Podcast #11 – Underwriting 2.0 Panel at InsureTech Connect 2017

This is a special podcast recorded live at InsureTech Connect 2017 in Las Vegas on October 4, 2017. I participated in a panel about the future of life insurance underwriting called “Underwriting 2.0: Opening Pandora’s Box.” I was alongside three industry professsionals and the panel was moderated by Samantha Chow of Aite Group. You’ll hear a spirited discussion that highlights the exciting new capability of our subsidiary Life Epigenetics, Inc. Life Epigenetics applies the predictive epigenetic science to life insurance underwriting. It was great to begin to tell this story to an audience of insurance professionals, most of whom were hearing about epigenetics for the first time.

Podcast 11 – InsureTech Connect 2017

Samantha Chow: Thank you for being here today. I’ve got a great group of gentlemen up here. I feel very underqualified as a matter of fact. But yes, I have with me Jon Sabes, who is a CEO of Life Epigenetics. We’ve heard a lot about that. Byron Udell, president and CEO of Accuquote, Kuang Chen, CEO of Captricity, and Rick Hu, founder and CEO of Vivametrica. So we have everything from medical, epigenetics, process, product, so we’ve got a lot of deep opinions, all of us up here, we’ve had some pretty heated discussions about some of the things we’re talking about today. So I’m going to get started real quick. You know, we know that there’s a lot change. We just heard a great panel of how the change is happening, but the question I have for this panel upfront is, is it going to be status quo for the next five to ten years, or are we really going to see an explosion of how underwriting is happening? Are we going to get to that fluidless underwriting process? Byron, I guess I’m going to start with you.

Byron Udell: Well, as you all know if you were there for that last session, we’re already there. There are a number of carriers that are engaging in a fluidless process. I think a key in how that plays out over the next three to five years, or even the next ten years, is based upon a number of different factors. Number one is pricing. If you take a look at the pricing of let’s say a 40-year-old male, nonsmoker, preferred risk in 1994 for a half a million dollars, 20-year term, that was 995 or a thousand dollars for the first time in 1994. Today, anybody want to take a guess? 350. So it’s gone down dramatically, and it was really low in ‘94 Actually I remember applying for insurance in 1994, and thinking, wow, this was really, really low. And so pricing has gotten lower because carriers have gotten addicted to all kinds of fluids. They’ve got all this data, and they can get it down to the point where margins are very thin, and the demands on capital are very low because interest rates are so low. So at today’s pricing, if you say you want this pricing, and you want the fully underwritten process, the pricing’s going to be the key. It’s going to work at today’s pricing. The work activates the pricing. If you have at least one carrier going to 500 thousand, never paying fluids, it’s not a maybe no fluids, then you’ll see. Anti-selection is a big, big deal. Right now, consumers maybe aren’t hip to the best way to go, but what are the carriers using if they don’t use fluids. They use big data. They use credit information. They’re using farma. And you take a look at, say, credit information, you got lousy credit, but you’re healthy, then you’re going to go to some website that’s going to tell you, you probably want to go through the fluid process because otherwise they’re going to hit you for your bad credit. Otherwise, if your credit is really, really good, but you’re in horrible health, they’re going to tell you to go through the fluidless process. Carriers are going to be stuck with what I believe is as much anti-selection as we heard about.

Samantha Chow: Interesting, so Jon you come from a different background too, so more of the epigenetics and health, so what are your thoughts there?

Jon Sabes: I guess the question is what fluids are we gathering and what information is it telling us? What’s the cost of getting and gathering those fluids? You’ve heard the phrase epigenetics. It’s a new phrase we’re tossing around this conference for the first time. We’re excited to be talking about it. It’s really with epigenetics, it’s really gene expression. And we believe that the cost to obtain through saliva, we can get the data, and be able to provide carriers in a very short time period, the types of information that allows them to underwrite to, say, 100 percent protected value. That’s what we’re focused on, and we think InsureTech technology, particularly around epigenetics offers that promise.

Samantha Chow: And Kuang, to hear from a different perspective, what do you think?

