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Auto Insurance Pricing Using Telematics Data: Application of a Hidden Markov Model

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  • Qiao Jiang
  • Tianxiang Shi

Abstract

This study develops a hidden Markov model (HMM)-based clustering framework to predict auto insurance losses using driving characteristics extracted from telematics data. Through a simulation experiment based on a proprietary telematics dataset, we show that HMM can effectively classify driving trips using model-implied hidden states, and HMM-based pricing methods provide better predictive power measured by deviance statistics. Importantly, the proposed framework not only enables us to price usage-based insurances at a granular level but is also viable for estimating long-term insurance losses utilizing the limiting properties of HMM.

Suggested Citation

  • Qiao Jiang & Tianxiang Shi, 2024. "Auto Insurance Pricing Using Telematics Data: Application of a Hidden Markov Model," North American Actuarial Journal, Taylor & Francis Journals, vol. 28(4), pages 822-839, October.
    • Handle: RePEc:taf:uaajxx:v:28:y:2024:i:4:p:822-839
    DOI: 10.1080/10920277.2023.2285977
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