Everything from what magazines you buy to how much television you watch could be used by insurance companies to determine whether you’re a risky client or not, and when you might die.
Traditionally used for advertising, a company is shopping around to insurance companies data-mined customer profiles as a way to evaluate risk. In one example showcased in a recent PowerPoint before the Society of Actuaries, the company showed two hypothetical customers, “Sarah” and “Beth.”
Sarah reads design and travel mags, has a short commute, runs and bikes, and watches little TV.
Beth has a 45-mile commute, divorced with no kids, buys fast food and diet and weight loss products, and is and avid TV watcher.
Based on that, the consulting firm would recommend that the insurance company go after Sarah with new business and retention efforts and quickly give her a preferred policy. On the other hand, they would recommend not sending Beth offers or using more aggressive retention methods, and would recommend getting more tests on her.
Testing out the system, one insurer said it was pretty “persuasive” in how close it came to the results they get with traditional underwriting techniques.
Data mining has long been used for targeted advertising, but if this method was implemented for insurance, it’s questionable whether it would be subject to the Fair Debt Collection Practices Act.
Even if you’re careful about what information you put out there, it’s hard when companies do backend deals to sell your data all over the place. You never know where it’s going to end up.
Here’s the source powerpoint, “Improving Life Insurer Performance with Predictive Analytics” (PDF) (skip to page 18) [SOA]