How Behavioral Data Should Impact Underwriting


At Verikai, we’re using data to transform the insurance underwriting process and ultimately provide our customers with the right tools to better analyze their clients’ risk. 

When explaining Verikai to a new prospective customer, family member, or a friend curious about what I do for work, I always share the same example. While my example is an oversimplification of how we truly use behavioral data, it provides a way to explain what we do in a relatable everyday situation. 

As we all know, the insurance rating manuals look at only four specific data points: age, gender, geography, and work industry. But with the use of behavioral data, we can look so much further beyond those four factors.

The typical insurance rating manual would identify a female in the age range of 19-35 at a higher risk due to the increased probability of pregnancy. This scenario really hits home for me. My female cousin and I were both born and raised in Syracuse, NY. We are the same age, falling between 19-35. In the same year, we both relocated to Raleigh, NC, to focus on career growth and plant new roots for our future. We lived in the same apartment complex with the same address.

Already, you can see we look the same to an insurance rating manual. 

However, my cousin and I lead entirely different lives. My cousin is married and beginning to start a family of her own with a baby on the way. She spends her time at work, staying active, and most importantly, preparing to become a first-time parent. 

While I am thrilled to become an aunt, I do not have these same plans to begin a family soon. Being new to the insurance technology industry, my main focus is growing my professional career in the next few years. I spend most of my time working, traveling for business, exercising, and spending time with friends.

As you can see, our lives look almost identical according to the four main insurance rating manual data points, but as we take a step further into our actual everyday behaviors – we lead very different lives. We had an actual example of a 25-year-old woman who was quoted at $318 PMPM (because she fit the pregnancy demographic), but with Verikai’s analysis we found she could actually be written as low as $44 PMPM. That’s a pretty drastic difference for all generally healthy young women to miss out on. And this example is one of many.

People who take care of themselves and aren’t planning large changes (like family planning or elective surgeries) should be rewarded for making healthy choices. Individuals should be fairly rated for what is going on in their lives at any given time, and incorporating behavioral data into underwriting is the solution.

Interested in learning more? Reach out to me on LinkedIn!

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