The New York State Department of Financial Services recently issued guidelines for life insurance companies that plan to use social media, and other available online data resources, when underwriting new policies and establishing premiums.
While it’s never been illegal to do so, new advances in technology and the proliferation of third-party data vendors have made checking the social feeds of perspective customers simpler and faster. For insurance companies looking to make well-informed choices social media data may eventually become as useful and easy to come by as credit scores and criminal records.
New York legislators are obviously prepping for this future and how it might potentially come to loggerheads with existing discrimination laws that protect consumers based on factors such as race, faith and sexual orientation. There’s also the issue of consumer privacy legislation, which has been gaining more traction as the public comes to understand how their online information is being handled.
Up until now in the industry, monitoring social media accounts has usually come into play only when fraud is suspected. An Instagram feed filled with exciting skydiving pictures from a customer who otherwise appears to be a couch potato could raise some red flags. But now, as AI promises to speed up the process of accumulating and verifying such online data, social media feeds could potentially become part-and-parcel of the entire policy formulation process.
So what are the pros and cons of this?
An obvious pro is the speed and accuracy with which a policy could be issued, which is always a positive for the company.
And few will argue that information gleaned from a social media timeline will yield more authentic results than those coming from the industry’s time-honored actuary tables. Suddenly high-risk activities like smoking, drinking and weekend racecar driving are identifiable and verifiable.
The cons begin where the New York guidelines start. It suddenly becomes the insurers responsibility to make sure judgement calls made from online data don’t inadvertently cross into discrimination of protected classes. Eager for sales volume, that’s a gray line that a broker might easily cross.