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Using AI To Financially Plan Our Future With Robert Kirk

Robert Kirk

Robert Kirk is the Founder and CEO of InterGen Data, a prediction and analysis company that uses AI to help financial professionals prepare clients for impactful life events. He is an accomplished technologist, leader, and industry expert in wealth management. With over 30 years of wealth management experience, Robert has led several firms in technology implementation and played a critical role in helping those firms grow.

Here’s a glimpse of what you’ll learn:

  • Robert Kirk talks about InterGen Data and what it does

  • The data collection process and the insight it provides

  • What are the challenges of running a software company?

  • Robert explains the accuracy of their life events predictions

  • InterGen Data’s ideal customer profile and success stories

  • How AI impacts the insurance buying process

In this episode…

As we navigate life, significant events are bound to happen, including retirement, critical illness, or inheritance. While we cannot predict precisely when these events will occur, it is entirely possible to prepare for them. However, many are completely unprepared for their future.

This is what Robert Kirk discovered after his grandfather was diagnosed with Alzheimer's and had to move into a long-term care facility. To help others avoid this, he built a company that uses predictive analytics and machine learning to analyze data and provide insights into potential future events. The insights help individuals make informed decisions about their financial future. Financial advisors can also provide personalized advice and recommendations based on the predictions and their client's unique situation.

In this episode of The Customer Wins, Richard Walker sits down with Robert Kirk, Founder and CEO of InterGen Data, to discuss the use of AI to predict the future and financially plan for it. Robert talks about InterGen Data and what it does, the challenges of running a software company, and its success stories.

Resources mentioned in this episode:

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Meanwhile, our mission is to help the top firms in the financial industry raise their bottom line by streamlining the customer experience with automated, convenient solutions.

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Episode Transcript:

Intro 0:02

Welcome to The Customer Wins podcast where business leaders discuss their secrets and techniques for helping their customers succeed and in turn grow their business.

Richard Walker 0:16

Hi, I'm Rich Walker, the host of The Customer Wins where I talk to business leaders about how they help their customers win, and how their focus on customer experience leads to growth. Some of our past guests have included David Knoch the CEO Docupace, and Philipp Hecker Bento Engine. Today I get to speak with Robert Kirk, founder and CEO of InterGen Data. Now today's episode is brought to you by Quik! the leader in enterprise forms processing, when the last step to earn your clients business requires processing forms, don't ruin a good relationship with a bad experience. Instead, get Quik Forms to make processing forms a great experience and the easiest part of your transaction, visit to get started. So before I introduce today's guest, I have to give a big thank you to the team at Docupace for introducing me to Robert many years ago, go check out their website at All right, I'm so excited to talk to Robert. Robert Kirk is an accomplished technologist, leader and industry expert in wealth management. With over 30 years of wealth management experience, starting with trading and then moving into tech, Robert has led several firms in their implementation of technology and to play a critical role in helping those firms grow. Since 2018, Robert is the founder and CEO of InterGen Data, where he is creating, developing, implementing, and disrupting the banking, financial service and insurance industries. He has built a proprietary life stage life event artificial intelligence engine, which helps companies predict when clients are likely to have a major life event. So advisors can serve their clients better. Rob, welcome to The Customer Wins.

Robert Kirk 1:50

Thank you so much Rich, it's a pleasure to be here. Great seeing you again and happy to share whatever it is we talk about with your audience.

Richard Walker 1:58

Oh, I've been looking forward to this for a long time. And I hope our audience is too because if they haven't heard this podcast before, I talk with leaders like you about what they're doing to help their customers win, how they built and deliver a great customer experience. And the challenge is to grow in their own company. Rob, I want to understand your business a little bit better. How does your company help people?

