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“When we started InterGen Data, what we did was to say give us your client data and we’ll turn it around and send you back a list of life events, especially around critical illnesses and how likely they are to happen. Because people don’t plan for those types of things, they don’t plan for a heart attack, you don’t plan to get cancer. But when it happens, you have to adjust, you have to make changes.”
— Robert Kirk, Founder & CEO, InterGen Data
Come on in, sit back relax, and enjoy episode 105 of the WealthTech Today podcast. I’m your host, Craig Iskowitz, the founder and CEO of Ezra Group Consulting. For the past 16 years we have worked with hundreds of fintech vendors and enterprise wealth management firms to guide them towards making better business and technology decisions. I’m going to give a quick shoutout to our Head of Research, Jean Sullivan, and all the terrific work she and her team have been doing this year. If your company has a software product that you’re selling to asset managers, broker dealers, RIAs, or other firms, go to our website EzraGroupLLC.com and fill out the Contact Us form, and let’s schedule a call to further discuss your needs. Our research team can deliver a wide range of market insights including competitive analysis, addressable and obtainable market estimates, sales targeting, insights on buying decisions and a whole lot more. Your firm needs this data to be successful, and you can get the ball rolling by going to our website.
This podcast features interviews, news, and analysis on the trends and best practices in wealth and technology for wealth management, asset management, and related areas. This episode is part of our July focus on the intersection of health and wealth. We’re talking to the founders of innovative startups who are merging health and wealth to help financial advisors build stronger relationships, improve outcomes and enrich their clients’ lives. A quick shoutout to our sponsor, the Invest in Others Foundation, please go to InvestInOthers.org, and be sure to subscribe to our show wherever you listen to podcasts so you don’t miss future episodes.
Companies Mentioned
- Jaccomo [03:00]
- Plug and Play Tech Center [00:45]
- Techstars [00:50]
- Y Combinator [00:50]
Topics Mentioned
- Shaking Up Insurtech
- Identifying Systemic Risk
- How Data Can Help Defend Your Recommendations
- Benefits of Insurance Shortfall Analysis
Episode Transcript
Craig: I’m happy to introduce our guest for this episode of the WealthTech Today podcast is Robert Kirk, CEO and founder of InterGen Data, hey Robert, how’s it going man?
Robert: Doing awesome, thank you for having me Craig, great to be here.
Craig: I’m glad you could make it, I’m glad we could organize this and coordinate our schedules and our calendars to get you on our program. We’ve been talking for such a long time about this, so I’m really psyched to have you here. Tell everyone where you are calling in from.
Robert: I’m calling in from Dallas, Texas.
Craig: Love Dallas. Hate the football team, love the people, great place to be. I have clients there, spent a lot of time in the Dallas area checking out all the barbecue. My favorite thing when I was going there every other week – there one week, home the next week – I would try a different barbecue place every time and it was just the best.
Robert: Absolutely, and as a Cowboy fan I also don’t like the Cowboys and where we’ve been for the last few years. so I understand that plight of your as well.
Craig: And there you go. We can do a whole sports podcast next time, but now we’re dong a tech podcast. So talk to us about InterGen Data, give us the 30-second elevator pitch.
Robert: Absolutely, so 30 seconds. We have built proprietary technology that predicts life events. We provide this data to businesses so they can better align their products and services to their clients’ needs, do it proactively, and in a regulatory manner.
Craig: A regulatory, compliant manner.
Robert: Absolutely.
Shaking Up Insurtech
Craig: And full disclaimer, I am on the advisory board for InterGen Data because I like it so much, I think it’s a great idea, great product, great technology, and I’m not the only one who thinks it’s a great idea, you’ve been getting a lot of traction from fintech and insurtech accelerators, can you talk about some of the most recent ones you guys are joining?
Robert: Yes, so we had recently been in the MetLife accelerator, which was tremendous, that gained us a lot of notoriety. It also brought us into the insurance world for many different reasons. One, the PNC, the life side also the regulatory perspective as well. And then, as of the beginning of April, we got into Plug and Play’s accelerator, the Silicon Valley insuretech accelerator and that’s provided us a tremendous amount of exposure to numerous other insurance companies who are now talking to us about how our data can be used within their companies.
