Predictive Analytics with Skip Brechtel From CCMSI

James BenhamOn episode two of the InsurTech Geek podcast, talking predictive analytics and all things innovation with Skip Brechtel, EVP and CIO of CCMSI. Insurtech geek podcast powered by Jimmy Knowledge is all about technology that's transforming and disrupting the insurance world. We'll be interviewing guests and doing deep dives with our own research and development team into technology that we see changing the industry. We're taking you on a journey through insurance tech, so enjoy the ride and geek out. Alright. Greetings, everybody. We're gonna have a great discussion this week.

Episode two. Episode two of the InsurTech Geek podcast. It's the early days, but we're going we're going in with the big hitters. I mean, I I pulled the I pulled the guy from the the batting lineup that really should be in that cleanup spot. He's the big hitter, mister Skip Brechtel,

Skip BrechtelEVP and CIO of CCMSI. Skip, how are you doing today? James, I am doing absolutely awesome. Great day, Friday. Getting ready to have a long weekend, so looking forward to our conversation today.

James BenhamAbsolutely. And it's it's gonna be a good one. We just saw each other. We've we've we've seen each other a good bit in the last couple months. You and I have been working together for years now, about eleven years, but but we got to see each other at the RIMS conference where we obviously met some amazing people, saw some great technology. We got to co present to a really neat forum talking about predictive analytics, and that's where we talked about having this discussion on the air so that everybody out there in podcast listener land could talk about and hear about what's going on at one of the leading TPAs in the country with predictive analytics and, of course, a lot more than that.

But before we we jump into that discussion about the the current and future tech that we're implementing, I love giving background on our shows list shows guests to our listeners so they can get to know you a little bit better. Now you've got a unique accent. I believe you call it Irish Channel.

Skip BrechtelYou're from the great city of New Orleans. Correct? That is correct. Grew up in the inner city of New Orleans. Attended a high school that was a block and a half from my house. Walked there daily, and then attended Loyola University of New Orleans. I was fortunate enough to get a a scholarship for sports to play d one college basketball when they let little short people play the game.

James BenhamExactly. So someone who was five foot ten would actually be allowed to play guard.

Skip BrechtelThat is correct. That is correct. So I even played, you know, a little forward every now and then. Sort of crazy. The game has changed dramatically,

James Benhamif you would. Yeah. But you absolutely loved sports and athletics. I know your family pretty well. They do as well. You had a a really fun time. Of course, the great thing is it pays for, you know, college and helps you get through a really phenomenal education. What what'd you study there in college? I

Skip Brechtelgot a degree in business administration with a degree in accounting. I had a minor in computer science, which was very interesting because it was all COBOL and Fortran then. So really embarked more on the accounting end of the career because the old computer systems were punch cards at the time, if you remember. Well, you would not remember. You're significantly younger.

James BenhamWell, I do remember. So I Fortran was one of my first programming languages. I started writing code in the early nineties, about '91. We learned Fortran '77 and Fortran '90. Did some Unix C. So we did some we did some of the foundational I call them foundational languages you really have to learn to understand where computer science has gone to now. I mean, it's a completely different program. Now when you look at computer science grads coming out of school now than what you and I went through, I as well got a degree in accounting and got a second degree in information systems.

So you and I share that. We had a passion for business and accounting, but also a passion for technology. And it's really played a big role. Your love for for business, accounting, technology has really played a steering role in your entire career, hasn't it? It has, James. I

Skip Brechtelstarted with Chevron, Standard Oil Cal, out of college. I was on an executive training program with them. Went out to the home office in San Francisco to help with the annual consolidation of all of the financial statements for for reporting. Everything was manual on on paper. This is 1976. So we had walls of spreadsheets from 50 different international companies. It was crazy how we used to do do things back then. Then, you know, technology, you you got into the early IBM systems, system 32 and some of these others.

God. But, again, a lot of that was to do invoicing, billing, not the degree of of technology that we can do now with in so many different areas. So

James BenhamYeah. It's really dramatic. It's it's almost hard to remember a time in business when we didn't have computers. My father, who you know well, built a large Teflon company he started in 1979. He sold it in 2004, and it wasn't until the last two years of a twenty five year business that he actually put a computer on his desk. Believe it or not, for 20 I I can't even wrap my brain around this. For twenty two years, he he built one of the largest producers of Teflon in the country without a computer.

