Technology: The Future of Insurance Part 1

Season 2 Episode 13

Technology: The Future of Insurance Part 1 with James Benham

Season 2 Episode 13


Intro/Outro: Welcome to the insurance leadership podcast, the podcast designed to bring new perspectives and principles from leaders in the life and health insurance industry. We trust you will enjoy today's episode.

Ryan Eaton: Good morning, and welcome to another episode of the insurance leadership podcast. I am Ryan Eaton, your host, and honored to have you with us this morning. Today, we're going to be doing a deep dive into some technology terms that we really don't use that often in the insurance industry. Terms such as artificial intelligence and data mining, and possibly some other terms that are not in the average insurance professional’s vocabulary. If you're anything like me, you hear these terms and you think tech industry, you think maybe the movie iRobot, but they are definitely things that we see in our industry and they're all around us every day. So, we brought in someone who is, I think, personally, a phenomenal speaker, but also someone who can take complex terms and make them simple for us.

So, hope you enjoy the episode. So today we have James Behnam on the show. For those of you who don't know who James is, he has been in the insurance industry for about 20 years. He started his first company, which was a software development company while he was in college, in his dorm room and started doing different things for insurance companies.

Now, I don't know about you guys, but I was definitely not developing systems for insurance companies at the age of 19, from my college dorm room. That impressed me about James to start with, but he's also a speaker. He's also got a podcast show called The InsureTech Geek that he has been leading for years now.

He develops technology systems. He's in the process of training for a half marathon and he just got his single-engine commercial seaplane license. When I tell you we have a great speaker on the podcast this morning, I'm excited to have him here, James, how are you doing? 

James Benham: Thanks, brother. You know, you've got to always keep pushing yourselves and I get real bored.

My mama always said that the idle hands and the devil's work. So, I I, I try to stay busy you know, teaching myself new things and learning new stuff. 

Ryan Eaton: Mama was very accurate. So, so look, James, one thing I've learned is that people don't tend to get into the insurance industry on purpose. They kind of stumble in why don't you tell us how you got into the industry.

James Benham: Yeah, I think I tripped, stumbled, and fell. When I started JBknowledge it was April 16th, 2001. I got a degree in accounting, which I think is a wonderful background for insurance. And I'd been writing software since I was about 11. I got really into programming in middle school and high school, and I went to a real nerdy engineering high school down in south Louisiana.

And I was finishing up college, I did an internship. Two of them, but price Waterhouse Coopers as an auditor. And I really liked the company. They wanted me to come work there. Full-time and I, I said, you know, I just need to chase my own rabbits, you know? And, and so I, I called my dad up and we pulled together 5,000 bucks and we started JBknowledge in my dorm room, I was finishing up my undergrad. 

I, I ended up going to grad school right after that. And so, I, I would go to grad school during the day, and I would write software at night. And I did that for another, you know 14 months before I finished my masters. Once I got out, it was kind of off to the races.

You know, a lot of things happened around that time. Mainly 9/11, we had a big ".com" boom and bust. And then following the bust, then we had 9/11, which further constrained the economy. So, what a terrible time to start a business and what a great time to start a business. Tragedy often breeds a lot of opportunity and it was a really interesting time to jump into the technology business.

I built websites and software for dozens of industries. Lumber manufacturers, injection molding plastics manufacturers, nonprofits, executive search firms. But in 2004, I landed my first, my first big insurance client that really did a lot of insurance. Before that I'd had some brokers and producers that I had built some stuff for, but I landed a really interesting gig building inspection software so that when a property.

Got insured the, you know, the, the underwriter would order an inspection. Our software would connect and manage that inspection between the carrier and the inspection company. And I got introduced through that and to USAA and nationwide and state farm. And, and I discovered this whole world of insurance technology and, and the, you know, these companies had had large technology budgets.

