing Interests: M.G. has received funds from insurance companies, but the funding organisations had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The authors declare no other potential conflicts of interest. (© 2024 The Authors. Published by Elsevier Ltd.)
Databáze:	MEDLINE
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