Scouting for Growth
00:08:10 · Sabine VanderLinden
[00:08:10] S0: That's super cool. I can't wait to maybe come to one of them in the future. So let's dive into, you know, advanced pricing decision intelligence. You know, what does that mean? You know, what are the crucial components we need to think about when we think about insurance and the insurance business, pricing, underwriting, actuaries, you know, how do they improve their day to day work in the world of artificial intelligence? And so how do you help them improve their decision making process?
00:08:43 · Richard Gunn
[00:08:43] S1: Yeah, good question. So there's lots of steps that go into kind of pricing and underwriting that we think about either how we do it or how we integrate with other providers, other parts of the workflow to achieve that all the way from initial submission, data triaging, predictive modeling through to kind of like the risk assessment and manual underwriting adjustments through to like quote and bind and so on and so forth. Probably the kind of cleanest way to illustrate how we're trying to move the industry or at least enable our clients to go from a to b is to kind of like give a couple of examples. And I would say, like, the main part of our thesis is that, like, historically, much of the insurance industry have looked at, like, risks on an incidental basis, particularly, like, five, ten years ago, or for many insurers still today, if they're pricing a risk in Excel, they'll kind of bring that information in. They might bring in some basic information to create a technical price, then they'll throw it away again, Or they might run some rate adequacy at a periodic basis and surface it up in one way or another, but they're not kind of really, like, capturing that data and an additional data to kind of make better decisions over time. Like, a good example might be if you're an underwriter and you get a submission from a broker, like, one underwriter just sees that submission. It kind of, like, leverages their brainpower to remember the last four or five and thinks about how they can approach the conversation with the broker most effectively. Whereas we would hope that an underwriter in our platform will also be exposed maybe information around the last five or 10 submissions from that broker and actually maybe what you priced them and like how they responded to that and maybe to show you some benchmarks from other brokers who are submitting your information for certain risks so that you can then leverage that in the conversation and your dialogue with with that broker. So it's about just kind of putting more information in the hands of, in that instance, the underwriter so they can hopefully definitely make a better informed decision, and hopefully over time that will aggregate towards them making better, more profitable decisions.
00:10:35 · Sabine VanderLinden
[00:10:35] S0: Yeah. You know, as you are talking about technical pricings, I remember when I was working with actually a company called SSV, those spreadsheet, those technical pricing spreadsheets that actually reviewed to actually see how we could improve things. And for me, it was very static. I said, you know, how can we just look at spreadsheet to make decisions? And I hope today in the time of artificial intelligence and platform, as human hx Renew, we can remove those spreadsheets and actually start doing much more dynamic pricing and being really able to customize those pricing for our customers. Am I right? Are we going there, Richard?
00:11:12 · Richard Gunn, Sabine VanderLinden
[00:11:12] S1: Yeah. I mean I mean, we have to. Right? If we think about, like, if you just take a step back, like, the landscape of risks are revolving. Like, what we are insuring now is materially different to what we insured in the past. So it's important, like, the pricing capabilities kind of keep up with that. Even if you look at, the FTSE one hundred or the Nasdaq, like, ten, twenty years ago, a lot of the assets that are baked into the company valuations there would have been, like, bricks and mortar. There'd have been, like, hotel chains and so on and so forth. And now we ensure an underwrite risk with relation to Airbnb, which is, like, almost all intangible assets. So, like, it's necessary that our pricing capabilities evolve with the risks that that that we're writing. And, like, cyber being, like, the poster child of this and particularly with, like, the recent challenges around CrowdStrike and so on and so forth, like, you need to ensure that you've got a pricing environment which allows your underwriters to kind of react to that kind of emerging data rather than, you know, historically maybe updating inflation adjustments on a, you know, six monthly basis and not much more. [00:12:09] S0: Yeah. And so when we look at what you guys are doing at Hyperexponential, tell us a bit more about big data and analytics and how you actually help provide that accuracy and help your customer drive profitability. At the end of the day, we all want to make more money, right?
00:12:29 · Richard Gunn
[00:12:29] S1: Yeah, we do. Whether you're in insurance or not. Correct. As CRO, broadly my responsibility is to at least manage one side of that P and L and consider the other side. I I I think probably one thing I'd say, and this might be less buzzy, but actually, we do less with the big data side of things, particularly in, the specialty commercial, more so, like, our clients are starting to use us in the SME space. So the full gambit of commercial P and C as there's there's some larger data sets there. But, like, actually, we often find that we're enabling our clients to work with, like, the small, sparse, fragmented data sets. And, like, how do you wrangle, like, insight from that data, and how do you use that to give you a competitive edge? And, like, really, like, the... One of the, like, core value props of our platform, if you will, but often it takes, like, a while for our customers to really, like, appreciate the value of this is no matter what our clients do on our platform, what pricing models they build, etcetera, what actions and activities the underwriters take will capture all of that data. So it accrues to an, like, evolving data asset, which could be coined, I guess, big data, which you can then start to evaluate and analyze and pull out trends. So I think that's just one distinction I would make is actually, like, central to our thesis is that, you know, it's gonna become table stakes that you're capturing, like, certain data points as you price. And if you don't have that, whether it's benchmarking or third party data that's available to most of your competitors, then you will like drop behind. But if that's like assumed, then it's like, where are you gonna drive your competitive edge, you can drive your competitive advantage? And that's where we're very focused on. Like, how can we make it as effortless as possible for different insurers to say, like, actually, we think this exposure method is going to be, like, interesting for us and enable us to, like, unlock some more value and more profitability in the market and just enable them to kind of bring that in and expose it to their to their underwriters.
00:14:19 · Sabine VanderLinden
[00:14:19] S0: So as I listened to you, Richard, there are two questions which come to mind. First, I just want to clarify, you know, do you do every type of insurance? Or you just mentioned cyber, SME. You know, let's clarify maybe the lines of business that Hyperexponential is focusing on. And the second question for me is, as you're actually looking at your customer base, you know, how do you, you know, who do you actually want to talk to in those organizations?