Algorithms play a crucial role in determining everything from social media feeds to insurance premiums. However, as powerful as they are, these algorithms can inadvertently perpetuate biases, especially in auto insurance pricing. Traditionally, insurers rely on demographic markers and even credit scores to assess risk - variables that can unfairly penalize certain individuals and lead to discriminatory pricing.
Telematics, a technology that collects real-time driving data, offers a solution to this problem. By focusing on individual driving behavior, telematics provides a more accurate and equitable measure of risk. Instead of relying on broad demographic categories, insurers can now tailor premiums based on how safely a person drives, reducing the reliance on biased proxies to move towards fairer pricing models. This shift not only helps combat bias but also aligns with growing consumer expectations for personalization and transparency in insurance.
Telematics, however, hasn’t always been a perfect solution. According to Joel Pepera (pictured left), director of core telematics data science at Arity, the technology has followed a three-stage evolution to reach its current state:
According to Henry Kowal (pictured right), director of outbound product management at Arity, one of the primary advantages of telematics is its potential to address biases inherent in traditional auto insurance pricing models. Historically, insurance companies have relied on factors like credit scores, education, gender, and location to determine premiums, but these variables often fail to accurately represent an individual’s driving risk and can contribute to discriminatory practices.
A report by ProPublica revealed significant disparities in car insurance premiums in Illinois, particularly in lower-income and more ethnically diverse areas. The investigation found that drivers in minority neighborhoods were often charged up to 30% more than those in similarly risky, predominantly white areas.
Telematics shifts this focus by assessing individualized metrics such as speed, braking patterns, and mileage, allowing insurers to evaluate drivers based on their actual performance. Kowal raises an important issue: "If I’m being rated on where I live and, due to my economic situation, I find myself in a less preferred neighborhood, it raises the question: how does that relate to my actual driving behavior and the risk I pose?"
He further elaborated that driving is a controllable factor: “If I am not a very good driver, perhaps I can become a better driver through smartphone app programs offered by insurance companies. But if I am a good driver, I shouldn’t have to be grouped with others simply because we share demographic markers that are deemed unfavorable.”
Arity’s recent consumer surveys support this viewpoint, revealing that consumers generally prefer to be evaluated based on their driving behavior rather than on external, uncontrollable factors. Kowal emphasizes that these insights highlight the need for a more equitable insurance model—one that prioritizes individual driving habits over socioeconomic status.
By leveraging telematics, insurers can create a more equitable system that aligns premiums with real driving risks, ultimately fostering a fairer landscape for all drivers.
