Victor Masese is a masters student at the University of Nairobi majoring in Actuarial Science. His research focuses on the application of Generalized linear models in pricing usage-based insurance; an idea that was enhanced while working at Heritage Insurance as an Actuarial intern. He holds a bachelor’s degree in Actuarial Science from Jomo Kenyatta University of Agriculture and Technology. He hopes to extend his knowledge and skills into the Insurance industry.
Application of Generalized Linear Models in Pricing Usage-Based Insurance
Technological advancements and big data adaptations are broadly impacting the insurance industry. Usage Based Insurance (UBI) is a result of the emerging technologies and big data adaptation as it is based on telematics data which is captured and relayed in real-time by telematics devices installed in insured cars. Consequently, data transmitted to insurers regarding policyholders by telematics devices is increasing at an exponential rate thus necessitating big data adaptation. Actuaries use Generalized Linear Models (GLMs) as the insurance industry’s standard method for determining auto premium rates. This study focuses on estimating the impact of how and when driving is done on premium rates charged by applying Generalized Linear Models (GLMs). The factors considered in analysis include average speed, distance driven and time of day. From the insurance portfolio analyzed, the pure premium increases with an increase in the speed and distance covered while driving during the day more than night decreases the pure premium. These findings are representative of auto insurance policies covered but 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 driving behavior.