Robert Kirk 2:18

Great question to start with. We are a company that was founded because of a life event. My grandfather at the time was getting old. And we needed to have him go live out his days with his daughter, which is my mom. But he ended up shortly thereafter getting Alzheimer's and we had to move him into a long-term care facility. Now, this is back in 2009, to about 2013. And we just weren't prepared. We weren't prepared physically or emotionally. No one ever is right? You're just dealing with this, and you're trying to figure out how to help a loved one. But then we also realized very shortly thereafter, that it was just we weren't prepared financially or legally, in fact, not prepared so much that even when we looked at the cost of a long-term care facility, plus medication and special treatment, it was $14,400 a month. And this happened for over four years. That's close to 680 $690,000. This is a number that should just blow away anyone in the US and say, are you prepared? Well, we weren't and I don't think most of the US is. So I started to look at the industry and say, well, I looked inward and first said who's worse than me? I'm working in technology. I'm deploying tech. I'm building tech I'm helping advisors and CPAs and estate planners. But I couldn't help my own family. So I sat there and I said, there's got to be why, why can I help what? What's going on? So I looked at the planning systems, the risk systems, the asset allocation tools. And what I shortly realized was there's a gap, simple gap, there's a lack of data, there's a lack of data around, hey, these events are likely to happen to you. And here's how much they cost. And here's what you need to do. And here's how much they impact you. And that data wasn't available. And I looked at the systems. And this isn't nefarious, these systems are great systems. But they didn't have this data. And they relied on the adviser to ask the questions. I'm sorry, but Rich, no one came up to me and said, hey, can you tell me when do you plan to have your grandfather get Alzheimer's? When do you plan to have your spouse get cancer? I mean, nobody does that. Right? Were people and in fact, told me, hey, this just happened. Most people would go, oh, I need to shy away and you go away. So we realized though, this is the most important time right there at that moment for an advisor to say, that's okay. I know other situations like this and people like you tend to go through this. Let me help you. So I built the company to provide that data to give an edge to the advisors to help the firm's but more importantly to help the clients understand If it's going to be okay, you can get through this.

Richard Walker 5:02

Rob, you said so many important things in there that I want to reflect on. Number one, I don't know if everybody really thinks about this as clients of advisors, advisors really, really care. And a lot of them come from a background of wanting to help their own families. And that's why they want to help your families. So I love that this is the origin story that you have. Second, when you're talking about these life events, I got my life insurance designation, I went and got the agent thing. And talking to somebody about death is really hard. But I would bet talking about disablement, or cancer or these life events that are affecting end of life and comfort, etc, it's probably even harder, simply because we all know, we're all going to die. We don't know if we're all gonna get Alzheimer's, right? So how did you figure out how to put this data together so that you could give these insights?

Robert Kirk 5:51

That was a, to say the least, it was a passion of pain, I had to go through tons of data. So this is back in 2013, I started looking for datasets. Today, the data is so much better than it was back then. But I would literally go through government documents, I would go through hospital documents, cancer research documents, I would go through PDFs...

Richard Walker 6:18

Reading all these things and learning.

Robert Kirk 6:20

Exactly, reading all these things in learning, then I would try to investigate where the data was where can I get access to the data? Is it public data? Is it private data? Could I purchase the data? How much was the data? And so I started cutting my first checks back in 2014, to tell people all to tell my team, hey, I found this dataset, I just purchased it, can you tell me? What can we do from a machine learning aspect. Now when we look at machine learning back then what we were doing is rather simplistic, but it's very important, we would look at all the data. So we would get all the people who died of let's just say cancer, and pick a cancer doesn't matter. We picked one, tell me all the data of all the births, tell me all the data of all the people who bought their first car when they bought their first car. So we would get the data specific to that, based on the research that I did that we did. And then we would find the data set and put that in to the computer, the computer and the machine learning aspect, we use a Bayesian approach. And all that means is that we're looking at history to see if we can predict the future kind of like you're looking at stock charts, or financials. And what we started to do is to say, can I predict and find the most common things that helped me understand, like when somebody makes the most amount of money in their life, when somebody their first child? And then can I break that apart by, well, is it because I'm of this type of a person, I'm Native American Indian, my wife is Korean. Or maybe it's my type of job, maybe it's where I live. So we started breaking the data apart, segmenting it until we found patterns. Once we found those patterns, could we use that data to say I want to predict from year 20 to 19. And then does that hold true for 19 to 18 to 17, and then 20, 19, 18 to 17. So we use the machine to do what a computer can do really well read a ton of data, see correlations see data and say, hey, this is highly correlated, or it's completely disparate. You don't need to think about it. And that's what we do. And that's kind of how we started.