And no email. Right? At an 800 number, used a fax machine. I mean, big innovation at the time, fax machine. He said they were able to cut order times down by days by using a fax machine. They were really early adopters on faxing because of how much and he he was he loved it, how much time it sped up the sales cycle. And so it's interesting and it's hard to remember a time when it when we did things in paper. What what attracted you to the insurance business?

Because certainly, you didn't study that in college.

Skip BrechtelNo. Actually, when I I left Chevron, I went into the oil field service industry. I was at asked me to go abroad, and I decided that wasn't my cup of tea. We had just had our first child. So I went with the oil field service company originally as their control and then eventually became vice president and general manager. And, basically, one one of my jobs was the procurement of insurance. So we had marine exposures, large trucking exposures, property. Cyber wasn't even a consideration back then. But, again, I started with the procurement, became good friends with the insurance brokers that I was with.

And lo and behold, fifteen years later, I wound up joining his firm as the the COO of his insurance broker, and he had a small TPA where I became president of a third party administrator, which I, at the time, had no understanding

James Benhamof what a TPA was, and that was in the early nineties, if you would. So that that's a big and if you you know, those of you out there in listener land who are in the insurance space know that's a major transition to go from buying insurance to running a TPA. For the uninitiated, what what are the major tasks of a third party administrator?

Skip BrechtelWell, again, in the workers' comp and property and casualty and, basically, we are approached by a number of different types of clients, large individual self insureds, Fortune 500 companies, insurance companies that we handle claims for, captives. So you you really deal with a broad spectrum of clients that each has specific needs, specific requirements, and we we try to make sure that we do a customized approach versus a a cookie cutter approach. A school fund in Michigan is is totally different than a a large waste management company, which has operations in 40 states throughout the country.

Which is totally different than an auto dealers group. Right? Correct. And

James Benhamreally, you're administering claims. So when people slip, trip, or fall or there's an accident or there's a major liability event, you're the first phone call. Right? I mean, you're the you're the ones on the front line receiving that report of injury or the report of the incident and grabbing hold of that claim and trying to get people healthy, get them back to work, get everything paid out. You know, it's it's you're you're really the front line of the insurance business in the claims side. Right?

Skip BrechtelThat's that's correct. And we we are actually a little bit different than a number of our competitors because we actually grew in the late seventies off of self insurance and group funds. So we we actually underwrite and do policy issuance on on over 200,000,000 in in premium where we actually take the submissions in from brokers, do underwriting, policy issuance, loss control. And then when there's a claim that happens, we handle the claims. That was where we started. That's where CCMSI basically are the the little sector that it it grew out of.

But now we're handling large deductible programs for clients

James Benhamwith many different coverages, etcetera. So it's very as I said, knowing it from the policy issuance and all the way through the claims end, it really takes us through the complete cycle. Yeah. And that's that that was really unique. Now now there's more TPAs that do that, but not not it was definitely not the time where you're really acting like a carrier without taking the risk on. You're you're you're doing all of the processes, whether it's, you know, know, binding the policy, underwriting the policy, working with the broker.

I mean, you're you're doing all of the steps. The the difference is you don't carry the paper. Right? That is absolutely correct. And and, you know, as technologies

Skip Brechteladvance, you can get significant enhancements in all of those areas,

James Benhamyou know, be it underwriting and policy issuance all the way through the claims act. How has the TPA business changed? Let's try and set technology aside, maybe from customer expectations, what they expect of a TPA. How has that changed from when you first took over in the early nineties running this TPA to when to to now, 2019? What what what has changed there? Well,

Skip BrechtelI would say in the old days, in the early nineties, we were looked at primarily as claims handlers. We would be the ones to handle claims for the clients, and their focus was to ensure that we did a great job of of doing that. As times have changed, and now I consider us a data driven company as much as a claims company. Clients are significantly more interested in their data, using their data to help drive better results for their programs. You're you're, at some level, a data aggregator

James Benhambecause of how many different clients you touch and the granularity of the data you have access to. I think that's evidenced by the number of entities like states and carriers who are now placing significantly higher demands on you to produce data to them. Right? Well, you

Skip Brechtelknow, not only the states, you're correct, You know? But we we're also all of the various carriers have different data and data requirements. So we have to make sure our applications and and data can can really handle the carrier specific needs as well as the state specific needs.

