Measuring the acceptance rate of Usage-Based Insurance (UBI) based on statistical methods (case study: Saman Insurance Company)

Abstract

Usage-based Insurance (UBI) is an innovation that differs from traditional car insurance and seeks to distinguish between high-risk and low-risk drivers. The premium in this policy is calculated based on the distance traveled and telematics variables such as road type, time, speed, etc. This study measured the UBI acceptance rate and the factors that influence it. Global surveys and expert opinions were used to design a questionnaire, which was then administered to 396 randomly selected respondents, meeting the requirements of Cochran's formula for indeterminate populations (at least 384). Multinomial and binary logistic regression models were employed to measure acceptance and the willingness to purchase UBI based on distance, as well as distance and driving behaviors. These investigations were carried out across five and three scenarios, respectively, considering value-added services, awareness levels, and the importance of factors. Finally, a confirmatory factor analysis model was utilized to validate the UBI acceptance model, with the indicators affirming its appropriateness. The findings suggest the need for plans to enhance the information and awareness levels of insurance policyholders regarding UBI. Additionally, variables such as providing warnings to policyholders to improve driving, policy price, awareness of UBI, awareness of providing UBIs by some insurance companies in Iran, and providing rewards/discounts are identified as influential in driving UBI purchases, warranting investment by insurance companies to boost sales.

Keywords

• usage-based insurance (ubi)
• confirmatory factor analysis (cfa)
• telematics
• multinomial logistic regression model
• binary logistic regression model