Fang Tian Modernizes UBI Ratemaking Model with Newest Study
Dr. Fang Tian, Seaver College’s assistant professor of Decision Science, recently published, “Ratemaking Model of Usage Based Insurance Based on Driving Behaviors Classification,” alongside her colleagues Zhishuo Liu and Mengjun Hao in the International Journal of Crowd Science.
The text demonstrates that usage based insurance (UBI) ratings should be established using the driving behavior classification model instead of the driving behavior score model. Tian and her collaborators claim that the traditional determination method for UBI considers mainly static information concerning vehicles and their drivers. This article offers a more dynamic and fair system from which to base future UBI ratings.
“There are two types of behaviors that we are measuring,” Tian shares. “One is the static factor – the driver’s gender, age, occupation, and vehicle age. The other is the dynamic – like mileage. So we would like to reevaluate these factors and try them to see if the current charge is reasonable or if it can be adjusted.”
By incorporating both static and dynamic variables into a single model, Tian’s research illuminates a more sophisticated, driver friendly system to help determine insurance rates. As a result, she feels as though this work has both academic and practical implications.
“The value of this work will definitely contribute to the literature [on the subject],” Tian explains. “It takes time for your paper to become a practice, but eventually it will adjust into the system.”
To learn more about Tian’s adjusted UBI model, visit the International Journal of Crowd Science website.