They had huge needs, a lot of benefit. And it was a glove in hand fit from that day on it. We've been building an insurance software, both doing professional services and building products. So, we've got, we're a unique company in that we sell our time and we sell our products. And today we've got just under 270 full-time employees across the world working for some pretty large carriers, brokers, TPA's TBM. And it's been a really fun ride in insurance.

Ryan Eaton: Awe. That's awesome, James. Well, you've definitely built a great business, a great model, and I appreciate you being on the show today. So, with that, let's go ahead and hop on in, you know, we, we brought you here because I want to talk about technology and I want to talk about some of the terms, the artificial intelligence and these other things that right now we're hearing in our industry.

I thought before we get started, I said, let me read the Webster's dictionary version of this. Because for most people, it kind of scares us a little bit when we hear those type terms. Cause it's the uncertainty, right? So artificial intelligence is simply the ability of machines to demonstrate intelligence rather than humans generating intelligence.

So basically, to me in simplistic terms, that means a computer understands that when this box is checked, that this procedure takes place. Am I, am I oversimplifying that, James? Is that, is that the basics? What's that.

James Benham: That’s the basics. If this, then that, okay. That's actually a website by the way. ifttt, if this, then that that's a program, but that's the fundamental of most computer science.

Up until now has been really programming systems to accept an input, to have conditional things that happen. If this happens, then do that. And then to store that data, process it, and then regurgitate it onto a screen. That's really been the vast majority of simple. Yeah, it's that simple. I mean, that's, that's really what you, what you learned in the early days of program before you jump into artificial intelligence, machine learning, computer vision, all these other things.

So, you're not going to talk about the fundamentals of computer science is handling input processing and storing that data. And then outputting that data again. That is the essence of all software now.

Ryan Eaton: That's good. So look, James, on this podcast, we have a lot of agents and carriers and TPA insurance companies listening in.

You've talked before about ensuring your future through innovation and insurance. I want you to hit on that for a minute and share what you've seen. Other agencies, carriers, et cetera, do to kind of set themselves up to stay ahead in the business. 

James Benham: Well, if you think about disruption and my good buddy, like a good southerner, I got a good buddy.

That's got a saying for everything, right. Except, you know, in Louisiana, my good buddies named Boudreaux and Thibodeau yours is named Bubba and Jim Bob and and Jimbo right here. 

Ryan Eaton: They'll be on next month. 

James Benham: So, my good buddy, Brett, he said, disruption is a business model change where segments of an industry cannot adapt.

And when you're looking at that, that definition of disruption, you're looking at what that really means that industries are going to persist, right? Industries are going to persist, but individual companies will likely not. And if you look at the number of companies that have rolled on and off of the S and P 500 in the last a hundred years.

The vast majority of the S and P 500. Now it's completely different than it was a hundred years ago. So, it is a, an unfortunate reality for us business owners. We think we're building permanent institutions. And the reality is that disruption is going to occur to us or by us. One way or the other, our companies will either be the disruptor or be the disrupted.

And so, when you look at technology and technology adoption, and you talk about ensuring your future, and the reason I use that phrase ensuring your future is that we're trying to ensure the future of business. My dad has a bunch of hard and fast rules about business that he learned growing up on a farm in Southwest Mississippi.

And most of his analogies are farm animal analogies, but you know, the one thing he really learned growing up in the depression cause he was born in the third. And he had to endure the Southwest Mississippi during the depression was survival. And so something he told me, James, the number one rule of business above anything is survival, survival, survival.

And number two is Cash is king. Number three is everything is the trade off. And I wrote a whole book that's coming out soon that, that recaptures my daddy's rules on business, but it's, it's really important to understand that our main mission has companies is to survive. Secondarily it's to generate a profit and cashflow for the dividend, dividends for shareholders, and obviously also to deliver value to our customers.

We can't do any of those things apart from each other, but if we're not constantly innovating, we are going to be disrupted by a new entrance. I, it was funny. I was watching yellow ever watch Yellowstone. I have, yes. So John Dutton was talking about his ranch and he says, you know, the thing about getting things is when you have a lot of something, then somebody else wants it.