Kuang Chen: Absolutely, so I’m somewhere in between things are changing, but it’s going to be the same to a very cool future of epigenetics. We have to ask, what’s practical now. And I think there’s a lot of practical applications of AI speeding up underwriting. So I think that it’s going to change. It’s going to change when carriers debate its practicality. But a lot of the signals that we get these days that are helping us skip fluids are based on social, behavioral, are based on life stuff processes. And that is a good process for some segments, but not all segments are represented. And it’s certainly not moving higher in the industry. And so when we think about what does, what if we could actually leverage the health information that carriers have been gathering for decades, and use that to build a model that then can compliment all the other scores and what not that are out there, trying to get us as an entire industry to a much less invasive future. And as an example of that, we’ve been working with MassMutual to go back in time two decades and gather hundreds of millions of data points of historical, app-type of health data. And they’ve been able to build a model that is nothing like this industry’s ever seen. And in fact we’ve been talking with them to first distribute this model to the rest of the industry, and that’s something that we’ve announced today at InsureTech. So I think with something practical like that, you can get to this future by taking these steps.

Samantha Chow: Great.

Rick Hu: I do think it’s a step-by-step process. I don’t think it’s going to be an all in for all people. But I think what we’ve heard is that the barrier to actually proceed that’s what they want to do. So distilling things down into a very small amount of personal information, and then having meaningful understanding of them is a key component, and the more passive that can be then the more attractive it is for the consumer.

Samantha Chow: Continuing on that, you know, Rick, we talk about some of these limitations carriers are facing, the hurdles they’re trying to overcome that relates to using internal data, the data they have for their own personal experience, demographic data, basic application data that they have, as well as these new forms of data, IoT, wearable. How do we overcome those natural limitations and hurdles?

Rick Hu: Well, it’s a long process, and we’ve been doing beta analysis since the early ‘90s. It was just biostatistics and using professional databases and data sites to actually look at a percent of the population, and effects of different choices on health. That gradually evolved into where we are right now, which is to upgrade large data sets that are validated to provide individual information. And from our standpoint we’re able to then use information that comes from devices and other data sources. They all have to be validated, though. They all need to have some understanding of are we comparing samples, are we actually using the data in the appropriate manner. And so this is for run of the mill kind of things, when you start to interact with the business processes, then you need to actually understand what is said. I think Kuang probably has a lot of personal experience with this, and the ability to interact that data.

Jon Sabes: Just one comment, I think at the end of the day, you know, we talk a lot about data sets, credit scores, social media. There’s so much going on. But at the end of the day, carriers need to go back to the personal health information to make these actuarial underwriting decisions. Staying focused on that, and how do we get accurate feeds, if you will, to properly underwrite is really where I think we as innovators need to be focused on the serving the industry those sorts of solutions.

Samantha Chow: Great. It’s a good kind of segway, but I want to remind real quick that you can ask questions if you like through the ITC app, and I will get those questions up here and kind of interject them if you just go to the vision pillar, you can enter the question. And we have one that’s jumped out here real quick. And I think this is what we were just chatting about, health, APS, how that data is so important. The question is how does epigenetics differ from genetics in GINA?

Jon Sabes: Well, GINA is a nondiscrimination act about preventing health insurance carriers from using genetic information to make underwriting decisions for health insurance. The life insurance industry is exempt from it. However, the industry is concerned about how to react and deal with the opportunity to look at genetic dispositions in applicants. Epigenetics is a really interesting science in that it really has more to do with the behavioral attributes of the consumer or applicant of life insurance. It allows us to underwrite to really factors that are currently being under consideration today, but we’re able to do that and pick it up basically at a molecular level, as opposed to health records, or the more general collection.

Kuang Chen: In response to the question, I kind of position it as, while the environment sorts out the morality and legality of issues of using these kind of approaches, why shouldn’t carriers leverage one of the only practical advantages they have over startups, which is their bigness and their history. And their history has a wealth of health information just embedded inside paper forms and other scanned images, which can be leveraged to get to something that is really developed in terms of predictive intelligence.

Rick Hu: So in relation to the genetics, in Canada earlier this year there was a law passed that essentially banned genetic testing for life insurance assessment. So there were a number of actuaries in Canada that were focused on receiving that act. It was a very sudden move. So it obviously has created a great deal of concern, so I think there will be some political, social interaction and view of this, but I think it’s up to the industry to really reassure people that there is a lot of positive that comes from this. I think it’s really deeper.

Samantha Chow: Let’s take it back to the present. You’ve been in product sales forever, almost. APS, how important are those? Are we going to get away from that? Is there ways that you see as you’re selling these products that we can get past the APS?

