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Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data

Abstract

We show how data collected from a GPS device can be incorporated in motor insurance ratemaking. The calculation of premium rates based upon driver behaviour represents an opportunity for the insurance sector. Our approach is based on count data regression models for frequency, where exposure is driven by the distance travelled and additional parameters that capture characteristics of automobile usage and which may affect claiming behaviour. We propose implementing a classical frequency model that is updated with telemetrics information. We illustrate the method using real data from usage-based insurance policies. Results show that not only the distance travelled by the driver, but also driver habits, significantly influence the expected number of accidents and, hence, the cost of insurance coverage. This paper provides a methodology including a transition pricing transferring knowledge and experience that the company already had before the telematics data arrived to the new world including telematics information.

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Notes

  1. 1.

    See Xu et al. (2015) for an extensive review of studies examining human mobility patterns in the field of transportation research.

  2. 2.

    The maximum age of the observed individuals is 37. Note that the insurance company that provided the sample sell this type of PAYD contract to young drivers.

  3. 3.

    We have used SAS PROC GENMOD to produce the model estimates and PROC IML to assess predictive performance.

  4. 4.

    The concept “excess of zeros” is a standard expression in the field of statistics that refers to situations where a large proportion of observations equal the value zero. This is the case in our data, many drivers did not report a claim in 1 year. It is likely that not all zeros are driven by the same rules. For instance, some may be due to a good driving style, while others may be caused by insureds that do not drive at all. Additionally the same (or different) set of explanatory variables might have varying effects on the two types of zeroes. For example, the car age may be a factor of danger thus leading to a larger number of claims, but at the same time having an old car may be associated to people who do not use the car much, so that they are likely to be occasional users and then the risk of a claim is lower.

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Acknowledgements

The study was supported by ICREA Academia, the Spanish Ministry of Economy and Competitiveness and the ERDF under Grants ECO2016-76203-C2-2-P and ECO2015-66314-R.

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Correspondence to Mercedes Ayuso.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Ayuso, M., Guillen, M. & Nielsen, J.P. Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data. Transportation 46, 735–752 (2019). https://doi.org/10.1007/s11116-018-9890-7

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Keywords

  • Tariff
  • Premium calculation
  • Pay-as-you-drive insurance
  • Count data models