Automobile insurance classification ratemaking based on telematics driving data

Huang, Yifan; Meng, Shengwang

doi:10.1016/j.dss.2019.113156

Cited by 82 publications

(49 citation statements)

References 25 publications

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“…Over the last decade, machine learning techniques have won a lot of attention in the area of insurance analytics (Denuit et al 2019a(Denuit et al , 2019bQuan and Valdez 2018). Machine learning methods are applied in the context of ratemaking (Dalkilic et al 2009;Huang and Meng 2019;Lowe and Pryor 1996;Pelessoni and Picech 1998;Richman 2018), fraud detection (Li et al 2018;Wang and Xu 2018), extreme value theory (Velthoen et al 2021), forecasting (Perla et al 2020), and in the explanation of the lapse behavior of customers (Guelman et al 2012;Hu et al 2020;Staudt and Wagner 2020), among others. While such models are used to select relevant risk factors and automate the creation of categories for continuous variables (Dougherty et al 1995;Henckaerts et al 2018), full-pricing applications are scarce, see, e.g., Guelman (2012) and Henckaerts et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Staudt

Wagner

2021

Risks

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For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.

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Section: Introductionmentioning

confidence: 99%

Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Staudt

Wagner

2021

Risks

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“…However, the drivers in their data set were all under the age of 30. Huang and Meng [40] collected 30 driving behavior variables derived from telematics data, combining different models to classify risk and predict claim frequency. ey verified the potential of driving behavior variables and machine learning in the UBI context.…”

Section: Ubi Pricingmentioning

confidence: 99%

“…We collected collision samples from January 1, 2019, to March 31, 2020, and then collected the corresponding vehicle data from the time of the collision to a prior year. To assess driver's risk precisely, we categorized collisions as severe or general, unlike previous studies, which made no such distinction [40]. A severe collision is defined as one in which airbags were deployed during the crash, which can be detected using IoV information.…”

Section: Data Descriptionmentioning

confidence: 99%

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Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

Zhao

Dai

2021

Mobile Information Systems

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With the development of big data technologies, usage-based insurance (UBI) has received considerable attention from insurance companies. UBI products focus on identifying the relationship between the individual driver’s risk and online channel behavior variables from Internet of Vehicles (IoV) data. Although omnichannel information integration has promoted the development of many industries, it has not been used to improve the accuracy of driver risk classification models in insurance industries. This paper investigates the role of combining different channel variables in improving the classification of driver’s risk. Specifically, several models, including logistic regression and three different data mining techniques (neural networks, random forests, and support vector machines), augmented with driving behavior data based on the IoV and offline consumer behavior data collected from 4S (Sale, Spare part, Service, Survey) dealers, are applied to the classification model of risk. The empirical results show that the inclusion of online and offline channel data improves the different risk assessments; results also demonstrate the importance of offline consumer behavior variables in different models. These insights have important implications for insurance companies on UBI pricing strategy and cost management.

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“…[3] described the partnership of a system integrator and auto insurers based on the use of telematic devices, which in turn created a profitable business venture. This has opened an avenue for a number of data mining [30] and risk modeling approaches [31] based on the close ties between system integrators and the risk and contract management departments of insurers.…”

Section: The Dissection Of Relationships Among Main Stakeholdersmentioning

confidence: 99%

An Acceptance Approach for Novel Technologies in Car Insurance

et al. 2020

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Background: Unlike other financial services, technology-driven changes in the insurance industry have not been a vastly explored topic in scholarly literature. Incumbent insurance companies have hitherto been holding their positions using the complexity of the product, heavy regulation, and gigantic balance sheets as paramount factors for a relatively slow digitalization and technological transformation. However, new technologies such as car telematic devices have been creating a new insurance ecosystem. The aim of this study is to assess the telematics technology acceptance for insurance purposes. Methods: The study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT). By interviewing 502 new car buyers, we tested the factors that affect the potential usage of telematic devices for insurance purposes. Results: The results indicate that facilitating conditions are the main predictor of telematics use. Moreover, privacy concerns related to the potential abuse of driving behavior data play an important role in technology acceptance. Conclusions: Although novel insurance technologies are mainly presented as user-driven, users (drivers and insurance buyers) are often neglected as an active party in the development of such technologies.

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Automobile insurance classification ratemaking based on telematics driving data

Cited by 82 publications

References 25 publications

Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

An Acceptance Approach for Novel Technologies in Car Insurance

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