Automobile Usage-Based-Insurance: : Improving Risk Management using Telematics Data

Cunha, Lourenco; Bravo, Jorge Miguel

doi:10.23919/cisti54924.2022.9820146

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The standard frequency-severity model has, however, some limitations. First, the model assumes a linear relationship between the response variable and the predictors, with empirical studies documenting nonlinear effects between, e.g., claim severity and the insured's age (Cunha and Bravo 2022;Frees and Valdez 2009). Alternative approaches using Generalized Additive Models (GAM) can overcome the linear predictor constraint of GLMs but have difficulty capturing the complex interactions among feature variables (Verbelen et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting

Clemente,

Guerreiro,

Bravo

2023

Risks

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Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, independence between the claim frequency and severity, and assign full credibility to the data. To overcome some of these restrictions, this paper investigates the predictive performance of Gradient Boosting with decision trees as base learners to model the claim frequency and the claim severity distributions of an auto insurance big dataset and compare it with that obtained using a standard GLM model. The out-of-sample performance measure results show that the predictive performance of the Gradient Boosting Model (GBM) is superior to the standard GLM model in the Poisson claim frequency model. Differently, in the claim severity model, the classical GLM outperformed the Gradient Boosting Model. The findings suggest that gradient boost models can capture the non-linear relation between the response variable and feature variables and their complex interactions and thus are a valuable tool for the insurer in feature engineering and the development of a data-driven approach to risk management and insurance.

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Section: Introductionmentioning

confidence: 99%

Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting

Clemente,

Guerreiro,

Bravo

2023

Risks

Self Cite

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Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective

Xie

Shi

2023

Mathematics

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Pricing using a Generalised Linear Model is the gold standard in the auto insurance industry and rate regulation. Generalised Additive Model applications in insurance pricing are receiving increasing attention from academic researchers and actuarial pricing professionals. The actuarial practice has constantly shown evidence of significantly different premium rates among the different rating territories. In this work, we build predictive models for claim frequency and severity using the synthetic Usage Based Insurance (UBI) dataset variables. First, we conduct territorial clustering based on each location’s claim counts and amounts by grouping those locations into a smaller set, defined as a cluster for rating purposes. After clustering, we incorporate these clusters into our predictive model to determine the risk relativity for each factor level. Through predictive modelling, we have successfully identified key factors that may be helpful for the rate regulation of UBI. Our work aims to fill the gap between individual-level pricing and rate regulation using the UBI database and provides insights on consistency in using traditional rating variables for UBI pricing. Our main contribution is to outline how GAM can address a more complicated functionality of risk factors and the interactions among them. We also contribute to demonstrating the territory clustering problem in UBI to construct the rating territories for pricing and rate regulation. We find that relativity for high annual mileage driven is almost three times that associated with low annual mileage level, which implies its importance in premium calculation. Overall, we provide insights into how UBI can be regulated through traditional pricing factors, additional factors from UBI datasets and rating territories derived from basic rating units and the driver’s location.

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Analyzing the Influence of Telematics-Based Pricing Strategies on Traditional Rating Factors in Auto Insurance Rate Regulation

Xie

2024

Mathematics

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This study examines how telematics variables such as annual percentage driven, total miles driven, and driving patterns influence the distributional behaviour of conventional rating factors when incorporated into predictive models for capturing auto insurance risk in rate regulation. To effectively manage the complexity inherent in telematics data, we advocate for the adoption of non-negative sparse principal component analysis (NSPCA) as a structured approach for data dimensionality reduction. By emphasizing sparsity and non-negativity constraints, NSPCA enhances the interpretability and predictive power of models concerning both loss severity and claim counts. This methodological innovation aims to advance statistical analyses within insurance pricing frameworks, ensuring the robustness of predictive models and providing insights crucial for rate regulation strategies specific to the auto insurance sector. Results show that, to enhance auto insurance risk pricing models, it is essential to address data dimension reduction challenges when integrating telematics data variables. Our findings underscore that integrating telematics variables into predictive models maintains the integrity of risk relativity estimates associated with traditional policy variables.

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Automobile Usage-Based-Insurance: : Improving Risk Management using Telematics Data

Cited by 4 publications

References 28 publications

Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting

Modelling Motor Insurance Claim Frequency and Severity Using Gradient Boosting

Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective

Analyzing the Influence of Telematics-Based Pricing Strategies on Traditional Rating Factors in Auto Insurance Rate Regulation

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