James BenhamYeah. And so you've seen what was I I've gotta ask this question. We're jump into predictive analytics in a second. What was the most interesting project or interesting things you were asked to administer in in just your your career in in the in the claims administration business, which which now spans, what, almost thirty years? That's yeah. Thirty years. Yeah. Almost thirty years. I mean, you've you are you're a pro. I mean, I I have learned more from you about the insurance business than arguably anybody else I've ever worked with because you you're such a phenomenal teacher.

But you you've had some weird stuff and some interesting stuff you've had to administrate. What what's some of the more interesting projects you had to work on? Well, let's see.

Skip BrechtelGoing back in time, you you know, we've we've had some really large governmental programs that were spread out through a complete state. And it was interesting just going across the state of Louisiana way, way back when, visiting the local parishes and and seeing the different needs, etcetera. And then Louisiana, you have four or five different dialects of of language within the same state. Yep. From way down South Louisiana, New Orleans got a has a different type. So, you know, just trying to go across the state and understand the different components of the program and how people thought about them was very, very unique.

It was one of the Yeah. Louisiana is a fascinating state. I mean, I was I was born and raised there,

James Benhamas were you. You stayed there. I went to I went to Texas, but, you know, leaving and going to Texas gave me even more perspective on the unique nature of just dealing with people. And in the TPA business, you deal with a lot of people, all the claimants. You have to be able to understand them. You have to be to communicate with them. It's a it's a big challenge. Did you ever get involved in in any hurricane claims? Well, actually, we the the the probably the other unique one was the not not hurricane claims, but the BP oil spill.

Skip BrechtelThere you go. We we the BP Allspill happened in the state of Louisiana hired us to actually be the data aggregator. There there was different companies that were were handling the claims, but the the state was trying to get their arms around all of the claims and all of the industries. So we actually worked with a a a large national firm to help build models for each industry for the loss of of business use and business income. So that was a really interesting project because we dealt with the shrimping industry.

We dealt with the the hotel motel industry building models to basically go back and help establish their claims for the loss of of business use generated

James Benhamfrom the from the oil spill. And that was a pretty expensive claim, wasn't it?

Skip BrechtelLet's put it like this. I think they're still paying out at this time almost ten years later. Oh my goodness. It was it was somewhere around

James Benham3,190,000 barrels of oil that spilled into The Gulf and ended up being huge did did technology play a role in your ability

Skip Brechtelto deal with the Deepwater Horizon oil spill? Well, it we we were one of the few people that could actually take data in from multiple sources, aggregate it together, and then, provide the state of Louisiana back with the information they needed by industry where they could assess what the losses were for each segment of the industry and each individual insured. Yeah. So so the answer is yes.

James BenhamData aggregation, the ability to pull the data together. Now let's let's kind of jump to today because you've you've entered this you've been a you started started out administering claims. Originally, TPAs administered claims on paper. Then we went to managing them digitally, then we went to digital workflow software, then you took then you went paperless. That was a big thing that y'all y'all did really early on just to dump the paper and start routing digital documents. Then you digitize the workflow. Where you're at now though to me seems like completely next level technology because you're not just processing data anymore.

You're actually using these decades of information you've been storing and capturing to start making much better decisions that are founded in fact, data, and history rather than gut feel. And so talk to me about predictive analytics and what it is to you and the role you think it's gonna play

Skip Brechtelgoing forward. Well, the insurance industry is sort of late to this game, quite honestly. Lots of other industries, automated cars, You know, we can go into so many different areas. But the insurance industry has had tons and tons of data that they collect from claims, etcetera. But to a large degree, we were not utilizing that data to the degree it could be used to help in providing better predictions on probability of certain things happening or or jumping on a claim early because it had the characteristics that that would indicate it was gonna be a high cost claim.

So so from from my perspective, back in 2010, we actually did a internal analysis on a on on a large national gaming company that we did that had thousands of of of claims a year. And we were just trying to determine the effect that comorbidity might be having on the ultimate cost of of claims. Needless to say, the machine learning that we have now, even in 2010, really wasn't existing to the level. So we went through file notes, etcetera, and determined that if a if a claimant had two or more comorbidities and all of the characteristics of the claim were were pretty similar, the cost of that claim was about 10 times more.