And that's just human nature. And he's talking about his land having to fight for his land, but it's that way in business too. When you grow a business, someone else wants your business. And they're going to innovate and do anything they can to get it and take your customers, take your revenue. And that applies to carriers, brokers, TPA's pharmacy benefit managers.

There's always somebody new chasing that hurdle, right? They're always chasing that ring. And so, innovation allows us to stay one step ahead. Unfortunately, individual technologies are not in themselves. Sustainable competitive advantages that the sustainable competitive advantages for us as businesspeople and businesspeople on an insurance.

Is our approach, our process, our culture that we create for innovation. Now I'm a builder of technology tools, but I recognize that those tools will soon be out there. Right. I mean, we, we no longer use VisiCalc for spreadsheets. Now we use Excel. Right. You know, VisiCalc got, got ousted by Lotus that got ousted by Excel, you know, which is now being ousted by Smartsheet and others.

I mean, so there's, there's always something. But we've got to constantly be retooling and improving our processes so that our logos are not in that graveyard. 

Ryan Eaton: What would you suggest to a broker? And before I ask this question, just so you know, rip was actually in Jackson filming a movie last month and he had a dinner at one of our favorite little places to get steaks here in town. But with that said I digress.

A broker, if you're looking at it from a disruption standpoint and, you know, we saw it with a company last year, we were, we were actually looking from an acquisition standpoint, good company had done a great job, but they had fallen behind on the technology side and for this guy to be able to update his technology at his age, tenure in the game, it was almost going to be impossible.

And it had gotten to the point where it's going to cost him all of his, all of his net income, basically, to be able to do that. What would you say to companies that are out there to, Hey, look, I might be a little late to the game, but I'm not out of the game yet. I don't want to be disrupted. What would you recommend them doing in that situation?

James Benham: No pain, no gain. Right. Short-term pain, long-term rewards. And what I've always said, as long as it doesn't exceed your profit margin. There is no cost that's too great. Right. And you know, survival is more important than margin. And you know, if you've got a wipe out your profit margin to catch up, then you've got to wipe it out because the, the alternative is being wiped out as a business, that there are people who incur and in technology, we call it technical debt.

And technical debt. That is just this debt, that you naturally incur over time where, you know, you have the program's age, your software ages you can also incur technical debt by introducing bugs into your system. That's another way of getting technical debt, but a lot of technical debt is due to obsolescence.

And that's what you're seeing there. Modern broker to services is very different, and in particular, the expectations of customers. On how quickly they want to get quotes on how they want to be presented the information on the options they have online to get their own pricing. Right? I mean, there's, there's a lot that goes into this and we've worked with we've, we've built programs now for two pretty large brokers who were trying very, very much to stay ahead of the curve because they're seeing a couple of things happen at the same time in the broker space, fee erosion, right?

People, buyers are getting smarter. They're they're negotiating fees. They're doing fixed fee brokerage, you know, there's, there's fee erosion and there's also a kind of a detachment with loyalty to the broker where they're, they're, they're more likely to jump brokers if they can get a better deal. And so you're seeing a lot of, a lot in that broker community saying, Hey, we've got to, we've got to have a suite of technology services that connect with our customer.

So that they're way stickier and way less. To shop around every single year on brokers. Right. And we all know that, that the carrier markets don't like getting the same submission from three different brokers, but their clients don't necessarily know that, you know? And so there's a lot of behavior. It actually ends up penalizing the customer.

If there is they're engaged in that and that the insured and that behavior, but brokers in particular have to recognize that their real existential threat. Is the erosion of fee revenue and the detachment and loyalty with your broker, your broker used to be your best buddy for your whole life. And that that's kinda, that's kinda changed a little bit and, and, you know, cause there's so many data offerings now and there's so much technology that stacked on top of it.

