Unraveling the Predictive Power of Telematics Data in Car Insurance Pricing

Abstract

A data set from a Belgian telematics product aimed at young drivers is used to identify how car insurance premiums can be designed based on the telematics data collected by a black box installed in the vehicle. In traditional pricing models for car insurance, the premium depends on self-reported rating variables (e.g. age, postal code) which capture characteristics of the policy(holder) and the insured vehicle and are often only indirectly related to the accident risk. Using telematics technology enables tailor-made car insurance pricing based on the driving behavior of the policyholder. We develop a statistical modeling approach using generalized additive models and compositional predictors to quantify and interpret the effect of telematics variables on the expected claim frequency. We find that such variables increase the predictive power and render the use of gender as a discriminating rating variable redundant.

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

“…Table 1 summarizes the available explanatory variables for the process of ratemaking (Frees 2015), i.e., building homogeneous classes of policyholders (see, e.g., Laas et al 2016;Staudt and Wagner 2018). Most of the factors are determined at the moment of underwriting of the contract (a priori variables, Denuit and Lang 2004;Denuit et al 2007;Verbelen and Antonio 2016). The age of the policyholder AG, the bonus-malus level BM, the horsepower of the vehicle HP, the value of the car VC, the value of the accessories as a percentage of the car value AP, the age AC, and weight WC of the car, as well as the longitude LO and latitude LA of the policyholder's main residence are continuous variables.…”

Bonus-Malus Scale models: creating artificial past claims history

“…That means that the BMS model NB1 with a BMS rating structure is the best model among those used. For the test dataset, the logarithmic score defined as n i=1 − log(Pr(n i ; λ i )) has been used (see Roel et al (2017) for details or description of other scores) to define the prediction quality. Results are similar regarding the ranking of the types of model, but for the underlying distribution, the NB2 distribution seems to always outperform the NB1.…”

Towards Explainability of Machine Learning Models in Insurance Pricing

“…Within the area of ratemaking, machine learning is still in its infancy. A significant portion of machine learning applications to ratemaking has been in the context of automobile telematics, such as Gao, Meng, and Wuthrich (2018), , Wuthrich (2019), Roel, Antonio, andClaeskens (2018), or Wuthrich (2017). Presumably this focus has been a result of the highdimensionality and complexity of telematics data, making it a field in which the unique abilities of machine learning techniques give a clear advantage over traditional approaches.…”