Craig: So for people who are listening who aren’t familiar with these what’s the setup of the Plug and Play accelerator, what is that?
Robert: Okay so Plug and Play is a well known entity kind of like TechStars or Y Combinator, and then what they do is Plug and Play has a group of companies that are considered their partners. So they’re anyone’s from USAA to Aflac, to all name brand companies, large corporations. And Plug and Play facilitates having meetings where they say to the companies like in Aflac or USAA, what are your problems, what are your hurdles, what are you trying to address? And kind of like an outsourced vendor, almost more like a camp, they really go through and talk to us as startups and say, here’s the problems that these large firms are trying to overcome, does your solution fit their problems, can your solution provide answers to their problems? Whether it’s on personalization, hyper personalization, defining risk, defining better product fit. And when we go through that they facilitate that meeting between the two companies, they’re kind of like the go between kind of that R&D Department of those firms and that’s what they really facilitate and then they go out and get groups of companies to be in the cohorts and then they say this is the class, then they go kind of promote that.
Identifying Systemic Risk
Craig: I see these all over the country it’s become a huge deal. Y Combinator used to be one of the only ones out at one point, now everyone seems to be doing it, but it’s a great idea. All these firms need exposure, they need money, they also need connections and advice in whatever particular industry they are in. And you guys cross a number of industries and you’re not just in insurance for example, you also help other firms, and we want to talk about a couple different case studies that you guys are supporting. So can you talk about the areas around Reg BI, KYC suitability, that your product is helping?
Robert: Absolutely, and thank you for that question. So, some of the traction that we’ve gotten recently over the past couple months, we finalized to proof of concepts with real data, with real customers one of those happened to be with a company by the name of Jaccomo, and what we did was we looked at Reg BI specifically for several reasons. As we know, governance, risk and compliance are ever increasing concern. Regulators are demanding that firms basically identify systemic risk proactively, and at the heart of this is really advice. So if you’re looking at advice it’s, is there a reasonable basis for believing that any series of recommended transactions, even if they’re suitable, if they’re viewed in isolation, are they excessive or unsuitable for a customer, if you look at the entire profile or picture of that person. And it’s not just a single person it’s you, it’s your wife, your family, your children, your extended family.
As we looked at InterGen Data what we did was we said look, give us data we will turn around and send you back here is when these life events are likely to happen. More importantly, here’s when the life events, especially around critical illnesses are likely to happen, because people don’t plan for those types of things. They don’t plan for a heart attack, you don’t plan to get cancer, but when it happens, you have to adjust, you have to make changes. So we started to say, in KYC and suitability, you have time horizon, you have risk, you have investment amount, you have asset allocation, which are all the standard ways of looking at it. But when you look at the data we’re providing it’s, well wait a minute, this could happen, and even though your risk profile says this is good, and you have a good amount of investable assets, the likelihood of you getting involved with say something like a variable annuity, it’s an illiquid investment. I may be the perfect target I may be 53, making the most amount of money in my life or whatever the situation is, I don’t have a lot of debt. So something like that could be a good vehicle. However, if I found out five minutes later that I had an 80% chance or 90% chance of cancer, I’m going to need the capital rather than an illiquid investment.
So it’s combining our expected expenses from a life perspective, looking at it as a whole and a house-holding perspective, and then saying, I might need more liquidity tomorrow than I do today. Even though you’re asking me the right questions, you’re comparing me to only a point in time situation, but you’re not taking into account the things that might happen. And in doing so we found out that you can actually look at that data and say it’s peer to peer comparison today but also future peer to expected expenses, and therefore, it may not be a suitable transaction for you. We basically combined both the life events, expenses of tomorrow with what the product is today. And we found that within the variable annuity space we could really determine if this is something that should be recommended or not.
Craig: This is one of my favorite use cases for InterGen Data, because it’s something nobody thinks about. As you mentioned, everything’s point in time, everything we see every tool is always what do you got today, and then it projects that out into the future without much changes, they may adjust it for capital markets assumptions a bit. Maybe if they’re doing Monte Carlo they’ll show you some sort of range but that’s just the market performance, it doesn’t look at your own performance like what’s my health performance? I need a Monte Carlo for my health, and what’s 1000 simulations of what can happen to me over the next 40 years? That’s how I want to see my expected expenses analyzed, and that’s, as you mentioned how you determine a real suitability.
