Transcript
Hi Mike Matchett with Small World Big Data and we are here talking, of course, about AI. That's our topic every day on everything we're doing. But real interesting. Today we've got a deep dive and a focus on the insurance industry and how they're actually using AI not just to play around, but to operationalize it and govern it at scale. So it's consistent. It's helping the insurance carriers make profits and roll out new products. It's helping the end clients get a consistent coverage. It's helping make sure everything is going according to the regulations that cover it. And it's it gets deep. So hang on a second. We'll be right back with earnings. Hey, Barry. Welcome. Today. Uh, maybe you could just tell us a little bit about, uh, how you got involved in insurance. Why? You know, because some people think, oh, insurance. That's not the not the place where I would get involved technology, but how you got excited about insurance and then how you got involved in earnings and why, you know, like the, the, the, the idea of the deep analytics behind that is, is so interesting. Yeah. Hey, Mike. Yeah, sure. Thank you. Great to be here. Um, yeah. So I do not come from an insurance background. I am a software engineer. That's where I started my career. So very much in the engineering side of the world. Uh, very early on in my career, I realized that my passion is actually taking that technology, which I know or knew very well, and applying it to solving real world business problems and in particular for large enterprises. So that can kind of be my theme in my career across a variety of roles. Um, and then I came across earnings. I had dealt with carriers before, but never at a, uh, they provided that focuses only on insurance. And I very quickly realized that beyond the technology, it's, uh, there's a deep science in how insurance works. What are the constraints that it has to operate in? What are their consumers looking for? And it starts with the fact that if you put on your consumer hat for a moment, you very quickly understand that you actually don't want to deal with the carrier until you have a problem, right? When things go wrong, that is when you evaluate if you have the right carrier or the right insurance provider. Um, and that is very interesting because the carrier has to prepare themselves all the time for that moment of truth with the right technology. Um, and this is how I got here. All right. So you're looking at these competing forces and that eventual, uh, clash of, you know, I have this need, you have to fulfill it. And you promised me this, and I need to execute on that, uh, coming together. And this is, this is when we think about insurance companies, we're talking scales that, you know, obviously tens of thousands of policies, but, uh, the dollar amounts can be staggeringly larger than that. The scope can be Geographically worldwide on their the regulation landscape horrifically complex. Uh, so how do we, how do we, how does a carrier, how do we as thinking if we're the carriers get a grasp on that and say, I got an issue policies in that environment and do it consistently and I don't know what's what's the word with some efficiency or speed. Yeah. So it's definitely the carriers as they are looking to, uh, build their business, uh, you know, have customers come back to them again and again and again. They have to continuously balance between the risk they're willing to take, um, the customer experience that they provide, which is very important in our world. Um, and the process that allows them to be ready for that moment of truth that I mentioned before, right? So they invest a lot in this process. And in order to be able to come up with the right answer when you ask for a price, what is that premium policy going to cost me? They want to be very accurate. They want to do it very fast, and they want to be very fair and in compliance. So they have to find that right balance between the two. Uh, and they use a variety of technologies to make sure that happens over time. Yeah. And I would think just naively and I know better now, but I would think naively that a core competency of an insurance carrier is the actuarially setting of prices appropriately, and that they all know how to do this and do this with sufficient speed and scale and regulatory oversight. But that's not really the case. What do insurance companies really run against when when they go and look at that problem? And you know, what does earnings come along and say, here's here's how we offer a better solution for that? Yeah. So first, I, I, I do think that the actuaries are a very, very strategic, uh, persona at a carrier. At the end of the day, they impact both the top and the bottom line of the carrier. So very important. Um, the key, the trick here is how do you take the work of these people and make sure it reaches the market at the point of decision, okay? Because these are people that are doing very sophisticated things, using very sophisticated technologies. But if their work stays outside, like in a silo and it's hard to move it to where the decisions are actually made, and frankly, the job isn't worth it. So this is where earnings come into this is what role we play in this market. We help them do their jobs better. Um, and then we take the output of their work, which is in the form of what are the right prices and what is the underwrite or the decision they want to put into the market. And it will help the carrier take it through the right governance process all the way to the moment of decision, which is as consumers, we feel it. You know, on the other side, we get a decision. This is going to be our pricing proposal. Right? Right. And I know that the the core earnings value propositions are things you've been working on for many years, and this is something. But we got to talk about AI. So how does how does AI come along? Is, is this confusing the carriers and how they might apply AI? And, uh, if, if, if that's the right place or are you helping them apply AI to their problems? What's, what's, how should we think about the story here from, from that perspective? So, you know, insurance has been around for many years, hundreds of years, actually, if you're interested, I would recommend someone look it up on Wikipedia. Uh, hundreds of years ago was the first, uh, carriers or first insurance providers. And over these years, uh, carriers have not just because of regulation, but because of their responsibility to society. They have developed certain ways of doing things. Um, uh, and those things sometimes could be considered a constraint or a way, something that slows