Byron Udell: I don’t make the decisions. I make the decisions as to which carrier we place it with, and some carriers in their very DNA of how they underwrite, some of them just have an appetite for APS’s, and they get addicted to them like heroin. All they got to do is find one case where they ordered an APS or they weren’t sure, and they find, oh my god, if I didn’t seen this I might’ve made a big mistake. So that causes them to order APS’s on fingernails, toenails, the craziest stuff. Life insurance is amazing. Some companies run APS’s on 60 percent of the applicants, even young applicants, which is insane. Others are down to 10 percent. So the question is, let’s say we get away from the stuff that’s sort of friction in the marketplace that causes people to drop out, is it going to get more people to say yes, is it going to get more people covered. Because if it doesn’t, then it’s just a matter of a different process, and a little different price, but we still end up with only half the nation with life insurance. I mean it’s a tragic situation. You take away the insured rate from 1965 to now, it’s just fallen, the percentage of people that are covered. I truly don’t believe that people say no because they know how painful and friction full this process is going to be. They say no, sometimes they drop out in the process. At the front end, they don’t know how painful it’s going to be. They don’t really know why they say no. They find out that it’s painful, but honestly if you say, it’s a really simple process. They say, well, I was going to say yes, but I didn’t know it wasn’t going to be simple. So I think we really have to decide what we’re going to do to get more people to buy. There’s a Grand Canyon between what the nation owns as a whole in terms of life insurance and what they really need, and even what they say they need. There’s a Grand Canyon between what they have and what they say they need. But APS’s aren’t going away anytime soon. I do think there will be less of them when EMR, electronic medical records, carriers can rely on them. They can get them from pushing a button, instantaneous. And they’re properly coded, instead of sifting through doctors’ handwriting that they can’t read. If they could just say, well, there’s an APS, and it’s coded, taking metformin for diabetes, we get it, it’s a lot easier that way to gage four weeks from the process, especially with doctors saying, you’ve got to stay on for four years, it’s not good enough. It’s a big issue. I hate when they order them because it does slow things down, and time is your enemy.

Samantha Chow: It really is, really is. A word to the doctor.

Rick Hu: I’m always surprised when you get patients, and you guess, is this business or is this truth. I’ve filled out thousands of these in my 27 years of practice, and it is almost never the complete source of truth. So the concept of having verifiable information on a medical basis is important. It’s very important. However, the whole concept of the APS being that source of truth is probably not correct, and there are many, many reasons, the least of which is that you have a doctor-patient relationship in many cases. But there are aspects of it that just make it less than objective. It has to transfer into a more fact based operated process. I think in that circumstance that that’s going to be the databases that we use, and what the APS is. It will be verified patient health records.

Samantha Chow: Jon, what are your thoughts on that?

Jon Sabes: I guess my thoughts are exactly echo Rick’s concern. I mean at the end of the day, you want to get to a factual based understanding of the health of the individual we’re trying to underwrite. So how are we going to do that? What’s the most efficient way to obtain it, the most cost effective way to do that? And again, I’ll draw back to what we think our business opportunity is to deliver something that the industry is calling for. This science is relatively new. We couldn’t have been talking about this three, four years ago. In fact, it didn’t exist. So the fact that we’re here today, we’re talking about it. I think it’s real, it’s viable, and it will deliver, as soon as confirmatory. There’s no silver bullet. And so if you can aggregate these things in a way that’s effective, quick, and efficient that makes the process as it can for distribution, you know, you’ve got a formula for data streaming forward, and that’s what we want to be part of.

Kuang Chen: I want to offer maybe like a bridge here, and say the APS statement is a very univariable approach to knocking something out. There’s not a lot of signal there, and it’s highly competitive variable in terms of quality, although there’s a lot of truth there. Whereas genetics is a extremely broad and multivariable approach to assess. That may be in the future, and that’s the future we want. The bridge would be what is a way for us to give a really good multivariable assessment now with the data that we have access to now that’s not contentious. And I think that it’s a combination of health information that carriers already have, and the social, behavioral metrics that are available, and being able to operationalize them in this bridge time between the past and future.

Rick Hu: We found that the information we could obtain from personal, sensory devices from phones are in fact informed of behavior. And so it’s not just that you carry a number, it’s actually a reflection of the individual’s, a reflection of how that person thinks of the world. And so this is where it starts to border on the epigenetics side of things. It’s the epigenological, the actual influences to that individual’s underlying basis. So this is not an and/or. Or rather, it’s not a minor thing. It’s actually steps on the process, and so the appropriate methodology for the appropriate product and the appropriate business pace is to keep part of it.