Richard Walker 8:27

So in my career of running my software company for 20 years, I've had several different dark moments. Like, I don't know if it's gonna work, I don't know if all this investment I'm making is gonna pay off and turn into a product. How dark was this for you?

Robert Kirk 8:41

It was dark enough that I had to tell my wife I'm about to spend x amount of dollars and I had to get her blessing. And she said, okay, so remember that trip. Remember that car? Remember this stuff you wanted? It's no. It's the decision of do I really want to put my money where my mouth is and say this is when I believe the moment I cut the check the moment I wrote that, it was probably the most stark moment I say, stark, not dark. And here's why. Because at that point, I said, if this works, we can help so many people. If it doesn't work, I'm gonna have to work a few more years to get back to where I was. So it was really interesting for me, and it started the path of me being the entrepreneur every day. It's a great exhilaration, the most promising time but the most frustrating and painful time at the same time, so I have to deal with it.

Richard Walker 9:42

Yeah, that is, I mean, I'm an entrepreneur at heart. I started my first business at age 12. So I get it and I love it. And it's not for everybody. And I'm really, really glad that you took this on. So I have insight that our audience doesn't have yet because I saw you at T3 and you gave me a full percent. active on what you've been building. And honestly, it's phenomenal. So I want to dive deeper into it. How many data points are you now collecting?

Robert Kirk 10:07

We're approaching 15 billion data points.

Richard Walker 10:11

Wow. And so what, what kind of life events are you now predicting? And How accurate do you feel these predictions are?

Robert Kirk 10:20

So let's go with the accuracy part first, the accuracy part is every one of our predictions has to meet. Going back to a Bayesian approach 20 to 19, 19 to 18, it has to hit 94% or higher before we release it, and say, we believe we can do this prediction. There are some firms out there and I don't need to name names, but you've got to think of it in terms of percentages. If someone says, hey, I'm 75% accurate, that's good. But flip it around, I've got a one in four chance of dying. That's not so good, right? So the better the accuracy, the more finite, we can drive the actual prediction to you. So you versus me, even if we're the same age, making the same amount of money, but we're living in two different areas, we're likely to have two different sets of data. And that's typically what we found. So it has to be 94%. accurate to the individual. So for me, 54, Native American Indian, CEO of financial services in Dallas, Texas, it has to be that relevant. And at that 94% accuracy. So that's the second part to your question. The first part is really, we're now at the point where we have 95 different life events. So if we look at them in two major categories, the first category are things that we call wealth. These are things like, when you couldn't get married? Who are you likely to get married to? How many children? Potentially how many divorces? How many other marriages? How many children for each marriage?

Richard Walker 11:51

Oh, wait, wait a second here, people go to psychics to find out how many children they're going to have? How are you predicting how many kids somebody's going to have?

Robert Kirk 11:59

So we have this statistics that I'll go through and tell you by your education level, by your demographic by who you are, by where you live, who you're likely to meet. So as an example, the easiest example is, who did doctors marry? Doctors marry doctors and nurses. Why, that's in their sphere of influence. That's who they're around every day. And who to nurses marry doctors and first responders. Well, who the first responders marry, they marry nurses, and then who do they marry school admin people, because that's who they're around every day. It's like saying, hey, Rich, you're likely to marry some supermodel from Italy. Well, if you're not a cameraman, if you're not working in the film industry, if you're not flying across to Italy, your likelihood of meeting them is zero. Now, from the data perspective, if you get married, or you get divorced, you have to fill out paperwork, that paperwork gets submitted to the government, so they know who you are. They know your race, they know your gender, they know your job, they know where you live, they know what you make, they know all of the things about your marriage and divorce. Think of all the paperwork that you have to do that data is publicly available, while other veterans not but we combine all the data sets. And that's how we start to plug in the data.