Robert: Yeah, you’re right. So I look at it this way I’m 53, my wife’s younger than me. I’ve got two daughters, 16 and 13. So in a few years they’re going to be going to college. But in a few years, what if I also have an illness or something chronic that I have to deal with, what if I have to also deal with my in laws or my parents? All of these things in life come together at one point, so the more you can measure the better you can understand that. That’s what we need is we need to look ahead and the things that are unplanned and say what can we take into consideration, like you said the Monte Carlo scenario. Give me a range of things, let me find out, that’s really the goal, so that you’re spot on with that.
How Data Can Help Defend Advisor Recommendations
Craig: A phrase you mentioned which I think is really something you want to dive into is, “is it empirically defensible?”. And we never really thought of that until Reg BI came around. You’re not a financial planning tool yourself, but you feed other tools with your data and your analysis, so how does your data and your analytics, make other tools better at empirical defense?
Robert: Yes, that’s a really good question, and you’re spot on, we are not a financial planning tool. We are not a risk tool. We are extra data elements that we believe should be incorporated into those products and to help make them more contextually relevant, and then help make their predictions and their models more accurate.
As an example what we did in this in this proof of concept we took in almost 12,000 variable annuity transactions. And yes, of course, we were able to process it in under 15 minutes. But what we did is we took a look at the reputational regulatory and financial risk of the company. And when we ran these algorithms on top of their transactional data, we were able to instantly find, okay, you do not have enough risk tolerance information across some, some time horizons are missing, some ages missing, some incomes aren’t correct. And when you look at them in comparison, they’re not quite there.
What I mean by that is, out of that original approximate 12,000 transactions, 92% didn’t have enough data. So right there we met here is empirical evidence that says you need to go to a data cleanup and get your advisors and your people to go back and ask more questions. Find out if this data is, is really true, or missing. And if it’s missing, okay, fulfill it. Now, of that, there remained about 1000 transactions we were able to analyze and 45% of them were in compliance and we said hey, these are straight down the middle. They’re not risky, these are pretty decent, easily defensible we like to say. But of the ones that weren’t about half of them, right, we turned around and said, here’s some moderate risk, these need review.
And what I mean by that is two things, whether it was the risk tolerance of that person does not match the account goals or does not match the age or the income. This is standard type of compliance. But when we turn around and say, oh by the way, 23% of these people are likely to get cancer or have a heart attack and here’s what that cash flow is, that’s based on 4.2 million people like them, 3.6 million people like them because we took the US data that we gather the 140 million rows of data that we have and say, this is typically what happens to this type of a person living in this area, of this race, of this gender, of this background. This is something you need to incorporate. So when I say empirically evident, it’s not guesswork it’s saying, hey, this is based on government data and US data and your client data. Now you can go back and say these are the reasons why we did or did not evidence why we should want to, let’s say purchase an annuity, if that makes sense.
Craig: It makes complete sense and that’s the defensible part that you show the data, you show your work. Here’s how we got here, here’s why we’re saying this, it’s not just a guess, it’s not random, it’s not based on faulty assumptions or opinions, it’s based on actual data that we’ve analyzed and reviewed. And back to the clean part, I was recording a podcast earlier and we talked a lot about data. And just that part of your business model where you force the client, whether it’s an insurance company or a wealth management company, to look at their data and go, 92% of our data is not clean, 92% of our records are missing something. That’s hugely valuable to them and they would not know that if they didn’t go through this analysis, and they wouldn’t realize it until it’s too late, like when they needed some information, they needed a report for regulatory or other issues. Now you’ve shown them that so it gives them a huge jump on getting the data house in order.
Robert: Absolutely, and that’s the part about the proactiveness that the SEC is asking and what FINRA is asking for. Even recent news announcements talk about, hey, we’re going to end Gensler said we’re going to enforce Reg BI as it exists. Well that means they’re going to start looking for evidence, they’re going to start looking for why you recommended something and how you recommended something because they’re looking for data, they’re looking for what is the model. And it’s not just the person’s guess or an advisors educated guess, here’s some evidence behind it. And if you don’t have that information, then go clean it up, it’s not a bad thing, it’s a good thing it means you can go get it done. It gives you the ability to be proactive and not reactive. And that’s what that’s what the regulators don’t want to see is you constantly being in a reactive state, they want to see you actually going out and doing something about it.
