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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jan Reig Torra & Montserrat Guillen & Ana M. Pérez-Marín & Lorena Rey Gámez & Giselle Aguer, 2023. "Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models," Risks, MDPI, vol. 11(3), pages 1-18, March.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:3:p:57-:d:1092483
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    References listed on IDEAS

    as
    1. Jean-Philippe Boucher & Roxane Turcotte, 2020. "A Longitudinal Analysis of the Impact of Distance Driven on the Probability of Car Accidents," Risks, MDPI, vol. 8(3), pages 1-19, September.
    2. Francis Duval & Jean-Philippe Boucher & Mathieu Pigeon, 2022. "How Much Telematics Information Do Insurers Need for Claim Classification?," North American Actuarial Journal, Taylor & Francis Journals, vol. 26(4), pages 570-590, November.
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    Cited by:

    1. Thomas Poufinas & Periklis Gogas & Theophilos Papadimitriou & Emmanouil Zaganidis, 2023. "Machine Learning in Forecasting Motor Insurance Claims," Risks, MDPI, vol. 11(9), pages 1-19, September.

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