Richard Walker 13:25

Alright, so sorry for interrupting keep going. So your wealth building, you have these events that affect your wealth building.

Robert Kirk 13:31

Yeah. So in that sense, it's also number of car purchases, home purchases, when you start businesses or how much money you're likely to make. I mean, that's wealth-related. On the health-related side, it's 81 critical illnesses. So not just cancer, but 27 types of cancer 22 types of heart issues, we have brain issues like Alzheimer's and dementia and the Alzheimer's, that's key to me, because that's what my grandfather went through. And we do this, if you want to think about this, from a predictive standpoint, all of these are genetically based types of, or what we would say, inherent into your own DNA that get passed down generation to generation. And fortunately for us, the US government says, there's really two different ways you can do these types of critical illness predictions. The first is DNA. But no one's going to go to a bank, no one's going to go to Charles Schwab and get their blood drawn and have it spun around in sequence somebody's genes. But the other way, and that is, by the way, the best way to understand your critical illnesses or what you're likely to get disease-wise, from the other way, which they recommend. It's really to look at your heritage, look at your past generation, your parents, your grandparents, maternal and paternal because these genetic diseases increase your likelihood and they're finding, smoking is more genetic. They're finding other areas, such as like the bracket gene or hair loss. These are genetic traits. So we use that data create a math model and then basically say, based on who you are, where you live, what you do, what other influences? These are things. And here's a probability and prevalence of these diseases or illnesses, according are specific to you.

Richard Walker 15:12

Wow. So if you're an advisor leveraging this data, and I'm presuming advisor could leverage this data, are you asking your client to provide a bunch of metrics that then you plug in and evaluate and predict? How does it actually translate to a customer?

Robert Kirk 15:28

Yeah, so there's a couple of ways of doing this. One, just know that we don't want the adviser to become a doctor. We don't want to show them all the data, unless they're the true data science guy, and they want to go into all the nth degree of what we do. We're happy to walk them down that path. But most people, what they need to do is they need to understand people like you Rich, living where you live, doing what you do, here's typically some of the things you're going to go through it your life stage. And that's big data coming in and giving you very big insights. Your children might be going to college, you might have just moved, you might become an empty nester, here's the products and services that most people like you need. Now, let's get specific. So have you had questions or thoughts about critical illnesses, because that's something that typically happens to people your age, it may not be you, but it might be your family. And all of a sudden, it becomes conversational, you're like, that's interesting. My grandmother had cancer, my mom has just gone in for a biopsy. I wonder what that means? Well, instantly, right there. That's all the data we need my maternal grandparents, my mother, and then all of a sudden you start building that mathematical formula. So we do it in that sense, where an advisor can be empowered to ask these questions, or they can use our UI to kind of tap through and say, this is what they've had. But from that perspective, it's very light. It's really just did your parents have arthritis? Or what your grandparents and your maternal paternal side? And just that simple question, yes, no, one or both. That's all we need. And then we get started, we've already done, the heavy lifting, and the predictions already had a time. So now that's just access to the insight.

Richard Walker 17:10

Okay, so what kind of companies are leveraging your data? And do you have a favorite customer success stories you might tell?

