A Case Study of Unfair Discrimination within Auto Insurance Pricing Models
Session Number
Project ID: CMPS 36
Advisor(s)
Dr. Frank Quan, University of Illinois Urbana Champaign
Discipline
Computer Science
Start Date
17-4-2024 10:00 AM
End Date
17-4-2024 10:15 AM
Abstract
In the insurance industry, pricing algorithms are often closely guarded as trade secrets. So, when Allstate Insurance publicly disclosed detailed information about its new auto insurance pricing algorithm, it drew significant attention. It is worthwhile to meticulously examine the data, and further uncover if there are disparities that disproportionately affected consumers. Considering the national presence of Allstate Insurance, inspecting its pricing model could provide valuable insights into the broader landscape of auto insurance pricing models. Using the state-of-the-art correlation method, Chatterjee’s Correlation Coefficient, and Python programming, we conducted an analysis of Allstate’s public data in conjunction with Maryland Census data. This analysis revealed that there is a moderate positive correlation between the race of the consumer, their assigned risk level, and their suggested price increase, as determined by the pricing model. These results suggest that pricing bias is potentially a concern for the auto insurance industry and that greater regulation and oversight should be put into creating pricing algorithms to mitigate bias.
A Case Study of Unfair Discrimination within Auto Insurance Pricing Models
In the insurance industry, pricing algorithms are often closely guarded as trade secrets. So, when Allstate Insurance publicly disclosed detailed information about its new auto insurance pricing algorithm, it drew significant attention. It is worthwhile to meticulously examine the data, and further uncover if there are disparities that disproportionately affected consumers. Considering the national presence of Allstate Insurance, inspecting its pricing model could provide valuable insights into the broader landscape of auto insurance pricing models. Using the state-of-the-art correlation method, Chatterjee’s Correlation Coefficient, and Python programming, we conducted an analysis of Allstate’s public data in conjunction with Maryland Census data. This analysis revealed that there is a moderate positive correlation between the race of the consumer, their assigned risk level, and their suggested price increase, as determined by the pricing model. These results suggest that pricing bias is potentially a concern for the auto insurance industry and that greater regulation and oversight should be put into creating pricing algorithms to mitigate bias.