Frequency-severity experience rating based on latent Markovian risk profiles

Verschuren, Robert Matthijs

doi:10.1016/j.insmatheco.2022.09.007

Cited by 3 publications

(2 citation statements)

References 76 publications

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“…See e.g. Park et al (2018) and Oh et al (2020b, 2022. Including claim severities in a posteriori premium corrections is challenging, due for instance to the sometimes long settlement procedures in motor third-party liability insurance so that the final claim cost is not known at the end of the occurrence period when the amount of premium must be updated based on past experience.…”

Section: Discussionmentioning

confidence: 99%

“…See e.g. Dionne and Vanasse (1992) , Pinquet (1998), Gomez-Deniz (2016), Park et al (2018), Cheung et al (2021), Denuit and Lu (2021), Tzougas and Cerchiara (2021) and Verschuren (2022), as well as the references therein. To recognize that telematics data are recorded while the policyholders are driving, exactly as claim experience, Denuit et al (2019) and Corradin et al (2022) proposed to use multivariate credibility models (also called mixed models, in statistics) to capture the joint dynamics of telematics data and claim experience.…”

Section: Introductionmentioning

confidence: 99%

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Bivariate Poisson Credibility Model and Bonus–Malus Scale for Claim and Near-Claim Events

Simon,

Trufin,

Denuit

2024

North American Actuarial Journal

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With the emergence of telematics, huge amounts of data have become available to insurers, raising the question of their integration into motor insurance pricing. Near-claim events are dangerous events which could have triggered a claim. Typically, they correspond to harsh deceleration, acceleration or cornering. Based on the motor third-party liability insurance portfolio of an insurance company operating in Belgium, we highlight that deceleration events are the best candidates for characterizing near-claim events. Then, we propose to integrate these events in the pricing structure in addition to policyholder's claim history with the help of a bivariate Poisson-LogNormal credibility model. Finally, we design an original bonus-malus scale based on both claim and near-claim events.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bivariate Poisson Credibility Model and Bonus–Malus Scale for Claim and Near-Claim Events

Simon,

Trufin,

Denuit

2024

North American Actuarial Journal

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

Jiang,

Shi

2024

North American Actuarial Journal

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Joint discrete and continuous matrix distribution modeling

Bladt

Gardner

2023

Stochastic Models

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In this paper we introduce a bivariate distribution on R+ × N arising from a single underlying Markov jump process. The marginal distributions are phase-type and discrete phase-type distributed, respectively, which allow for flexible behavior for modeling purposes. We show that the distribution is dense in the class of distributions on R+ × N and derive some of its main properties, all explicit in terms of matrix calculus. Furthermore, we develop an effective EM algorithm for the statistical estimation of the distribution parameters. In the last part of the paper, we apply our methodology to an insurance dataset, where we model the number of claims and the mean claim sizes of policyholders, which is seen to perform favorably. An additional consequence of the latter analysis is that the total loss size in the entire portfolio is captured substantially better than with independent phase-type models.

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Frequency-severity experience rating based on latent Markovian risk profiles

Cited by 3 publications

References 76 publications

Bivariate Poisson Credibility Model and Bonus–Malus Scale for Claim and Near-Claim Events

Bivariate Poisson Credibility Model and Bonus–Malus Scale for Claim and Near-Claim Events

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

Joint discrete and continuous matrix distribution modeling

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