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Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation

Author

Listed:
  • Farha Usman

    (School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2050, Australia
    These authors contributed equally to this work.)

  • Jennifer S. K. Chan

    (School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2050, Australia
    These authors contributed equally to this work.)

  • Udi E. Makov

    (Department of Statistics, University of Haifa, Haifa 3103301, Israel)

  • Yang Wang

    (School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2050, Australia)

  • Alice X. D. Dong

    (Transdisciplinary School, University of Technology Sydney, Ultimo, NSW 2007, Australia)

Abstract

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies.

Suggested Citation

  • Farha Usman & Jennifer S. K. Chan & Udi E. Makov & Yang Wang & Alice X. D. Dong, 2024. "Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation," Risks, MDPI, vol. 12(9), pages 1-33, August.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:9:p:137-:d:1466155
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    References listed on IDEAS

    as
    1. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    2. Montserrat Guillen & Jens Perch Nielsen & Ana M. Pérez-Marín & Valandis Elpidorou, 2020. "Can Automobile Insurance Telematics Predict the Risk of Near-Miss Events?," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(1), pages 141-152, January.
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    4. Min Deng & Mostafa S. Aminzadeh & Banghee So, 2024. "Inference for the Parameters of a Zero-Inflated Poisson Predictive Model," Risks, MDPI, vol. 12(7), pages 1-18, June.
    5. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
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