Craig: It’s another reason why I really like what you guys are doing at InterGen Data because my company, Ezra Group, we have launched a series of data as an asset assessment products, consulting packages to review broker dealers, insurance companies, and other firms their data, but looking at it as an asset and doing these kind of cleanups, these reviews of infrastructure, frameworks, overlapping, data models and things and the fact that you’re doing that already is huge and provides a lot of value to companies.
Robert: Oh, thank you. We hope so. So we’re looking forward to seeing this kind of version into some more revenue for us as we go forward this year.
Benefits of Insurance Shortfall Analysis
Craig: Indeed, so moving on to the next area, and I keep repeating myself, I really like this use case as well and this is one of the use cases I talk about when people ask me, what’s my 30-second overview of InterGen is, a cross sell upsell opportunities for insurance and the insurance shortfall analysis, can you explain how that works?
Robert: Yeah, so this is something we did with our accelerator and during the accelerator part with MetLife and we were able to take some anonymized customer data, and use that data to show them where to pinpoint A) who to market to, B) why to market to somebody, and C) how much they need to really purchase on the insurance side.
As you know every year all employees get up towards the end of the year and they’ve got to enroll into their corporate insurance plans and you got to opt in and is it high deductible, low deductible, medium deductible, life, health, and it always ends up being a tough decision because you don’t know how much you should purchase, but if you ask people, they’ll say, maybe an insurance agent will say purchase as much as you can well that’s not really the answer. The answer is purchase as much as you need to for the reasons in specific areas you have to.
So what we did was we took in an employee plan, and we took in some data, again it’s anonymized, and we were able to use that data and ping it against our system. And what we came back with was a couple of things, we said look, let’s look for all the critical illnesses that these people might have, their employees, over the course of 1, 3, 5, and 10 years and let’s see if it’s a big amount. And what we found out over the course between 5-10 years was that this one employer, they were likely to have about almost 46% of their employees have more critical illnesses, than they were projected based on what they had for insurance.
Now what I mean by that is when they came back with our answer, it ended up being a $20 million shortfall. So think of, 1200 employees and think of 600 of them saying they’re going to be in 5-10 years $20 million of expenses, and they don’t have that in terms of their coverage. So all of a sudden we knew, Oh my gosh, whether it was cancer or heart attacks or diabetes or all these different types of diseases, that they’re gonna have issues.
Specifically, we were able to identify the ones that had enough coverage that were good, that was group one, group two were people that said hey, If you make a small adjustment $20-30 a month. Today, over the course of the next five years or so you’re going to be okay and that was group two, and then group three, well they were the group that really needed the most help. And we found evidence that people, and here’s one example, where one employee is going to have about $180,000 shortfall and expected critical illness and expenses, and she makes $25,000 a year. There’s no way for her to overcome that type of problem, even if we were 50% wrong, and it’s a $90,000 difference. She still can’t make that up.
So that becomes a problem for lost wages, lost time. But what about the people, how do you care for them, how do you help them? And by us being able to identify that, that allowed them to understand from a hospital indemnity, critical illness, optional life. Here’s other products we can sell to this person or that that person should use to help overcome that difference, and that’s massive. That’s a huge thing for the employee for the employer, and it makes everyone look good, it’s kind of a win win situation.
Craig: It is, and I thought that was a huge benefit. But I wanted to drill down on one piece of that, when you said 46% of the employees were projected to have more illnesses, is that a probability analysis when you said that there are this probability likely or this percentage, more likely to get an illness, or how does that work?
Robert: The way we look at everything it’s by race and by gender and by where you live and what you do. So if you think about it, the short answer is yes, it is a probability study. And then secondly upon that it’s probability based on who you are. So if you think about, let’s say, look at all the cancers in the world, you build a big bell curve. And within that bell curve that might affect X million people, but then it’s a big bell curve based on let’s say myself, Native American Indian so I’m a smaller subsection, and I happen to be male. I’m a smaller subsection, but I happen to work in Texas in financial services. So based on that, I know the core group of people, and then I can say, Does this represent a true number in accordance to the national statistics of how many people get cancer, based on your age, based on your race, and then we combine that data to say, this is the likelihood of you getting this disease, and this is the likelihood of you having to pay for these and typically, at what age. So that’s how we do the grunt work of it but it does come out and say X million people, X percentage of this population. Here’s the propensity number. So we assign that and if it’s greater than 50% we include it, if it’s under 50%, we exclude it. So that way it becomes kind of binary.