Robert Kirk 17:16

Yeah, so there's a couple of different ones. And I have to be careful, I can't name the customer who can do is I can tell you this. So banks, clearing firms, insurance companies, home health care companies, they've been the biggest users of our data, we have a couple of success stories that we think are really, really key. One is from a group insurance perspective, we were able to help a company that is looking at their benefits that they provide. And we were able to help the broker who's helping them decide what benefits they need to actually try and use throughout the year. Now, open enrollment comes every October, it's a really horrible time you go in its PDF, Purgatory, you click, click, click PDF, click, click, click PDF. And then you're sitting there saying, I don't know what to do. And so I go to somebody, I go Rich, what did you pick? High Deductible, low deductible, this insurance how much, right and it ends up becoming a really tough thing, because you're going through that not knowing sure exactly what you should pick. But Rich, were not the same. But I'm still asking, as it were. So what we were able to do was take the data from this company, we do not take in PII. So it's de-identified anonymized data. And then what we did is we helped them look at their employee population and say, hey, 42% of your population is really good. They've got the right product mix, they've got the right insurance, that's great. Tell them great job, thumbs up this 15 18%. This is kind of interesting. They're about to get older, they're not really hitting some things, a few bumps, $10 a month here will really help them something really small. But then there's these two other groups, right, and it was almost, I think, 20 to 28%, right around there. That literally meant, hey, these groups, they're going to need a little bit more money, they're likely to have a lot more life events, illnesses. Now, that could be two things, it could help you on your downturn in terms of customer, or let's just call it productivity, you have productivity hit from a company perspective, but you also have employees, you want to take care of, hey, we should really start helping you this way. Let's look at other things. We can do dietary needs. Maybe we give you other benefits to help you start working out or do something differently, various ways that they can really humanize benefits to say this is for you. We were able to provide that because we found a $20 million gap within about 35% of their employees between the insurance that they're likely going to need and claim over the next five years versus what they have today. So tell them today in advance. That's one of the great things we were able to do. And then I'll get maybe one other...

Richard Walker 20:01

Before you get that, if there was a $20 million gap, did that mean the company, the employer had to buy more insurance than it may have had to offer more products? Did it mean that you could inform the employees to buy up their products? But what did it mean actually?

Robert Kirk 20:18

It actually meant that they were able to give them information that says, hey, we've identified that people like you tend to go through this, you may want to consider more insurance, you may want to consider this for the next three or four or five years, or even 10 years. So it gave us an opportunity where the employer is looking out for the employee, what we didn't want to do and this was many of the conversations is tell them, you have to tell them, they have to buy this now you don't have to. But if the employer didn't offer it, well, there's two choices, the employer can then offer it and say, hey, 50% of our company is going to need this, let's give it to him. Right, that's a great thing. That means you're looking at the other side, if would be, let's say, on the conscious side to that, let's say it's only 5% that 5% say it's not big enough for us to offer it. But work with the broker and say, Get us a good deal. So you're the company asking the broker, get us a good deal for this group of people help them out. So now what we're doing is your giving them that ability to buy up but still buy something that they need. It's really about taking care of the person, the employee.

Richard Walker 21:20

Yeah, and having the opportunity to understand it, so that you can address it well, before you need it. Like you don't want to buy long-term care of the day you need it. You got to buy it ahead of time.

Robert Kirk 21:30

And that's actually one of the learnings we. So sadly, if and this is for anyone who knows anybody, if you're going to put somebody into a long-term care facility, their money needs to be in a trust five years before that actual date. Why if it's not done five years, if it's four years, 364 and a half days, and you just miss it, the government has the ability to take all the money, you sell the house, they take it all, they take all the bank, they take all the savings. So if it's not putting trust in that five-year rule, the only way you know about it is if somebody tells you where you've missed the date, and somebody has gone through it, and we went through it, so we missed the date. And the government unfortunately, they took all of my grandfather's capital, so nothing you can do have to live with it.

Richard Walker 22:21

Yeah, yikes. Wow. So I interrupted you, because you had another story you wanted to tell, which I'd love to hear?

