The race to develop accelerated products has driven life insurers to cautiously embrace the next generation of data.
Underwriting in the US life insurance industry has had more change in the last five years than it has in the prior 30…and many underwriters are struggling to keep up with the pace. Terms like accelerated underwriting, automated underwriting, simplified issue, predictive models and big data are bounced around at industry meetings like ping pong balls. If you are confused by all the new terminology, you are not alone.
The cutting edge of the insurance industry involves adjusting premiums and policies based on new forms of surveillance.
So how do insurers unlock value from big data? Jeff Heaton, RGA’s Chief Data Scientist, published author and professor at Washington University in St. Louis, has a few ideas. To start, he suggests it’s time for insurers to better understand the basics of data science. To that end, he self-produced a video to explain the basics in just four minutes. RGA sat down with Heaton to discuss the video and his thoughts on what every employee at an insurance company should know about this form of statistics.
Indirect use of discrimination factors that are outlawed is inexcusable and needs to be avoided by due diligence by insurer and indeed the firm supplying the data. Any decent carrier would not want to cross those red lines anyway – and that is even if the data company involved is not itself subject to regulatory oversight and/or consumer protection laws. Moral: act with integrity and choose your business partners carefully.
Insurers are using customers’ social-media posts to determine premiums, inviting the potential for our digital lives to become disingenuous performances.
On 19 January 2019, the New York State Department for Financial Services (DFS) issued a circular letter concerning the use of external consumer data and information sources for life insurance underwriting. This followed a prior notice sent to insurers that the Department was investigating the use of such data for potentially unfair or discriminatory practices.
As the use of algorithms and public data to inform insurance premiums becomes more common, we’ll need to decide what is and isn’t okay
It’s a new day not very far in the future. You wake up; your wristwatch has recorded how long you’ve slept, and monitored your heartbeat and breathing. You drive to work; car sensors track your speed and braking. You pick up some breakfast on your way, paying electronically; the transaction and the calorie content of your meal are recorded.
Technological advances in biosensors and increasing amounts of heart rate data from wearable devices and electronic health records are leading to the development of more sophisticated underwriting algorithms. This data, when coupled with robust epidemiological evidence about the prognostic value of heart rate, may improve insurer understanding of cardiovascular risk and ultimately allow underwriters to better predict morbidity and mortality risk.