For the for the uninitiated out there, just what what are the common comorbidity factors? Well, we we're talking about obesity. We're talking about diabetes. We're talking about smoking. We're talking about cancer, heart issues, arthritis conditions, etcetera. There's a list of about eight or 10 of the major comorbidity factors that that go into the analysis.

James BenhamLogically speaking, you would think, well, yeah, that makes sense. Right? But you don't have data and evidence to prove exactly what type of impact a comorbidity factor can have on a claim. So that's what's been interesting for me as an outside observer to what you're doing and involved technically in implementation is that this when you start peeling back the history of the data in these claims, really it helps, What really helps is that you guys have done such a good job of of tracking data historically because you can say, hey.

Here's all the claims that had good outcomes. Here's the claims that had bad outcomes. Here's expensive claims. Here's inexpensive claims. And here's all the factors that led up to them. Right? You have to have that data so you can teach your model. And once your model learns what's good and bad and what's a good outcome and bad outcome and expensive and inexpensive, then it can start identifying what I like to say, Skip, and you've heard me say this before, causality versus correlation. Because just because someone happened to be obese doesn't necessarily mean that caused their claim to be expensive.

It might just be, you know, correlation, but it might not be causality. Causality isn't that really truly caused the outcome, and that to me is what you're really trying to drive towards is what, you know, here's the correlation, but here's the real causality. The

Skip Brechtelend result is what? That you can set reserves better, administer the claim better, get the person back to work faster? Well, yeah. And and actually be more right. What you're trying to do is identify problematic claims or potential problematic claims, high risk claims, high cost claims. You're trying to identify them early on so our adjusters, supervisors, and clients can jointly be more proactive in in in that claim to hopefully drive a better outcome for the injured claimant, which in turn should drive a better financial outcome from a claim cost perspective.

I mean, I I'll give you a perfect example. I you know? And this goes way back when we were doing the analysis. There was a restaurant worker that was obese, so about five foot eight, three hundred and fifty pounds. It was just a little slip and fall with a sprained ankle. Well, that that person, 32 years old, wound up having diabetes, having other comorbidity factors, and, ultimately, a a a minor slip and fall that resulted in a sprained ankle wound up being an amputation because of of complications from the the diabetes, which was not work related.

But, ultimately, that car that claim came in over $500,000. So, you you know, you can easily, in a situation like that, understand how factors not necessarily related to the the causation, but actually are impacted by the medical condition of the claimant has a dramatic impact on a claim. Yeah. And medicals,

James Benhamyou know, it's it's a big big can be a big bugaboo. Right? Because you Correct. Because the human body is so complicated. You're not sure when when an incident occurs exactly what outside factors that were preexisting conditions will impact the outcome of that claim. No no two claims are alike, and no two claimants are alike. So you have to constantly look at what all the criteria Has all this resulted in you guys collecting a lot more information?

Skip BrechtelWell, we we we did. So from that comorbidity study back in 2010 and 2013, we actually created a a tool called a claim risk assessment. And, again, remember, originally, this information was going in paper. Then, you you you know, you you started having file notes, but you did not have data fields where you could easily data mine. And you really weren't in an area at that time to really mine the data notes effectively. So so, basically, we developed a a a tool called claim risk assessment where we added about 70 new data fields, distance to work, pain threshold.

I'm just giving you a couple here. Probability that you think you will return to work. And we developed a an internal algorithm that would score claims. And, primarily, we were doing this with just indemnity claims and rating those claims as high, moderate, or low risk claims so our adjusters could again, this would normally be done during the recorded statement, which would happen in the first ten to fifteen days of a claim, which which basically would give us an early indication of some of the drivers of the claim that we thought would help our adjusters do a better job in proactive claims management.

James BenhamBut that was all human assumptions. You you did a study that was powered by humans, that was run by humans, and then at the end of the day, you produced formula that was baked into your software that was based off the results of this comorbidity study. The game kind of changes when you move to predictive analytics because now you're feeding this engine the data, and then it's now saying, hey, there's some stuff you never thought about that's driving your claim cost. Absolutely.

Skip BrechtelSo, again, I think we all in the technology space have been trying to keep up with the the rapid increase in the use of machine learning. Right? A lot of people call it artificial intelligence, but it's machine learning where, you you know, the the systems we utilize can learn so much from the data and be proactive in providing you analysis of that data. We will started looking at some other alternatives in 02/1617. In 2000 early two thousand eighteen, we we signed an agreement with a company called Gradient AI.