them down. And in some cases it is, but the reality of it. As consumers, we really want them to go through that process. So it should be actually it's very good for us. Uh, now AI comes into play, and in a world where so many things are changing on a continuous basis, a decision a carrier makes today might not be relevant in two weeks. Right. And carriers have to make faster and smarter decisions. Right. So I see I think most carriers, despite the concerns around AI and we can talk about that, of course. Uh, I think they see AI as an opportunity, uh, a way to deliver better service, a way to do more business with, uh, with their customers. Uh, and their challenge is not how do I use AI? Or can I create some AI in some use case somewhere in my, in my operations is how do they do so in a way that is purpose fit for a regulated environment? How to put the right guardrails? How to operationalize AI, a problem they've been dealing with with other technologies in the past. They now need to apply the same concepts again. And you must have to have some deep. I think you mentioned this to me earlier, vertical focus and vertical experience in order to help someone really deploy this wild West of AI in the set of use cases in that governed and orchestrated and regulated environment, that seems like a pretty big challenge. Yeah. We, uh, we very much categorize ourselves as a vertical AI player. Uh, we are experts in bringing AI to the insurance market, which has its own unique characteristics, its own needs, uh, and applying general purpose AI just wouldn't be good enough. It wouldn't deliver the right accuracy levels. It wouldn't be governed in the way it should be. Uh, it won't help the personas that are in the process today. So that expertise that we bring of the insurance market and how insurance operates is, uh, is, is part of the value proposition that we bring to the market. Uh, I'll give an example. Um, one of the big topics around AI is how do you provide the right explainability to the decision that was made? Uh, so it's, how do you do that in the world of AI is a very interesting discussion. Uh, what is the right moment to do so? So those are examples of expertise that we bring to the market. Yeah. I think the takeaway for me being more of a generalist in it is that there are companies like Onyx that can provide a vertical specialization to deploy, as you keep saying, operationalize AI in a governed and scalable and efficient and even agile way, uh, that we're not going to get just by looking at the next open source package coming out of GitHub, right? We are going to have to evolve the entire landscape of markets and vertical markets, uh, for players like Onyx to come along and say, we can solve this problem here, right? So that's correct. And again, as an example, we are not here to invent new AI techniques, right? What we do really well, we have a whole team that is responsible to continuously scout the technology landscape, landscape, understand what capabilities are out there, general purpose, and then making them purpose fit for insurance. So what do we need to do to how do we leverage them in the right way? Maybe we need to enhance them. Maybe we just need to put them in the right place or connect them to the right workflow. So we have whole teams that that's what they're doing day in and day out. All right. I, you know, I think there's a lot I could sit here and ask you about that dives deeper into how insurance works and how what you guys do as a SaaS service to provide this governed and orchestrated environment for carriers that if you're an insurance industry, you should definitely take a look at. Uh, but I, you know, I think at this point, um, we should just have you tell us what your recommendation is. So like, so your key takeaway would be if you're looking at deploying AI in a large scale out regulated environment, and then where do we find some information on earnings if we want to follow up with with you. Yeah, sure. I, um, I, I'm recommending to anyone in the insurance industry and frankly, in other industries as well, uh, you know, we have this AI buzz everywhere. Some would even say a bit hyped, right? Because you can't, you keep hearing about it. And sometimes it goes back to basics, right? If you want to lead a transformation in your organization, it goes back to the same patterns of transformation. How do you enable the people? How do you put the right processes? How do you, uh, and how do you have the right people that can help evangelize it? And then you apply the pattern of scale. How do you do things in a repetitive way. So if you're thinking just, I'm going to use AI to solve this use case. It's nice. It's not going to scale and deliver material value to your organization. You have to think scale. You have to think how you do it across your entire carrier. And you need the right tools and the right capabilities to, to enable that. So that's kind of my recommendation and definitely something we apply internally also as we internally adopt more AI capabilities. Um, if you want to learn more about our links on our website, it's always a great place to start. You will find some very interesting videos from some of our recent, uh, customer events. So you can learn all about both the technology, but also about the people that we have. We're very proud, uh, with our experts and how we engage with our customers. Uh, definitely think that would be a great place to start your engagement with our next. All right. Thank you so much, Barry. I, you've got a couple other things we could talk about in the future. Uh, your AOS and, uh, we could talk about, You know, that there's no choice but to adopt AI enabling technologies if you're trying to do this at scale. So, you know, AI is not a a flash in the pan, even though there's a hype that's bubble, it's probably going to burst. There's definitely some advantages that are always going to stick around. Uh, but we gotta go. Uh, so I just want to thank you again for coming here today and explaining this to us. Thank you very much. Thank you. Mike. My pleasure. All right. And if you are in the insurance industry, you definitely got to check out earnings. If you are even in adjacent industry or another regulated vertical, it's probably still worth checking out to see what they're doing and look for similar solutions in your vertical. Uh, and if you are just interested in AI in general, you still may want to check this out because this is one of the ways that AI is going to come back out of this hype cycle and be applicable for a long time in the future and become more effective and efficient. So take care. Love.
