big data

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.

Wearables introduce a multitude of ways to monitor health. The quality and quantity of information supplied by wearables will transform how we manage our lives. There is a huge opportunity for life insurance companies to change the way we interact with our customers and to improve how we manage risk.

New global research released by Vitality, a leading behavior change platform, reveals that financial incentives combined with wearables encourage people to significantly increase their physical activity.

Customers can withhold their fitness data, but that will result in higher premiums, which may put life insurance out of reach for low-income earners. This in turn could have an impact on whether would-be homeowners can take out mortgages, some of which can require a life insurance policy on the principle borrower.

As data mushrooms, models become more complex, roles become more specialized, and terminology becomes more confusing (and over-hyped) – we need to be honest with ourselves, honest with stakeholders and not allow hubris in our models to displace common sense.

RGA shares basic steps insurers can take to prevent overfitting for more accurate predictive models.

The practice of life underwriting is undergoing a lot of change as insurers look to speed up and enhance the customer experience. Big data, artificial intelligence, pharmacy records, accelerated underwriting and more are playing a part.