Craig: Yeah, I think all this stuff is really cool because it’s something that’s really not being done at any other level, and there’s so much value across the spectrum of whether it’s wealth or insurance or other aspects to help provide this kind of information. But I don’t want to dwell too much on that because we’ve got another use case, I wanted to sneak in on this podcast so, can we talk about what I was calling, zip plus four, but it’s really increasing your predictive capabilities, expanding partnerships with other data providers this particular data was anonymized tax information, and how can you use that data to provide more value and more accurate results for some wealth management firms?
Robert: That’s a really good question, for several reasons. First of all data can either help us on a quantitative side or it can help us on a qualitative side. And when we look at the quantitative side what we’re looking at is specifics around actual numbers. And if you think about the averages of where people make money, when does a person make the most money money in their life. Well there’s technically two answers. One is 51 for 20% of America and the second numbers 47 for 80% of American, but the differences between the two are massive, the differences are between $160,000 and $65,000.
So the 20% of America that makes the most amount of money in their life at 51 tend to have similar jobs, they’re lawyers, they’re doctors. And so what you realize is they have extra degrees, those extra degrees or something that took them four more years so if you go back and just push off the earnings four years, then what happens is you’re able to earn more.
Now what’s important about this is if you look at the zip codes and you look at zip code plus four, you’re looking at where people tend to get the most amount of incomes. Well if they’re lawyers and doctors they tend to be in the larger cities but that’s also where larger costs are. So as we start to realize where people live in what they do, it allows us to quickly get rid of the noise. So if I live in Kankakee or Walla Walla, Washington, or Paris, Texas, the likelihood of me having a million dollar salary is less, the likelihood of me having a million dollar salaries in larger cities. So that’s standard, we kind of know it, but now we can prove this down to the zip code plus four.
What I mean by that is the data providers we’re looking at give us 150 million households data, 200 million adults in the US, we have 1400 data elements that we can start to look at. So imagine being able to say, you don’t have to ask 20 questions to understand my financial place in this world. Just say what’s your zip code plus four and instantly I can get all the noise, take it away. And now you can start to say, okay now I have a good subgroup. Now tell me what you do. Now tell me a few more things, and I can actually start building a financial model based on who you are because of where you live. So hopefully that makes sense to you but that’s what we’re looking at it for, it’s personal, it’s business, it’s gathering that market intelligence and being able to use that to really drive wealth and asset management.
Craig: Oh, it makes sense to me. Absolutely. That’s one of my pet peeves is every vendor you talk to ask the same questions, right, and even the small question start becoming annoying because every website asks the same question, what’s your city, state, zip what’s your annual income, your assets, what’s this, what’s that. And what you’re saying, if I can paraphrase is all I really need to give you is my zip plus four, and then you can assign a baseline, the median value of assets under management and the median value of income, and other assets that may not be exactly right, but at least it puts me in the ballpark. So you can do estimates, or you can say well how much above or below are you from these numbers, which saves a lot of time.
Robert: Yes. Well, think about your statement just now. What is your address, your city, your state. Well if you know the zip, you already know the city and state, why ask the city and state?
Craig: That’s my pet peeve, they’re confirming who you are. What where do you live, I live in East Brunswick, New Jersey, what’s the zip. I live in East Brunswick, New Jersey, I know the zip. That doesn’t verify anything, you’re providing any value I’m making me give you the zip, I should just give me this if nothing else, then you should know what city and state I live in, but they all ask for everything it’s over and over again.
Robert: It bugs me too because at that point you realize that so basically the amount of data that we can get from the zip plus four basically says, Okay, I know the household income on the median for that whole zip plus For I know the capital gains, I know the interest, I know the tax loss carryforwards, I know the typical pensions and annuities, think about all your schedules you submit to the IRS.
Craig: Right, that comes to the tax the anonymized tax data you’re getting.