At the time, they were owned by Milliman. They had a 26,000,000 claim work comp dataset that we could tap into, provide our data, build models off of it, and really provide just some outstanding analysis both from the probability of future treatment that will be needed on the claim, the comorbidities that are impacting that claim, and the probability of this claim having a a high dollar threshold. So we we we started live with them actually in early two thousand nineteen where we

James Benhamall of our work is now coming into a product that we're extremely excited about. Has has it resulted in any surprises? Were were there some claims that it identified as really risky claims that under your traditional human analysis just would have been missed? Well, one of the things that I think surprises

Skip Brechtelus is is basically how quickly they start putting a or or signing a a mean incurred value thirty days into a claim. Now they do this both for medical only and indemnity claims because they feel like before assigning a value, it it will basically it needs thirty days of information to set that value. But what surprised us, and and we've looked at the analytics, is that as good as our adjusters are, it it really appears that the the the artificial intelligence generates during that at least from day 30 through maybe day one fifty with the adjuster catching up.

Normally, at about one one hundred and eighty days into a claim, the adjusters and the and the artificial intelligence are are more in sync with with the their their expectations on the ultimate cost of of the claim. But, again, as we talked about earlier, getting on problematic claims very early normally drives better outcomes. So if the machine of the software can assist our adjusters in identifying these problematic claims earlier,

James Benhamwe feel we'll definitely get better outcomes and better financial results. Yeah. It's really amazing to me when you look at claim volumes and how hard it is to stay on top of all the data in insurance. Right? Just the number of data fields, number of claims you have to manage. And you guys really do a great job of keeping claim counts per adjuster in line. Even with that, all the work you do to to keep an appropriate claim count and give your adjusters time to manage a claim, it is so difficult to catch everything.

And that's to me one of the biggest things here is that you you finally have some robotic assistance, not just processing the data and presenting a screen you can click on, but saying, hey, you might wanna check this out because I think that it's gonna have some problems. And that's really where this comes into play for me is that you've got this area of machine learning which is a subset of AI. It's a specific form of AI that is really gonna deliver, I think, leaps and bounds of volume.

The other area that really fascinated me, Skip, is all the plain text images and videos. So we call that, you know, unstructured data, data that wasn't in fields that you can now tap into using a predictive analytics engine. You can tap into years of these diary entries and start to peel useful information out of them. James, look. I

Skip BrechtelI couldn't agree with you more. I mean, you you know, we push every data field in our in our system, every file note because now the technology can read the file notes, pick out keywords, phrases, everything because the adjusters put a ton of information in there. We push every medical bill, every PBM bill. You you know? But where the future is going, quite honestly, we're already, you you know, discussing the the possibility using voice, know, in the analysis as well where when you do the recorded statement or even even at the initial call in of a one eight hundred report of injury, it's amazing the technology can now pick out key factors of of from a a person's voice that might be providing false statements that that we all know has an impact in in the cost of a claim.

Sure. Yeah. If they're lying to you, you might have a problem.

James BenhamAnd and it's it is interesting. So truth analysis, also another subset of machine learning and AI where you're actually trying to detect intent tone. If you ever watched the TV series Lie to Me, it was all about using machines to identify facial features and voice features when someone's lying to you. So certainly, you are trying to detect that, but also just read using a machine to read through two decades of diary entries and two decades of notes that really were impossible for a human to sit and read through and say, hey.

We've got some commonality here. We've got we've got a real issue. Whenever whenever an adjuster starts talking about this on a claim, we got a problem. That to me is probably one of the most powerful areas I've seen you utilize here. Have you seen a lot of people in the insurance space doing this? Because or or do you do you consider this a pretty early adoption? I can tell you in our discussions with Gradient,

Skip Brechtelwe are the first TPA that I'm aware that is doing this. I think a lot of our our competitors and the industry in general is trying to get their arms around this. I I think they are all in agreement that machine learning and AI can assist, but we have not seen much from our competition that's actually delivering the kind of information that we're gonna be delivering here. Do you think all of this is going to lead

James Benhamto the ability to start taking simpler claims and auto adjudicating them? You're able to just say, hey. Look. We've got a whole bunch of claims that are pretty simple that we can just automatically pay out. Well, I I let let's put it like this. Right now, we are looking at this to be

Skip Brechtelan additional tool for our adjusters and our supervisors to basically utilize to see if there's a major variance between the estimate that the the the AI is predicting versus what our adjusters have. But let's be real about this. As the machine gets more information and, basically, the analytics and the AI will continue getting better, in the future, I do see there there could be the potential for especially in the medical only space doing a medical only claim almost on the auto adjudication basis.

