The Risk in the Algorithm: How Insurance is Turning Personal Data into Policy Decisions

Insurance companies are no longer just looking at age, where you live, or how many accidents you’ve had. Now, they’re watching what you do every day—your habits, routines, even your mood—through devices you already own. From smart locks to fitness trackers and home sensors, insurers are pulling in real-time data to judge risk. This means your late-night visits to the door or how often you walk your dog could directly affect your premiums. It’s not just about risk anymore—it’s about behavior. And that shift is changing how people see insurance, making it feel more personal, more invasive, and less predictable.

The rise of connected devices has made this possible. Smartphones, thermostats, cars, even kitchen appliances are now constantly feeding data into networks that insurers can tap into. In some cases, companies are working directly with device makers to access this information. What used to be scattered and siloed is now a continuous stream of activity—every motion, every temperature change, every app opened. This flood of data allows insurers to build detailed profiles, but it also means people are being watched without always knowing it. Terms of service often slide past users unnoticed, and many don’t realize how deeply their daily lives are being recorded and analyzed.

Key Shifts in How Insurance Uses Personal Data

• Behavioural policy pricing: Uses real-time data from devices to assess risk based on actions—like fitness levels or home activity—rather than just demographics. A person who walks regularly might get lower rates, while someone with frequent late-night activity might face higher premiums.
• IoT as a data source: Devices like smart thermostats, security cameras, and cars generate vast amounts of continuous data. Insurers now tap into these systems to get a clearer picture of daily behavior, often through partnerships with device manufacturers.
• Privacy concerns and consent: People often agree to share data without fully understanding how it’s used. Terms of service are short, vague, and rarely explain data collection or sharing—making it hard to know what’s being stored or who has access.
• Algorithmic bias: The models used to analyze data are trained on past data that may reflect unfair patterns—like neighborhood differences or income gaps. This can result in pricing that feels unfair or even discriminatory, especially for certain groups.

Insurers need to be transparent about how data is used and who sees it. Without that, trust erodes—and the whole system risks becoming skewed, unbalanced, and unresponsive to real fairness.