Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models

Pocuca, Nikola; Jevtic, Petar; McNicholas, Paul D.; Miljkovic, Tatjana

doi:10.1016/j.insmatheco.2020.06.004

Cited by 13 publications

(5 citation statements)

References 40 publications

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“…Mixture of experts or model averaging are other flexible approaches to insurance pricing. Since these methods are not machine learning but statistical, we do not investigate further but highlight Fung et al (2019aFung et al ( , 2019b; Hu et al (2018Hu et al ( , 2019; Jurek and Zakrzewska (2008); Počuča et al (2020); Richman and V. Wüthrich (2020); Ye et al (2018). See Fung et al (2020) for an application in reserving.…”

Section: Conventional Pricingmentioning

confidence: 99%

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Blier-Wong

Cossette

Lamontagne

et al. 2020

Risks

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In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

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Section: Conventional Pricingmentioning

confidence: 99%

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Blier-Wong

Cossette

Lamontagne

et al. 2020

Risks

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“…Meanwhile, the clustering weighting model is crucial for examining the claim probability intensity. Počuča et al. (2020) proposed two important extensions for the clustering weighting model.…”

Section: Literature Reviewmentioning

confidence: 99%

Freight insurance pricing strategy based on an online freight platform

Yang,

Chen,

Xia

2023

IMDS

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PurposeThe development of digital technology has provided technical support to various industries. Specifically, Internet-based freight platforms can ensure the high-quality development of the logistics industry. Online freight platforms can use cargo transportation insurance to improve their service capabilities, promote their differentiated development, create products with platform characteristics and increase their core competitiveness.Design/methodology/approachThis study uses a generalised linear model to fit the claim probability and claim intensity data and analyses freight insurance pricing based on the freight insurance claim data of a freight platform in China.FindingsConsidering traditional pricing risk factors, this study adds two risk factors to fit the claim probability data, that is, the purchase behaviour of freight insurance customers and road density. The two variables can significantly influence the claim probability, and the model fitting outcomes obtained with the logit connection function are excellent. In addition, this study examines the model results under various distribution types for the fitting of the claim intensity data. The fitting outcomes under a gamma distribution are superior to those under the other distribution types, as measured by the Akaike information criterion.Originality/valueWith actual data from an online freight platform in China, this study empirically proves that a generalised linear model is superior to traditional pricing methods for freight insurance. This study constructs a generalised linear pricing model considering the unique features of the freight industry and determines that the transportation distance, cargo weight and road density have a significant influence on the claim probability and claim intensity.

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“…Other extensions include high dimensional covariates (Subedi et al 2013), non-linear functional relationships (Punzo 2014), detecting outliers using the contaminated normal distribution , and a general approach that allows various types of response variables as well as covariates of mixed-type (Ingrassia et al 2015). Počuča et al (2020) consider a further extension of Ingrassia et al (2015) by further splitting the continuous covariates into Gaussian and non-Gaussian covariates.…”

Section: Cluster Weighted Modelsmentioning

confidence: 99%

Multivariate Cluster Weighted Models Using Skewed Distributions

Gallaugher¹,

Tomarchio²,

McNicholas³

et al. 2021

Preprint

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Much work has been done in the area of the cluster weighted model (CWM), which extends the finite mixture of regression model to include modelling of the covariates.Although many types of distributions have been considered for both the response and covariates, to our knowledge skewed distributions have not yet been considered in this paradigm. Herein, a family of 24 novel CWMs are considered which allows both the covariates and response variables to be modelled using one of four skewed distributions, or the normal distribution. Parameter estimation is performed using the expectationmaximization algorithm and both simulated and real data are used for illustration.

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Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models

Cited by 13 publications

References 40 publications

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Freight insurance pricing strategy based on an online freight platform

Multivariate Cluster Weighted Models Using Skewed Distributions

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