A new data paradigm for pooled risk


(Monday, Nov, 19, 2018)
|   3 mins

We live in a world resting atop a pool of data and in every industry, companies are using this data to make smarter and more well informed decisions. The amount of data available about individuals (and by extension, society as a whole) has exploded in recent years. The more recent additions of wearables, connected homes and increased mobile usage have taken us to a point where, for most people, there is a wealth of data ready to be used by firms.

On a day to day basis, we see this data put to good use. Our shopping experiences are tailored based on what retailers know about us. Our web searches are focused in on our location to make the results more relevant. Our email accounts strip content to create reminders for events and travel plans to stop us missing important dates. Every service provider knows our location and uses this information to make our interactions with them more relevant.

Data is changing our world and changing the way industries work. The pensions, investments and insurance industries are no different.

Insurance providers of all types are now trying to make use of this data to provide more competitive and accurate pricing for customers. The basic principle is that with more data known about someone, the pricing can become more specific and tailored, reducing the risk and ultimately ending up with a keener price for both the client and the insurer. It also has the associated benefit of reducing the likelihood of non-disclosure and policy fraud. Or at least, that’s the idea.

For insurance providers, this is the continuation of a trend.

All traditional insurance businesses are based on the principle of pooled risk. When you take out life insurance, for example, you are becoming one of a group of people with the same policy. The insurance company has made a series of assumptions about your group. They think some people will die earlier than you will, and some people will die later than you will.

They calculate what they believe to be the likelihood across the book of business, and price the policies within this book to reflect those assumptions, and a bit of profit of course.
What’s true in life insurance is also true in the world of defined benefit (DB) pensions. Where longevity risk has traditionally been managed across a pooled book of business with the CMI tables forming the cornerstone of the longevity data and assumptions.

But the world has changed and now there is a much wider array of data to base our assumptions on.

We have been exploring the potential of using health and wellness data from mobile devices, wearables and fitness apps to make more informed decisions about the make-up of longevity risks within pools. Understanding the scheme specific risk profile is one way of making smarter decisions about the future.

Of course, in this context, we are still working at scheme/aggregate level, where a collection of people fall into that pool of risk. In terms of direction of travel, we are increasingly seeing insurance applications focused on the individualisation and personalisation of insurance. With car insurance, we have an easy time with the concept that better /safer drivers have cheaper cover. It becomes a more difficult conversation when we look at the potential impact of health and wellbeing data on health insurance and individual annuities and life insurance. Are we likely to see a big uptick in the number of people who are uninsurable, because providers have a much clearer view on their specific risk profile?

We then end up treading a fine line where there is a real risk that you improve services for some, while excluding others from support all together.

In this context, technology is an enabler. But morality and social conscience are the scales on which providers may ultimately be judged.


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