Robert Kirk 22:27

Oh, so the other success story, which we found really interesting is in a marketing scenario, we had a company who wanted to know more about their other customers. And what they wanted to do was really simple. They wanted to go market this specific product to them. And they said, here, we're gonna give you this data, you tell us about the customer, you rank and prioritize who we should go after, they gave us about 15,000 names within that we identified 500, they should go after we said this is the group. Don't worry about everybody else. This is the group. Now this company is a large insurance company record keeper, they're well known very big in the industry. And their initial data came through, they have a very high open rate of 25.9% for their emails. That's way above anyone that I know, they are probably, I would say probably the best in the world. We were fortunate to work with them. They gave us the opportunity we went through prioritize those names. And with our data, we had an open rate of 48.7%. We almost doubled what they were able to do, which meant two things. One, we identified the right group that this is top of mind what their product was fit their needs. Then, as you know, comes the next thing, well, what's the call to action? What's the next thing? What are they going to do next? So we both went through and we went okay, this is going to be great. Yes. And this is a tale of being an entrepreneurial get this. We said oh, we're gonna look forward to the numbers. And we both came in at the call to action next step at under 1%. And we're like, what happened with how do you go from 50%, opening it to less than 1%. And then we've realized it was one of the analysts spoke on the phone and said, well, you, you did send this during Thanksgiving and Christmas. They're probably spending them and we went oh, that's a good point. But so we did is we did another AB test with their group. We sent out the data again, we saw similar rates on the open so we knew we're identifying the right people, different group. And now we're going to measure and see how that next group comes in. So we think over the next few months, we'll have a full six months of data that says initial onset, initial open rate, follow-through purchase. So it's the tailor, so everything is great. Oh, no way good. Still good

Richard Walker 25:00

That's entrepreneur's journey.

Robert Kirk 25:04


Richard Walker 25:05

Wow. Okay, look, I want to switch gears a little bit because you are into AI, you've built your own AI, which I don't meet a lot of people who have. There's a lot of leveraging of AI, but building your own data sets, etc, is really impressive. But I want to ask you a slightly different question. Do you see AI with the kind of data you're doing possibly changing how people buy products like insurance? I mean, today, the 80-20 rule applies, right? 80% of the people don't need it. 20% do. So it's all covered. But what if you go into this analysis, and the insurer says, oh, Rich, we predict you're gonna have cancer at age 55. We don't want you now. Do you see something like that happening?

Robert Kirk 25:46

So the regulatory agencies, and the companies are very scared of that, right? You don't want to discriminate? That's something that you shouldn't do. Now, this is we're in a really unique place. I love how you staged the question. We're in a really unique place, because insurance companies and the actuaries themselves, they want to know this data. So a good example would be, I'm Native American Indian, I have a higher propensity for type two diabetes at the average age of 54. I'm 54. For the last few years, since I've known this data, I've started cutting out my sugar. Do I still need to lose weight? Absolutely. Am I on that path? Yeah. But I'm more cognizant of it. That helps lengthen my longevity. So in that sense, my wife who's Korean, well, guess what, she has a higher propensity for cancer, I don't, she does. And in fact, she did get cancer a few years ago. And thank god, she's in complete remission, but she got two cancers at once, which is, so she goes through that? Well, some people might say, well, you can't provide data to me as a Native American Indian, and you can't provide data to that person who's Korean and a female, because that might infect or affect the way we might want to market to them? Or how do we know I want to know this, because you know, what, my children are a mix of both. They're gonna have different propensity models, if you really want to help me, you want to help me prepare for my future, you don't want to help me prepare for the average, because Native American Indians or Koreans were not the same. Genetically, we'd have to say, and that's okay. And by the way, male, female, we're not the same. Even if I have a sex change, even if I change my name, I have 0% chance of getting ovarian cancer, I can't get it. Right. Yeah, there are certain things that we use within the math and the model and the data and the genetics and says this is it. So if you're going to hide that data from me and not use it for me, then some might say, Are you mislead? I'm not going to say people are lying, I'm not going to say people have this. But they might take that group mentality. But here's where this really gets interesting. Other states are trying to say we need to make sure AI is unbiased. Well, how are you unbiased if you don't show the different disparate datasets of everybody? Right? If you're not transparent in that, then how do you prove unbiased around Colorado, New York, and they've already put bias laws out there. So this is already starting. So the reality is, the data needs to be there. In my personal opinion, in our corporate, the way I'm driving the company is we want to show transparently the difference between it's not to show, differences, it's to celebrate who we are, but also to plan. Because if we can plan better, we can help the individual, we might save more, we might do more, guess what we're going to live better lives. That goal, whether it's insurance to protect a person, or whether it's accumulation, or whether it's deep accumulation. You want to show people we can help guide you up, save and help you live and then pass on that money to generations, the only way I think you can do it is Be transparent the data. So we're at a really unique time where AI rules, group insurance type of mentality, data sets, AI, all of it is intertwined. And we're going to we're going to come to a head I believe I'm on the winning side. But we'll see.