Robert: Correct. And so all of a sudden that you realize that you don’t have to ask all of that, and like you said present a different use case where all of a sudden you’re like well, I make a little more I make a little less, it becomes a slider bar adjustment, it’s a much better experience for the customer. And it’s actually something that relates to you. You don’t have to say, Oh, I’ve got to fill this all out again, that’s the nightmare scenario.
Craig: And also you can take steps, depending on what your your user experience is, you can provide more information to the client right away without asking for all that, because even though, let’s I’m just making these numbers up if you if you live in a particular area, and the median household income is $75,000 But you make $87,000, that’s not going to change much of the advice I give you that difference. I can just give you the advice, it doesn’t really matter whether there’s a certain range above or below the median, that the same advice holds true, is that a true statement?
Robert: That is a true statement, and that’s where we say that’s your first step, let’s change the way it’s done. Secondly, now let’s think about how else we can change things. So, like you said, if it’s 75 or 68 or 72. That’s small variance, but let’s say I’m Native American Indian, I have a large propensity to Type II diabetes, alcoholism to, Alzheimer’s. Okay, well, at what age that typically happen? We’re going to give you that data as well. So now, you already know my expenses going forward, almost instantly, one question, zip, boom.
I mean, of course I can tell you a whole bunch of data we collect that may not be useful but all the people that speak a Croatian language in Allegheny County in Pennsylvania. But in reality, who cares right? So it’s great to know it, but we’ve got to make sure that that information is really contextually relevant and that’s what we’re saying, make it relevant. Make it quick. You don’t need to ask 50 questions.
Craig: People who speak Croatian or Hungarian I mean, my one of my grandfathers was Hungarian, trying to translate his birth certificate was a real pain in the ass, it’s in Hungarian, but yeah I know what you’re talking about. So, wrapping up because we’re running out of time, closing statements. We’re talking about the intersection of health and wealth, and how they’re coming together and how they’re merging and how they really shouldn’t be separate because there’s so much interaction, so can you talk a bit about how InterGen Data is helping advisors merge health and wealth?
Robert: Yes, so it goes along the same lines to some of the increase in our predictive capabilities right so additional data sets. So having data around emergency medical records, having data around claims, we can take in $7.8 billion claims data, we can take in all the prescription data, what’s typically happened, knowing whether it’s 11 billion RX claims and here’s what people are spending their money on. You have right now, I think by 2030, the most amount of people all the Baby Boomers will be over the age of 65. This is a huge silver tsunami, on its way. And the way we are going today, the medical expenses are going higher. People are living longer, which means you’re going to endure that cost longer. We have to be prepared for that. So, if we’re not taking into context the medical expenses, the living expenses, then the question is, what are you doing today to help us get there to help us overcome this?
So, it’s a massive amount, and a massive number that’s coming forward, but it’s also where the people who are doing the wealth, whether you’re in the preservation mode, you hopefully you’ve gone past the accumulation mode for that group, but in other people it’s the accumulation, decumulation, preservation, utilization. You’ve got to combine that. The only way to do that is understand the cost of health, figure it into your asset allocation to say someone from 50-55 should be thinking this way 55-60 should be asset allocated this way, and I almost think that it’s, if you think of it as sleeve management, time sleeve or time series management. You need to fund and over fund the times when you, you’re likely to have a lot of life events and issues, and the places that are overfunded that you don’t need a lot of money, use that money to kind of fill in that gap. That’s where I really see it happening so I know I kind of went off topic a little bit but I see the convergence between wealth management, asset allocation, and understanding that the expenses of medical, it is becoming one and we’re having to do it and we’ve never had to do it before. This is a huge amount of people that are that are not getting to be 65 years old and older.
Craig: There is no such thing as off topic on this podcast, everything’s fair game, but we’ve run out of time and you’ve sent it all. Can you tell everyone who wants more information about InterGen Data, where they can find you?
Robert: Absolutely. So, you can always reach out, go to the website at www.InterGenData.com Feel free to email me directly, Rob@InterGenData.com. You can reach out to myself or anyone on the team, we’re more than happy to answer any questions that you have.
Craig: Brave man who gives out his email address on a podcast. Rob, thanks so much for being here. Talk to you soon.
Robert: Take care.
