Application of generalized linear Models in Pricing Usage-Based Insurance
Abstract
Technological advancements and big data adaptations are broadly impacting the insurance
industry. Usage Based Insurance is a result of the emerging technologies and big
data adaptation as it is based on telematics data. Incorporating telematics data in auto
insurance pricing models reduces moral hazard and adverse selection phenomena which
arise from information asymmetry. Traditional auto insurance does not consider how
and when driving is done which is part of telematics data. Thus, there is need for insurers
to rede ne their pricing models and risk selection criteria to include telematics data.
This study aims to model claim frequency and severity using generalized linear models
in order to evaluate the impact of distance driven, speed and time of driving on premium
rates which are part of telematics data. Generalized linear models have been applied by
insurers in ratemaking, reserving and underwriting general insurance policies for over
50 years. The models allow for response variables with non-gaussian error distributions
hence suitable for modelling auto insurance claim frequency and severity. Speci cally, the
gamma and Poisson generalized models have been employed in this study. The gamma
has been used to model claim severity while claim frequency is modelled by the Poisson
model. From the insurance portfolio analyzed, speed and distance variables were found to
be signi cant while time was not signi cant in both models. Coe cient estimates for distance
categories were positive indicating positive correlation. Speed band categories had
negative estimate values indicating negative correlation. We observe that severity and
frequency increase with distance and speed. Similarly, the pure premium increases with
distance and speed. These ndings are representative of particular auto insurance policies
and do not represent a generalized trend. Results of this study can help auto insurance
industries to evaluate the risk of driving more precisely and come up with personalized
premiums for drivers based on their real time driving factors.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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