James BenhamWe're we're not there yet, but it could it it will happen. Yeah. It will it will happen. We we're we're already seeing it in the residential property business. We're seeing it in personal lines auto where there's a certain cast class and category of claims that just get automatically paid out now. They they don't even go through again, we and I'm very careful on all of my talks and all of my podcasts that I do. We're not talking about human replacement. We're talking about human augmentation here.

We we're talking about giving people the ability to do higher thinking tasks. We're not talking about replacing a sea of adjusters. We're talking about taking those same adjusters and making them way more effective. And we're talking about taking those same adjusters and allowing them to potentially manage, you know, the really high volume nasty claims and spend a lot of time on those to manage them, those things that need human interaction, and offloading the menial work onto machines that can handle that for them so they can really focus their time and efforts on getting people back to work faster and taking care of the customer.

And so I just wanna make it really clear for all of our listeners. We're not talking about human replacement. We're talking about human augmentation. Giving giving a I would

Skip Brechtelwould agree, and and I would I would state, you know, in the old days, James, the carriers did so much training of adjusters, etcetera. The carriers are not handling as many claims as they did. A lot of them are starting to outsource claims, And and the adjuster pool is is it's very difficult to to find quality experience adjusters out there. So I don't see this as a replacement, like, as you said, but it will definitely assist organizations to maintain quality adjusters,

James Benhamyou know, from a staffing perspective. Yeah. It's a it's a it's it's gonna be awesome. I I think it's gonna be awesome for the quality of life of some of these workers that you you just have a you have millions of workers in the insurance business that are kind of bogged down with with tasks that can be automated so they can really free their day up to think, you know, and to really use their God given talents to to really reduce the cost of risk and add value to the client.

And so it's a it's an exciting future for me. Now this can't be the only tech you're excited about. I know that you you have a lot of different technology that really gets you geeked out. So what else out there that that you've been seeing over the last few years do you think is a is a game changer? Well, I'm gonna wrap up the artificial intelligence for the adjusters with the next phase,

Skip Brechtelwhich is actually recommending interventions back to the adjuster. Like, is this an appropriate time to to do a IME? Is this the appropriate time to get a field case manager or TCM? And what is the financial benefit of doing that? That that is coming as well. So not just doing predictions, but just recommending treatment interventions or or claim handling interventions back to the adjuster, that that's that's coming shortly after probably within the next six months or so. A couple of the other areas, that so many clients now, are capturing extremely large video surveillance.

It's the storage of information that, you know, we're we're talking about utilizing data. Well, we've gotta store the data and having the ability to store, you know, all these large video files and and and so the cloud has been a phenomenal new source for basically housing a lot of this data at a much less expensive cost that will allow both the the the client and the the TPA to retrieve that information extremely quickly at a at a low cost.

James BenhamAwesome stuff. Let let's talk about the the future of insurance, cause we're starting to see technology companies that are starting to carry risk. Instead of just the technology company being a vendor to the insurance business, you're starting to see the technology company take over risk operations and actually carry risk. And do you think that this is a harbinger of some significant change

Skip Brechtelin the fundamental makeup of the insurance business? Well, I see new companies, new carriers entering the space. A lot a lot of them seem to be very, very data driven. They're they're using the data not only for claims, but actually to really do a large portion of the underwriting to basically identify the what they feel is the best and most profitable risk. So, yes, I see the industry changing a good bit, and and that's why

James Benhamwe are are now, as I started out early, saying, we're data driven companies as much as anything else. For me, it's it's feasible to see a world in which you have a a series of companies who are simply incapable of making the leap to being data driven organizations that carry or manage risk, and you have a series of companies that are started as fundamentally started as data and technology organizations that happen to carry or manage risk as one of their functions. And I I think you're gonna see I I'm gonna say that you're I I think we're gonna see some pretty significant change in the makeup of the market.

I think that's why you see a lot of the really big insurance carriers have started technology venture funds, and they're starting to invest in the very companies that are gonna disrupt their own business models because they're they're almost acknowledging it'll be challenging for them to change it from within. And so they're investing in the very companies that are doing it so they can at least have an equity ownership and, of course, one day merge those companies into their operations. You're seeing really, really big players in the insurance space become Insurtech venture players.

