The Use of Telematics Devices to Improve Automobile Insurance Rates

Abstract

Most automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyse information on exposure to risk and driving habits using telematics data from a Pay-as-you-Drive sample of insureds. We include distance travelled per year as part of an offset in a zero-inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance travelled, so that longer driving should result in higher premium, but there should be a discount for drivers that accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.

Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression

“…Here, pay-as-you-drive (PAYD) insurance schemes represent an alternative method for pricing premiums based on personal mileage travelled and driving behaviors. Guillen et al (2019), Verbelen et al (2018, and Pérez-Marín and Guillén (2019) showed the potential benefits of analyzing telematics information when calculating motor insurance premiums. Gao and Wüthrich (2019) analyzed high-frequency GPS location data (second per second) of individual car drivers and trips.…”

Quantile Regression with Telematics Information to Assess the Risk of Driving above the Posted Speed Limit

“…Indeed, safety measures and even telematics-based insurance should segment the population of drivers accordingly. Guillen et al (2019) confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. Gao et al (2019b) analyzed the driving characteristics at different speeds and their predictive power for claims frequency modeling.…”

“…It has been shown that, compared to women, men present riskier driving behavior, driving more kilometers per day, during the night, and at speeds above the limit. All these factors have been shown to be related to a greater number of accidents (Gao et al 2019a;Gao and Wüthrich 2019;Guillen et al 2019). For example, Paefgen et al (2014) found that the risk of accident is higher at nightfall, during the weekends on urban roads, and at low-range (0-30 km/h) or high-range speeds (90-120 km/h).…”

Predicting Motor Insurance Claims Using Telematics Data—XGBoost vs. Logistic Regression

“…Here, pay-as-you-drive (PAYD) insurance schemes represent an alternative method for pricing premiums based on personal mileage travelled and driving behaviors. Guillen et al (2019), Verbelen et al (2018), and Pérez-Marin and show the potential benefits of analyzing telematics information when calculating motor insurance premiums. Further, Hultkrantz et al (2012) highlight the importance of PAYD insurance plans insofar as they allow insurance companies to personalize premium calculation and, so, charge fairer rates.…”