, Volume 46, Issue 3, pp 735–752 | Cite as

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

  • Mercedes AyusoEmail author
  • Montserrat Guillen
  • Jens Perch Nielsen


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.


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



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.

Compliance with ethical standards

Conflict of interest

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


  1. Abdel-Aty, M., Ekram, A., Huang, H., Choi, K.: A study on crashes related to visibility obstruction due to fog and smoke. Accid. Anal. Prev. 43(5), 1730–1737 (2011)CrossRefGoogle Scholar
  2. Alcañiz, M., Guillen, M., Santolino, M., Sánchez-Moscona, D., Llatje, O., Ramón, L.: Prevalence of alcohol-impaired drivers based on random breath tests in a roadside survey in Catalonia (Spain). Accid. Anal. Prev. 65, 131–141 (2014)CrossRefGoogle Scholar
  3. Aseervatham, V., Lex, Ch., Spindler, M.: How do unisex rating regulations affect gender differences in insurance premiums? Geneva Pap. Risk Insur. Issues Pract. 41, 128–160 (2016)CrossRefGoogle Scholar
  4. Ayuso, M., Guillen, M., Alcañiz, M.: The impact of traffic violations on the estimated cost of traffic accidents with victims. Accid. Anal. Prev. 42, 709–717 (2010)CrossRefGoogle Scholar
  5. Ayuso, M., Guillen, M., Perez-Marin, A.M.: Time and distance to first accident and driving patterns of young drivers with pay-as-you-drive insurance. Accid. Anal. Prev. 73, 125–131 (2014)CrossRefGoogle Scholar
  6. Ayuso, M., Guillen, M., Perez-Marin, A.M.: Using GPS data to analyse the distance travelled to the first accident at fault in pay-as-you-drive insurance. Transportation Research Part C: Emerging Technologies 68, 160–167 (2016a)CrossRefGoogle Scholar
  7. Ayuso, M., Guillen, M., Perez-Marin, A.M.: Telematics and gender discrimination: some usage-based evidence on whether men’s risk of accidents differs from women’s. Risks 4(2), 1–10 (2016b)CrossRefGoogle Scholar
  8. Baecke, P., Bocca, L.: The value of vehicle telematics data in insurance risk selection processes. Decis. Support Syst. 98, 69–79 (2017)CrossRefGoogle Scholar
  9. Boucher, J.P., Denuit, M., Guillen, M.: Number of accidents or number of claims? An approach with zero inflated Poisson models for panel data. J. Risk Insur. 76(4), 821–846 (2009)CrossRefGoogle Scholar
  10. Boucher, J.P., Guillen, M.: A survey on models for panel count data with applications to insurance. RACSAM-Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas 103(2), 277–294 (2009)CrossRefGoogle Scholar
  11. Boucher, J.P., Perez-Marin, A.M., Santolino, M.: Pay-as-you-drive insurance: the effect of the kilometers on the risk of accident. Anales del Instituto de Actuarios Españoles 19, 135–154 (2013)Google Scholar
  12. Denuit, M., Maréchal, X., Pitrebois, S., Walhin, J.F.: Actuarial modelling of claim counts: risk classification, credibility and bonus-malus systems. Wiley, New York (2007)CrossRefGoogle Scholar
  13. Edlin, A.S.: Per-mile premiums for auto insurance. In: Arnott, R., Greenwald, B., Kanbur, R., Nalebuff, B. (eds.) Economics for an imperfect world: essays in honor of Joseph E. Stiglitz. MIT Press, Cambridge (2003)Google Scholar
  14. Elias, W., Toledo, T., Shiftan, Y.: The effect of daily-activity patterns on crash involvement. Accid. Anal. Prev. 42(6), 1682–1688 (2010)CrossRefGoogle Scholar
  15. Ellison, A.B., Bliemer, M.C.J., Greaves, S.P.: Evaluating changes in driver behaviour: a risk profiling approach. Accid. Anal. Prev. 75, 298–309 (2015)CrossRefGoogle Scholar
  16. Ferreira, J., Minikel, E.: Measuring per mile risk for pay-as-you-drive auto insurance. Transp. Res. Rec. J. Transp. Res. Board 2297(10), 97–103 (2013)Google Scholar
