How an Insurer Predicts Your Client's Future Now

It's a lot of "if this, then that" statements. Every time you say yes to something, you might trigger the need for additional third-party data.

If you go through these health questions, and answer no to all of them, why would the insurer request any additional data about you? But if you start answering yes to some of these questions, then the insurer may want to go beyond MIB and motor vehicle records to gather the prescription data. And if they have found something a little bit suspicious, they may want to go to third-party data from LexisNexis, until they've got enough information to underwrite you and assign you to a proper risk class for pricing.

Or maybe they go further and get some medical underwriting. Maybe you need a paramedical exam, or urine, to confirm that you're a nonsmoker, but that is determined by the data collected and only used if there is uncertainty.

That's the real path; that's how to use these data streams, and how they can help improve risk evaluation and reduce the cost of underwriting, all while improving the customer experience.

How do technology choices affect how all of this works?

EIS is built with an extremely open application programming interface (API) architecture. That's the key to integrating with external third-party data sources. It's also the key to integrating with external reinsurers and their underwriting workbenches.

And you need to be in the cloud; you need strong open-API architecture. You need really, really minute microservices-based technology, as well, to integrate with some of these outside systems, to create the seamless, in-the-cloud, real-time assessment of risk, so that you can make decisions immediately and give a good customer experience.

It's not necessarily up to us to add these data streams; it's up to the carrier to understand which data streams or data elements they want to add and the value the streams and elements bring to them so they can be competitive in the market without assuming more risk than they have in the past.

That's where a company like EIS comes in. Having the right core technology lets carriers be flexible in the type of underwriting they offer, based on the product and market they're trying to serve.

That's where personalization comes from. And that's key to the success of any insurer, life insurer or otherwise, and very key to the success of underwriting here.

Obviously, life insurers underwrite life insurance policies. Do they actually underwrite annuity applications, too? If so, do they use any extra data streams?

There are insurers that will underwrite annuities, but they're few and far between. The reason they do it is to benefit the annuitant or the contract owner.

Here's an example: I've got cancer; I'm only expected to live for the next 10 years, and I want to put $100,000 in a single-premium immediate annuity.

If the SPIA issuer knew that when they underwrote me, they would give me a shorter expected lifespan, and I would get more money back on a monthly basis than if I had a standard level of risk.

Here's another example: I'm 65. I'm not a smoker, and I'm super healthy.

Because annuity pricing is based on standard mortality, which will include male/female predictions, it doesn't make sense for me, the consumer, to want to go through any type of underwriting.

Underwriting in this situation is only valuable for those who are ill or expected to live a shorter life than the standard mortality table predicts.

So, for annuities, they're going to use these standard data streams for prefill purposes, identity verification, and that kind of thing. That's not for underwriting, but rather for filling out the contract, doing their due diligence, and making sure they've got the right person for the right reasons.

Pictured: Samantha Chow. (Photo: EIS Group)