Richard Walker 29:17

I think you are because I think what you said is important to repeat back, which is you as a person want to know the differences that are unique to you. And the corollary to that is going to a doctor, I wouldn't want a doctor to look at me differently than who I actually am. I wouldn't want to say, oh, well, you might be female, when I'm definitely male, and try to treat me for female problems that are definitely not afflicting me. Right. And we also know that there are propensities. My wife's a nurse, she was an ER nurse. And we know there are propensities for things like sickle cell that affects a different population than others for heart disease, et cetera. So I think it's fascinating to look at it from that standpoint. And the corollary is that There is not as far as I know, an insurance product out there for white males in their 50s versus, white females in their 40s. I haven't heard of that product.

Robert Kirk 30:11

So it is funny because that's ultimately the question. You don't want to discriminate. So do you create disparate products? Well, in some cases, actuaries have enough data, they could do that. They could create multiple products. Oh, yeah, I would, and other places say no, that's bad, you're going to inherently create this bias or whatnot. And I laugh because I say, look, if I went out, and let's say both of us are the same age, living in the same city, doing the same thing, making the same amount of money, but I get 10 tickets a month for speeding, and you don't get any, I guarantee my rate goes up, I guarantee you, I'm paying more I guarantee or the point they're going to stop doing or cancel my insurance. Also, if we lived in the same place, but I lived on the side of the volcano and you live down by the see, I'm probably going to pay more if it maybe not get, you know, insurance for the home. Same thing for money. So it's interesting to me that we can be I'm gonna say it this way biased in those types of things. But when it comes down to my health, why not? COVID it let's go back to the behemoth right. What did that teach us? It taught us that black, brown, yellow, green, purple, whatever, people of color got affected more than people who are white. There's nothing wrong with that. We just need to know how to treat it. But if you're trying to tell me that I'm the average white person, I'm not. Not in a negative sense, in a way that says, Help me Help protect me help insure me help me accumulate money helped me save money, help me help my family.

Richard Walker 31:43

I love this perspective. Okay, I have one more data question for you that's been burning in my mind. Is your data only US data? I mean, what happens if you have an immigrant from India or South Africa or Australia? How does your data model affect that?

Robert Kirk 31:57

Yeah, that's a really interesting way to phrase that. So today, about 90% of our data, and I say this in total, right? So let me state it in its simplest manner. Yes, we're US-centric. However, we're collecting data now from Canada, from the UK, from Singapore. After COVID, all of these countries started producing more data, and produce it in a way that helped understand how the disease and how all the comorbidities because it wasn't just the disease, it was a disease plus, or the virus plus comorbidities that made it exponential. So whether I was hospitalized or died, it really, really affected us. So within that they're making this data available. And we're starting to collect all of that. Now, to the real, I guess you would call it central part of your question. If you're from India, and you are Indian descent, and then you move to the US, you're still Indian. Genetically, it's the same. Now your density models are the same, the differences would be okay, what type of job the type of stress. So we're finding not just the genetics, we're also finding the type of job you have in where you live also influences the likelihood probability and prevalence of you are not the prevalence is there, but the probability of you getting this disease, more stressful areas lead to higher types of cancer and lead to overeating lead to over drinking lead to, right versus if you have that less stress, it's not as much. So we see those pockets, you're still likely to get these diseases, but your probability isn't as high and the type of food you eat. So all of this does play into account. But we still go with the basis. If I'm Japanese, in Japan, or from Japanese and Russia, I'm still Japanese in the US. There's no real difference, right? So it does see we see the model. Now we're going to try and see if it goes the other way, too. And we're working with companies overseas to say, how does our model look there? Where can we grab the data, and then we'll have two sets of data, A and D, to then combine and run separately to see how it works. That's the beauty.

