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

Qiao Jiang and Tianxiang Shi

North American Actuarial Journal, 2024, vol. 28, issue 4, 822-839

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.

Date: 2024
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DOI: 10.1080/10920277.2023.2285977

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