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

Farha Usman (), Jennifer S. K. Chan (), Udi E. Makov, Yang Wang and Alice X. D. Dong
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Farha Usman: School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2050, Australia
Jennifer S. K. Chan: School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2050, Australia
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

Risks, 2024, vol. 12, issue 9, 1-33

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.

Keywords: usage-based insurance pricing; lasso regression; Poisson mixture model; ROC curve; experience rating auto insurance premium (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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