Richard Walker 34:08

I guess another way I could have asked my question is, is America enough of a melting pot to have enough demographics and socio-economic information to help somebody of another country who's immigrating here?

Robert Kirk 34:20

Yeah, it is. If you think about the population in terms of size, you know, we're 70%, white, 30% Hispanic or whatever, that's still a large enough population is 30% of 330 million is actually quite a population. Right? So 20%, if you're black, or if you're Asian, or if you're this, it's a large enough we the vastness of the US, meaning the disparate types of urban suburban areas that we live in, as well as the types of stresses in models. It does provide a good proving ground because You're talking about 330 million. In a course of over 8 billion people, statistically speaking, were significant enough. But if you want to look at it, I would say this, from my perspective, as a Native American Indian, there's only 2.643 million of us with a really, really tiny data set at that end. So it wouldn't be as good for us. But for the larger populations, absolutely.

Richard Walker 35:25

Yeah. But and there's still other things that are applicable, the fact of where you live, what kind of job you took, etc. Man, Rob, this has been so fascinating and interesting. And I really admire that you took a personal challenge and decided to build a business, a hard business, by the way, and risk it all and go for it. I love, love, love entrepreneurs like yourself for doing that. And we unfortunately have to wrap this up. So before I ask my very last question, I want to know how should people connect with you? How do they find you?

Robert Kirk 35:54

Oh, it's really simple. So you can go to If you go to about us, you can see each of our all the people that are on the team. Click LinkedIn with us, we're on LinkedIn, you can connect directly there, email us directly. So We even put out our phone numbers, my cell phone numbers out there and gladly take calls from individuals. And I'll even be bold enough and say 917-680-8702 be more than happy to speak to everybody and anyone. So feel free to reach out.

Richard Walker 36:28

That is awesome. Man you're so open. That is great. All right. I still want to keep talking about this. But here's the last question, who has had the biggest impact on your leadership style or how you approach your role today?

Robert Kirk 36:40

The gentleman that I would say has probably impacted me the most is an ex-colleague, who then became my boss before I went out and started this company. His name was Indra Nell Roy, is an ex-CTO, Barclays, Japan, very well-known ex-city. He's been the most influential, I would say that my life has been led in thirds, the first third of my life, I chased after what I thought was, who I needed to be chased after the dollar and a person. The second third of my life, I followed a gentleman who is very well established and did very well in the industry, very well respected. But the last third was probably Indra Nell, he really sat down and helped me understand how to interact with people on a greater level, that no matter what happens, it's water on a duck's back, and that it just rolls off. And in the end, it was whenever you get into a disagreement or an argument or thought about somebody, it's always think about them, if they're getting heated or that person is getting annoyed or mad. Think of them as a little child and say, somewhere back in their history, they had a pain. Do that you humanize them? You forget about your pride, you forget about your intentions. It's how can I help that person? Because my job as a leader was to help them, not to show them teach them. It's not to be parochial. It's I want to see them succeed. And he taught me that the key to my success, was their success. And that's what I would have to say Indra Nell has been, he's a student of life, but he's also just an amazing man.

Richard Walker 38:28

Man, that is so awesome. This is why I love asking this question. I love these types of stories and hearing all this. Rob, thank you. So I have to give a big thank you to Robert Kirk, CEO of InterGen Data for being on this episode of The Customer Wins. Go check out Rob's website at And don't forget to check out Quik! at where we make processing forms easy. I hope you've enjoyed this discussion, will click the like button, share this with someone and subscribe to our channel for future episodes of The Customers Win. Thanks for joining me today, Rob.

Robert Kirk 39:01

Awesome. I appreciate it all the time Rich. Peace out man, peace out.

Outro 39:06

Thanks for listening to The Customer Wins podcast. We'll see you again next time and be sure to click subscribe to get future episodes.


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