Claim prediction and premium pricing for telematics auto-insurance data using Poisson regression with lasso regularisation

33 Pages Posted: 5 Jul 2023 Last revised: 4 Apr 2024

See all articles by Farha Usman

Farha Usman

The University of Sydney

Jennifer Chan

The University of Sydney

Udi Makov

University of Haifa

Yang Wang

The University of Sydney

Alice Dong

The University of Sydney - School of Mathematics and Statistics

Date Written: August 15, 2024

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

Suggested Citation

Usman, Farha and Chan, Jennifer and Makov, Udi and Wang, Yang and Dong, Alice, Claim prediction and premium pricing for telematics auto-insurance data using Poisson regression with lasso regularisation (August 15, 2024). Available at SSRN: https://ssrn.com/abstract=4501573 or http://dx.doi.org/10.2139/ssrn.4501573

Farha Usman (Contact Author)

The University of Sydney ( email )

Jennifer Chan

The University of Sydney ( email )

University of Sydney
Sydney, NSW 2006
Australia
61293514873 (Phone)
2218 (Fax)

HOME PAGE: http://https://www.maths.usyd.edu.au/u/jchan/index.html

Udi Makov

University of Haifa ( email )

Yang Wang

The University of Sydney ( email )

University of Sydney
Sydney, 2006
Australia

Alice Dong

The University of Sydney - School of Mathematics and Statistics ( email )

Sydney, New South Wales 2006
Australia

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