Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

Jan Reig Torra, Montserrat Guillen (), Ana M. Pérez-Marín, Lorena Rey Gámez and Giselle Aguer
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Jan Reig Torra: Department of Econometrics, Statistics and Applied Economics, RISKcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
Montserrat Guillen: Department of Econometrics, Statistics and Applied Economics, RISKcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
Ana M. Pérez-Marín: Department of Econometrics, Statistics and Applied Economics, RISKcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
Lorena Rey Gámez: Department of Econometrics, Statistics and Applied Economics, RISKcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain
Giselle Aguer: Department of Econometrics, Statistics and Applied Economics, RISKcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain

Risks, 2023, vol. 11, issue 3, 1-18

Abstract: Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also influence the probability of having an accident, as well as vehicle and personal characteristics. This paper uses a monthly panel data structure and the Poisson model to predict the expected frequency of claims over time. Some meteorological information is included. Two types of claims are considered separately: only those related to at-fault third-party liability accidents, and all types of claims including assistance on the road. A sample of drivers in Spain in 2018–2019 is analysed with information on claiming frequency per month. Drivers were observed for seven months. Our analysis is novel because monthly summaries of telematics information are combined with weather data in a panel structure, revealing that external factors affect the expected claims frequencies. Reckless speeding behaviours and intense urban circulation increase the risk of an accident, which also increases with windy conditions.

Keywords: motor insurance; predictive models; telematics data; contextual data; at-fault claims (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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