So now you're looking at, Hey, how are they serving me as a technology consultant as an advisor? Are they stepping in helping me analyzing my wrists? That's really what a good. A good broker risk manager does. So you just said 

Ryan Eaton: something I thought was very interesting as kind of the erosion of the broker relationship.

And especially when you get to kind of, if you're getting an individual insurance, especially like on the life and health side or P and C side, there's so many quotes and enrollment that can be done online now. Right? Like they're there, you can go to any website and you type in, you know, car insurance, quotes, health insurance quotes, you're going to have.

255 pages of different quote and enrollment platforms out there. What are some of the things that technology companies are doing? Maybe, maybe from an artificial intelligence standpoint, to be able to kind of track that customer's journey to make better decisions on how they can capture those individuals, the data they want to capture, kind of seeing them through the process to make sure they stay on the page longer.

What's some of the stuff you're saying. 

James Benham: Yeah. So that's really, you can kind of put that under an umbrella called user experience. Right? So there's a great picture that helps define the difference between user interface and user experience. And there's a picture of a sidewalk and we've all seen this in our real life and it's a right angle.

So you're, you're coming out of a park and you're. Okay. And, and th the way the city planner designed that park is that the sidewalk goes at a 90 degree angle into the intersecting sidewalk. Right. It's a corner we love, we love 90 degree angles. We love them. They're great. They're beautiful. Right. And so, but there's grass on both sides.

Well, what did people end up doing when they have to turn left or right across the grass? Now, eventually now, th this happened at my local donut shop. Now I'm a Southern boy and I do like my donuts and a week in Baton Rouge, I grew up with Mary Lee donuts, which are. I came out here to Texas and we got Shipley's and Shipley's, it's just God's gift to mankind.

That is those buttery sweet goodness. I mean, and my Shipley's had the same thing situation. Th there they had a right angle. They had the sidewalk going to the store, the sidewalk from the parking lot, but there was a little piece of dirt between two gardens that 90% of people were using that dirt path as the user experience.

Okay. And what Shipley's did, that was really smart by the way, as they observed the user experience and they improved it, they ended up paving the dirt path. That was the cut through that everybody was using. And that's what we've got to do when we're looking at the customer experience. And brokerage what a customer is have.

Well, they hate filling out forms. And I can tell you, I hate filling out forms. We all do. What do we hate doing? I hate filling out the same form every year when things haven't changed, what most brokers do, email a form, the same form every year. So there's simple things you can do, like creating your own database to store your customer information.

And pre-populating the forms for the next year. And just asking them to update the. Right. That's a simple, that's a simple change that every broker can engage in is look at your user experience. What are they? Where's the dirt paths that they've formed to get this done. All right. What about, what am I competitive threats?

Well, EV I mean, there's a lot of carriers out there. Want to disintermediate the broker, go direct to consumer and cut the commission now. Right. But they're finding that that distribution model is really challenging because their ad spend is so high. That they're better off going back to their distribution channels.

So you're seeing a lot of InsureTechs that originally started that originally started to go direct to consumer are actually going back to distribution channels through brokers and B because of the cost of customer acquisition that just the sheer marketing and ad spend is too high for one company to absorb us why the distribution.

It's, it's real interesting. You're seeing a tug of war there. That's right. So I think that in your business that you've got to really look at the user experience, look for those dirt paths and start paving them and say, what, how do we, how do we pre-populate forms? How do we, how do we use public data?

And when we say this sprays big. Now there's a professor from MIT defined big data. His name is Michael Stonebraker. He was wicked spot he's from Boston. He, he, he, he defined big data as big volume, big velocity, big variety, a whole bunch of data from a bunch of different sources. Moving quickly. We have more data at our fingertips available to us than we ever have in human history.

And we have more data on properties and more data on people than we've ever had. And on companies. How are you leveraging big data? So you're taking that 200, 300 page questionnaire to get a quote down to 20 questions. How are you pulling all the data that exists out there? And you're seeing this a lot with the big, big list.