  17. Gourieroux, C., Monfort, A., Trognon, A.: Pseudo maximum likelihood methods: theory. Econometrica 52(3), 681–700 (1984a)CrossRefGoogle Scholar
  18. Gourieroux, C., Monfort, A., Trognon, A.: Pseudo maximum likelihood methods: applications to Poisson models. Econometrica 52(3), 701–720 (1984b)CrossRefGoogle Scholar
  19. Hassan, H., Abdel-Aty, M.: Predicting reduced visibility related crashes on freeways using real-time traffic flow data. J. Saf. Res. 45, 29–36 (2013)CrossRefGoogle Scholar
  20. Isaacson, M., Shoval, N., Wahl, H.W., Oswald, F., Auslander, G.: Compliance and data quality in GPS-based studies. Transportation 43(1), 25–36 (2016)CrossRefGoogle Scholar
  21. Jun, J., Guensler, R., Ogle, J.: Differences in observed speed patterns between crash-involved and crash-not-involved drivers: application of in-vehicle monitoring technology. Transp. Res. Part C 19, 569–578 (2011)CrossRefGoogle Scholar
  22. Langford, J., Koppel, S., McCarthy, D., Srinivasan, S.: In defence of the’low-mileage bias’. Accid. Anal. Prev. 40(6), 1996–1999 (2008)CrossRefGoogle Scholar
  23. Lemaire, J., Park, S.C., Wang, K.C.: The use of annual mileage as a rating variable. ASTIN Bull. 46(1), 39–69 (2016)CrossRefGoogle Scholar
  24. Litman, T.: Pay-as-you-drive pricing and insurance regulatory objectives. J. Insur. Regul. Natl. Assoc. Insur. Comm. 23(3), 35–53 (2005)Google Scholar
  25. Lokshin, M., Newson, R.B.: Impact of interventions on discrete outcomes: maximum likelihood estimation of the binary choice models with binary endogenous regressors. Stata J. 11(3), 368–385 (2011)CrossRefGoogle Scholar
  26. Newson, R. B.: Somers’ D: a common currency for associations. In: United Kingdom Stata Users’ Group Meetings 2015, No. 01, Stata Users Group (2015)Google Scholar
  27. Paefgen, J., Staake, T., Fleisch, E.: Multivariate exposure modelling of accident risk: insights from pay-as-you-drive insurance data. Transp. Res. Part A Policy Pract. 61, 27–40 (2014)CrossRefGoogle Scholar
  28. Paefgen, J., Staake, T., Thiesse, F.: Evaluation and aggregation of pay-as-you-drive insurance rate factors: a classification analysis approach. Decis. Support Syst. 56, 192–201 (2013)CrossRefGoogle Scholar
  29. Shafique, M.A., Hato, E.: Use of acceleration data for transportation mode prediction. Transportation 42(1), 163–188 (2015)CrossRefGoogle Scholar
  30. Sivak, M., Luoma, J., Flannagan, M.J., Bingham, C.R., Eby, D.W., Shope, J.T.: Traffic safety in the U.S.: re-examining major opportunities. J. Saf. Res. 38(3), 337–355 (2007)CrossRefGoogle Scholar
  31. Tselentis, D.I., Yannis, G., Vlahogianni, E.I.: Innovative motor insurance schemes: a review of current practices and emerging challenges. Accid. Anal. Prev. 98, 139–148 (2017)CrossRefGoogle Scholar
  32. Underwood, G.: On-road behaviour of younger and older novices during the first six months of driving. Accid. Anal. Prev. 58, 235–243 (2013)CrossRefGoogle Scholar
  33. Vickrey, W.: Auto accidents, tort law, externalities and insurance: an economist’s critique. Law Contemp. Probl. 33(3), 464–487 (1968)CrossRefGoogle Scholar
  34. Xu, Y., Shaw, S.L., Zhao, Z., Yin, L., Fang, Z., Li, Q.: Understanding aggregate human mobility patterns using passive mobile phone location data: a home based approach. Transportation 42(4), 625–646 (2015)CrossRefGoogle Scholar
  35. Yan, X., Li, X., Liu, Y., Zhao, J.: Effects on foggy conditions on drivers’ speed control behaviors at different risk levels. Saf. Sci. 68, 275–287 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Econometrics, Riskcenter-IREAUniversitat de BarcelonaBarcelonaSpain
  2. 2.Cass Business SchoolCity, University of LondonLondonUK

Personalised recommendations