Using Big Data to Discriminate Charged Price in the Car Insurance Industry: Evidence from United States

Abstract

The purpose of this paper is to examine the gender-based discrimination in car insurance rates and whether the reasons provided by the car insurance companies for the different rates are valid or not. The paper studies the average annual premiums paid by men and women across different age groups from 16 years old to over 56 years old along with the percentage differences. Additionally, the concept of big data and how it is utilized by businesses to apply personalize price discrimination is investigated. The research design is conclusive and secondary data is used in both qualitative and quantitative forms. Qualitative data is collected from articles for the literature review and as for the quantitative data it is in the form of reports and surveys. The data shows that at lower age groups women pay less than men for car insurance but as the age increase men start paying less. The paper reaches a conclusion that gender is not necessarily a crucial risk- factor as the regular factors such as driving record can provided accurate risk determinants.