Let's just use laminate as an example right now, because they're a big public company. Their underwriting questionnaire is pretty much Darren dang T to be done in under a minute because they they're leveraging every public database. On property. They're usually leveraging every public database on tax rolls.

And they're automatically determining, based on your address, where it is, what floodplain it's in, how many bedrooms and bathrooms you have, how many square feet it is from the tax rolls, right? They're pulling all this public data on your property. So you're not having to ask that. And which means that the customer experience is 10 times better getting a cry.

I mean, you're talking about, you're talking about initial. So quote to bind and under five minutes and it doesn't have to be just carriers that do that. Brokers can do that too, but they've got to get real serious about their technology. Well, James, I love the 

Ryan Eaton: predictive analytics piece, kind of you're hitting on right now where you're talking about the more data you have quality data.

We got a guy at our office who always says garbage in garbage out. He's talking about our CRM system and you know, people with. People misspell the names and don't, you know, they, they mess it all up. And then, well, I don't know why it went wrong to this guy. You know, it's a, it's one of those things, but how are you seeing predictive analytics?

How would you describe that? I guess, you know, I have a definition, but I'd like to see kind of really your, your words on kind of what you may say. How are you seeing people use it? And what suggestions may you have to kind of people listening today? 

James Benham: It's predictive analytics is that as an interesting field of study, now keep in mind, we cannot predict the future.

Right? Right. There's no amount of software, hardware, even quantum computers that, that are getting really, really, really, really powerful cannot predict the future. If we can predict the future, then none of us will be in business anymore. We just predict the future. Right. We just bet on every sports game and they'd be all done.

So we cannot predict the future. We can assign a probability to an outcome now, and we can do it in a far better way. So predictive analytics for me is the process of using data and system. And machine learning and machine learning is when you teach a machine to learn rather than explicitly programming every potential outcome, traditional computer science, where you're building software is predefining the input, the processing and the output, nothing happens.

That's not explicitly programmed to happen, which means that all knowledge. Was contained in the originator of the software. All knowledge was in a person's head before they expressed it into a program in predictive analytics and machine learning and deep learning. We get into these topics of specific forms of AI.

We're talking. Training a machine, how to learn. So it can do things that it wasn't explicitly programmed to do. So it can learn from data. It can I analyze a good outcome and about outcome, which is insurance is pretty straightforward, right? Right. A claim claims bad outcome, right? You don't want to claim no claims is a good outcome.

And they then in a claim you can determine good and outcomes inside the. And then you can tie that back. And what you're looking for is correlation and then causality. Now remember correlation doesn't mean causality in star Trek. If a guy's wearing a red shirt, he's going to die right now. I don't know why, but gene Roddenberry, when he wrote starts putting it together, that's the way it is.

So at that point, that poor song go on, walks on the screen. He's got red shit, y'all he gonna die? Right. He's going to die. You're gonna die. Red shirt. Guy's gonna die. And so do red shirts cause fatalities. They're simply correlated, right? But predictive analytics is, is looking for causality and it's using machine learning.

It's using a machine learning algorithm. That's learning from the data analyzing good outcomes and bad outcomes, tying it to every possible data input, possible age, height, weight, comorbidity factors, all of these things. And it say, okay, there are 50 criteria. We were tracking. Here's the 500,000 claims and their outcomes.

Let's find the cause so that we can identify that early, right? And then better assess the risk and more quickly assess the risk. Yeah, there's two things, right? I want a better outcome. I also want to arrive at the conclusion faster. 

Ryan Eaton: James, I'm going to stop you right there if it's okay. You have such good information. I want to be able to ask some more of these questions so that maybe we can take into a part two for next month. So hold right there and we'll pick up on this episode next month. Thank you so much for joining the insurance leadership podcast today where a good plan today is better than a great plan months from now. Thank you so much.

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