Features to Involve and Collect in Future Auto Insurance Pricing
Year: 2024
Type: article
Abstract: The property insurance industry is a highly data-driven sector. In the traditional model, insurance pricing largely depends on the self-reported risk characteristics of clients, such as age, gender, vehicle type, etc. These characteristics help insurance companies categorize clients into different risk levels and set premiums accordingly. With the development of telematics, insurance companies can now collect and analyze more dynamic data that is more directly related to risk. This study presents a comprehensive analysis of three feature selection models for vehicle insurance pricing. Utilizing regression techniques, it evaluated a multitude of factors believed to influence claim frequency and severity. Through a rigorous comparative study, key factors that significantly impact the vehicle are assessed and fit to different regression models. The article’s findings indicate that factors such as policy bonus, mileage base data, and car usage are paramount in determining insurance rates. Moreover, the study revealed that incorporating these factors into the pricing model enhances its accuracy and fairness.
Author: Shuang Guo
Institution: Washington University in St. Louis
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4403846236