I’ve had the pleasure of being in a few different industries now: call centers, industrial machinery, and now insurance. It’s always interesting to try to observe not just the differences between them but also their similarities. Specifically, the similarities in how these different industries are incorporating data into their vertical stacks.
Within insurance, data has probably a more direct impact than any other I’ve seen because it’s based almost entirely on math, at the end of the day. You have to take in more than you pay out.
Here are a few areas where there is just so much potential for data in the insurance industry, and some interesting things different people are doing with it.
Using data to underwrite better
Boils down to taking more good risks than bad
- classic data science application, write algorithms that are smarter
- finding completely new sources to use such as drones, sensor data, and social media profiles
- partnering with third parties who have exclusive access to useful data including personnel histories, vehicle location, business profitability, and more
Ultimately all of those come
sources of proprietary loss data
Challenges:
- Lack of a process for figuring out how to use new data sets
- Getting new data sets into a data lake
Using data to simplify insurance for the consumer
Prefill data etc
Using data to simplify insurance for the consumer