Jon Sabes: I want to just make one addition to that. You know, the technology we are currently bringing to the market and testing with several carriers now or going to shortly is based off predictive epigenetic biomarkers that happen to be linked. And we’ve translated those into the actuarial tables and methodology, so it’s far more advanced than sitting here today you might have thought several years ago.

Samantha Chow: Carriers are so focused on the consumer. Is that the right path, and I think that’s just me throwing a question back out to the audience. But one of the other ones that popped up here kind of goes back to what you were saying of genetic testing. Is it a substitute for all of the data available on the typical app? Jon, what are your thoughts

Jon Sabes: I don’t think so, no. Again, it’s early, and there are a number of things to confront. You certainly think about technology. You think about the life insurance industry. These are key areas where we as entrepreneurs in the application of technology should be focused on. And I do hope they promise ultimately the opportunity to reduce overall costs if you will of insurance. It will serve to make the products, as Byron was saying, much more available, maybe even drive costs down to even consumers who may or should pay more based upon their overall profile.

Samantha Chow: Yeah. I know that there are some solutions out there that have put some thoughts around how to collect the saliva without sending a person to collect it. Number one, expensive, number two, invasive. I’m not sure how collecting saliva is any more invasive than me sitting on my own couch doing an EKG for a very low face amount policy. So, but I’d love to hear your thoughts on that.

Jon Sabes: There’s a lot of talk about it, but consumers love the information. I mean, there’s plenty of people in this audience who would either subscribe to Ancestry.com, 23andme, TeloYears. So the consumer, I think we all, we try to protect the consumer too much, giving them more information, being more transparent I think only leaves you more informed on making decisions and engagement with the kinds of people you go into business with.

Byron Udell: Capitalism will force a resolution. If capitalism says consumers want a simplified issue, $400 and get rid of the 350 super hyperscrutiny type of underwriting, then that’s where the market will go. If 20, or 30, or 40 percent of the consumer still want the absolute most price, what we might find is that they’re going to just add your guys’ processes to the existing processes to get the price even lower. For whatever reason, carriers seem to beat each other up getting prices lower and lower and lower, which makes no sense. But I think when we spoke earlier, I was saying, I underwrote my wife in 1986 for a million dollars. It was just unheard of. Five years ago, you couldn’t get $150 thousand a year later without fluencies, just hitting the market in the head, being able to speak for that. Once they started seeing what they could get, they got addicted to seeing blood sugar and all the other stuff then that’s when the rates just started coming down. And so consumers are addicted to these prices. So capitalism will actually be the decision maker. The invisible hand will decide what the consumers want.

Samantha Chow: Yeah, and I think that it kind of falls right into our last question with a couple minutes left. Kuang, I think I’ll start with you here. What are the biggest hurdles you see as carriers really try to move towards that more automated underwriting process, and do you ever see it being 100 percent on the money?

Kuang Chen: Yeah, I love that question because it sort of sets aside the new product that already automatically is underwritten to some degree. But those don’t address the primary sets that carriers really care about, and so I see that question as asking, what can we get for automatic underwriting for the existing set and deal with all the changes that in this society and this economy. I think that the primary challenge that I see is still operationalizing the predictive capabilities, and the new data that are ever coming out, and the operationalizing means how can you literally, let’s just get down to the brass tax, how you plug in a new signal into to an underwriter’s life, or an underwriter’s assistant’s life, or a case manager’s life, such that they don’t fight it. They don’t feel like they’re going to get replaced by robots, but they embrace it as something wholesome. And to this end, actually I have an example. We’ve been working with MetLife for the past six months, and we actually launched a new product also today called Case Correct that allows them to efficiently prioritize with AI what they look at, to route the decisions of hard ones to some workers, and easy ones to some other workers, or by location. And the whole point is there needs to be some practical applications of AI that help carriers keep abreast of the changes, and if they did that, that overcomes the hardest challenge.

Samantha Chow: Alright, AI, I’ve done some research in this area before, and talked to some underwriting departments where they spend 80 percent of their day doing administrative work and 20 percent truly underwriting, so getting to that automated process is key, even before taking a step further into being more advanced in underwriting. Gentlemen, I want to thank you very much for being here today. Jon, Byron, Rick, Kuang, it’s been great. Thank you very much for being here today, and the questions you provided.

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