And I I've I've seen venture funds from some of the largest guys that we see at REMS every year are putting together Insurtech venture funds. And they're saying, hey. If we can't drive the innovation internally fast enough, maybe we can invest in the next people that will so we can still play a relevant role going forward in this really data driven process. The other area I'm seeing, like, I'm a drone pilot, you know, I love flying. I love flying planes. I love flying drones. And Verifly is one of those really interesting providers that just sells you insurance by the minute.

So you're starting to see this really interesting fractionalized market where I can log in on my drone app and buy sixty minutes of coverage in a specific geography to a specific altitude for a specific purpose. And it and you think I I would I would not and, you know, when I got involved in insurance almost twenty years ago, I would not have imagined a world in which I could buy sixty minutes of insurance. But you can now. You can, yeah, you can buy it yourself. It binds immediately.

It issues the policy. It uses your GPS on your phone to know where you are. It knows what kind of drone you have because it's connected to your drone. I mean, it's it's incredible how intelligent this is. And if you go through some of the modern underwriting processes for residential and renters insurance, they're pulling on 20 different public data sources. You're not answering any questions hardly on underwriting cause they're answering all of the questions on your house for you. And so it to me, Skip, it it it seems like a world in which guys like you are gonna operate really well because you're used to running a data driven organization, but some just won't be able to make the leap.

Well, you

Skip Brechtelknow, again, we we we have always embraced technology to help us be better as a company and as an organization. We're we're all about our people, but if we can make our people better and perform better by giving them the tools to to do their job and technology is is definitely extremely important in our space to do that, then we need to do it. I mean, telehealth is is coming to the comp space. Right? So so just think about this. We were talking about, you know, again, you've got video or you can have video coming back and and communication coming back from from the the claimant and the telehealth provider.

Again, just think about the ability to drop that into a a artificial intelligence tool, a system in analyzing that claimant's attitude, that claimant's desire to return to work, etcetera. Again, it's just gonna continue by leaps and bounds because of the enhanced capabilities of technology.

James BenhamYeah. There was a interesting article today, Skip, about Google Duplex. Google Duplex is Google's voice tool that allows their machine to to call restaurants and salons and set appointments for people. And I'm a big believer in the consumerization of of IT where that consumer tech creeps into the into business tech and really starts in consumer land and then it moves into businesses. And so this particular chatbot is a voice chatbot that can call people and talk to them, even has little nuances of human speech like uh-huh and mhmm.

And they've made it so realistic that there's many people that say on the receiving end they can't tell if it's a person or a machine. And there there's an article that recently came out that Google was using about using humans for about 25% of those calls. So if if a call kinda went sideways and the machine couldn't deal with it, it would bring humans in and to to finish the call out. But about 75% of these calls are able to be handled entirely by the machine.

And and so a lot of those same type of technologies will be used exactly like what you just mentioned. That is giving you the ability to analyze what they're saying and how they're saying it and provide recommendations and trigger key phrases. I mean, it's gonna be an interesting next ten years in the insurance space, and I think you'd agree the pace of change is certainly accelerating over what we saw from 1990 to 2000. Well, it

Skip Brechtelis rapidly changing. I think almost all organizations are are are trying to figure out how they can use the technology to get better. Some are doing it better than others, and and those would be the companies that take the lead in the industry.

James BenhamAnd let's let's let's wrap up our final final comments on benchmarking because it it was in the past, you really didn't have a great way of benchmarking how you're doing in the insurance business. You have a you have the the NAIC that has the the $0, first dollar data, but not not a really great dataset on large deductible self insured retention. Right? And so benchmarking has really become a hot topic for you so you can identify how your customers are doing versus the data universe, not just how they did because what you see a lot of times, Skip, is customers and and you're looking at how you did compared to yourself the previous year because that's the easiest comparison to make.

Right? You already have all the data

Skip Brechtelin Benchmarking internally tends is is easy. Right? I mean, you can see what my results were this year versus last year, what was my cost per claim Yeah. You know, etcetera. But, you know, that's great benchmarking yourself, but how are you benchmarking against your your your peers? Are you performing as well as or better than your peers? It's critical to try and get that data, right, and get data you're talking about self insured. You you know, NCCI is is what I call more of a guaranteed cost smaller client.

But but large deductible clients and large self insured retentions, those clients tend to be much more sophisticated. They're taking a large portion of their risk themselves. So ensuring that their programs are driving the results and keeping them as competitive or even more competitive than their than than the people they compete with is is critical. So I think benchmarking, it's great to have the AI to help you drive results, and and you know you're getting better results internally. But, again, other people are are pushing to get better as well.

So how are are you staying as competitive, or are you beating your peers?

James BenhamYeah. And, again, when you benchmark against yourself, like, sometimes you can give yourself remember the the most improved awards. Right? Right. We we had that in the I was in the corps cadets at A and M, and we we had the we had an award for the outfit that was the most improved outfit, and no one called it that award. Everyone in the corps called it the you sucked last year award. And and to me, that's what benchmarking against yourself is like, what if you just sucked last year?

Like like, what if you were just really, really bad and now you're just mostly bad? And and that's concerning, and that's what self benchmarking can drive is this irrational belief not rooted in reality that you're doing well compared to everyone, not just compared to yourself. And so, you know, we worked on a pretty pretty awesome project called Compmark that allowed for TPAs to benchmark themselves against the data universe. And now that project and and there's, you know, a little too early to announce what that project's becoming, but that project's gonna become something else, something independent for people to build a benchmark claim performance against a data universe without worrying about their information going public.

It's a it's a confidential secure system. And so what that's what's exciting to me, and and this was you were one of the brainchildren behind this, where it actually gave you the ability to say, hey. Among hospitals in Illinois, how are we doing? Are we doing well or not? And it gives you a a truly objective scorecard.

Skip BrechtelWell, and if you're a hospital in the Chicagoland area versus a hospital in in the the southern part of the state, you're you're that that's two different universe that's two different universes. That's just like claims in in California are significantly more expensive than claims in Indiana. Different laws, different statutes, different regulations. So when you're benchmarking just to say, well, how did I compare across the country? You really need to look at it on a state by state basis because each jurisdiction is is so different. Right?

For someone that might have operations in, you know, certain areas of the Midwest, their their claim results, just because of the of of the area they're operating in, is gonna be less than California or less than New York, etcetera. So you've you've gotta benchmark back to your jurisdiction as well. Very, very critical. Yeah. And what's really interesting, Skip, is that the process

James Benhamof benchmarking actually teaches you about local and data nuances that impact the cost of claims and the cost of risk. Just being able to see, hey. Show me, you know, average cost per claim by state by this, you know, industry code. It actually allows you to become a much better adjuster, much better risk professional because you start to understand not just a gut feel that things are more expensive in California because, let's be honest, everything's more expensive in California. The true data backing to prove that, yeah, claims actually cost more there.

Really once I started to be able to peel through these years of benchmarking data, it taught me a lot about what really drives the cost of risk and how important data and benchmarking are. I I couldn't concur more. Well, Skip, you're a you're a, in my opinion, a true visionary in the insurance and technology space. Appreciate the time you took to talk with us today. Any any closing comments or remarks?

Skip BrechtelNo, man. Just you know, we just need to keep jointly geeking out and and trying to figure out better ways to drive better results. And I'm one of the old guys in the industry that still is having a great time trying to come up with new ideas and thoughts to help our our company and our clients get better results. It's always challenging, but but very, very fun.

James BenhamYeah. And if you don't know Skip, that that little number called his age may be going up a little bit, but he's still he's still a healthy 21 on the inside. And don't don't ever forget it. He has more energy than almost anybody I know. And and absolutely has a passion for innovation technology. But, you know, the the biggest thing that you have a passion for that is worth noting, Skip, is your passion for people and investing in people. You've invested in me heavily, and I appreciate it.

You've invested in a lot of other people who who owe a lot to you. So we we all appreciate it, and thanks for your time today on the podcast. What a great time it was, and everyone have a great day. Take care, everyone. That was our interview with Skip Brechtel, the CIO and EVP at CCMSI, talking about predictive analytics, data, and risk. This has been the InsurTech Geek podcast powered by JBKnowledge. It's all about technology that's transforming and disrupting the insurance world. I've been your host, James Benham.

Thank you for joining us this week. Look forward to talking with you soon. We're taking you on a journey through insurance tech. So enjoy the ride and geek out. See you next time.