Scouting for Growth
00:29:22 · Richard Gunn, Sabine VanderLinden
[00:29:22] S1: You will indeed, with all of our bells and whistles. So we're very excited to go to that. It'll be the first time that we're going to ITC Vegas. And yeah, we'll be there. I think we'll be speaking in a couple of slots and there to meet people and discuss and understand, you know, about what's working for them, what's not working for them and see how we can partner. [00:29:43] S0: Yeah. I think it's very important to understand that your style is first to really understand the problem. You want to be able to meet as many US located insurers, chief underwriting officers, actuaries, pricing experts, to really understand what is the challenge they are facing in the local market. As you know, when we think about emerging risk, a number of insurers have come out of specific risk, as you know, because of climate change and emerging risk that they are unable to really understand. And so being able to have a conversation with you around, you know, how do we use all these data? Maybe external data sources we don't know about and actually start building pricing, which are right pricing for the risk we are actually prepared to undertake is going to be critical. And I assume you're happy to engage in those conversations.
00:30:28 · Richard Gunn, Sabine VanderLinden
[00:30:28] S1: Very, very happy to. I'd add to that list as well. We're increasingly having conversations with team members on the IT side of the business, with our CIO, CDO, CTO, and everything, everyone below. And that's, you know, motivated by the macro trend around Gen AI and everyone being asked the question or asking themselves the question like, how are you leveraging Gen AI? And then kind of the question that kind of that peels peels away is like, do you have the data to be able to leverage it? And and that's where we will often come in and have a conversation. Like, as I mentioned earlier on the on this recording, like, a a key part of our platform is capturing and structuring that data. So it kind of positions our clients very positively to be... Then be able to leverage this emerging capability, and we're we're increasingly having conversations on the IT side of business to understand how we might better help them there. [00:31:20] S0: Is it fair that you are actually exploring or leveraging Jira TVI, right?
00:31:24 · Richard Gunn
[00:31:24] S1: Yeah, a lot inside of our business, and we announced at HxLive, so I think I can share on this as well, like, what our strategy is and what we're putting in the hands of our clients. One thing, we didn't rush to put an AI badge on our brand. We rush to use it internally. Like, the framework that that... Or kind of like the the way that someone framed it to me was like, need to... You you and your organization need to have... Work out how to leverage AI better and faster than your customers and your competitors. Otherwise, you will lose. Right? And I think that's like a really powerful pertinent statement that everyone should internalize. And so we rush to kind of to leverage and understand where it can add the most value both to us in our day to day. So a lot of my go to market team will leverage AI on a daily basis, often without realizing it and often intentionally with some of the kind of internal kind of co pilots, if you will, that we've built out. But from a client perspective as well, like thinking about how our consulting team could leverage AI within Hx Renew, and then we can share some of those learnings. So the first question that we asked ourselves was, like, why are we uniquely positioned to be able to enable AI in our platform rather than, like, how can we enable AI in our platform? It's like, why are we uniquely positioned? And, like, broadly for us, what that resolves to is, like, we... Our actuaries will log into an IDE on our platform, a development environment. And so you have, like, full the development and deployment environment at all of your models and all your data and the interface all in one place. And then we aggregate that data asset that sits underneath it. So all of the activities that the actuaries and the underwriters do on our platform, we then aggregate that into a data asset. And then we seamlessly integrate that into your wider tech stack. So that gives you, like, the foundations to really build and, like, make the most of kind of the emerging capabilities in Gen AI. Right? And so in terms of, like, what that's manifested in terms of our strategy, it's like kind of really, like, threefold. So, like, first of all is actually to build out some libraries, like AI libraries in our platform. We have the idea of libraries in our platform.
00:33:23 · Richard Gunn
So if you need to build out certain capability and use it across seven, eight, nine, 10 lines of business, you can do so and just call it in as a library. So to build it out some, like, common libraries that, like, our clients will benefit from. And, like, theoretically, they could build them themselves. Right? They could integrate it with their chosen LLM, maybe have an agent in there following some specific commands and kind of implement it themselves by our integration with our platform, but we've seen that like there are certain areas, whether it's on the ingestion side or other areas of the workflow that we can just support them with some kind of prefabricated, pre milled built libraries. So we're releasing them to our clients at the moment. The other two areas we're looking to kind of leverage AI, one is as an actuarial analyst, so a virtual actuarial analyst, and then one is kind of a virtual underwriting assistant. And on the actuarial analyst side, this is about kind of... Probably the easiest way to resolve it to is think about like an actuarial copilot who has, like, an infinite knowledge of all of the coding for your organization and your specific environment, and so can pull, like, best practice libraries. You can say, hey, I'm currently writing a marine model. Can you pull this type of exposure modelling from my cyber model? And really help you both drive consistency, speed, but also like importantly, allow you to kind of improve your risk assessment and build more sophisticated models over time. So, like, that's just making the actuary better at their job by giving them that assistant. And likewise, the underwriting side, the underwriting side will be more around how does that copilot, if you will, look into your data asset and just, like, help them become more informed more quickly in a way that they would likely want to anyway. So can they ask a question of the underwriter assistant like, hey, how does this risk compare to other risks I've had from this broker? Like, how does this map to my how does this map to my portfolio strategy? Like, how much have we written in this given kind of vertical or risk sector at this point?
00:35:14 · Sabine VanderLinden, Richard Gunn
[00:35:14] S0: Yeah. Fascinating because what I hear as well is data is critical to do that very, very well. And that's what you are saying. Data, data, data, aggregating it and creating the right taxonomy to actually leverage it regardless as to whether it's a Jira API or AI. But then the power of this technology is then to enable the human with more automation, augmentation to help them make the job the job better, but then focus on more complex task, I'm hearing as well. [00:35:45] S1: Yeah. There'll be areas where our clients, like, just double down on algorithmic underwriting. But I would say in the whole, it's about, like, augmenting and supporting the people that you have in the organization to do their jobs more quickly and then spend more time in the areas where they think they can drive differential value. Right? Like, they're kind of 80% of tasks that every insurer will be doing. Do that as quickly and consistently as possible and then think about, okay, right, how are we going to, like, gain additional insight at the edge of of of the data landscape, and how is that gonna make us hopefully make a a better decision than than our competitor? [00:36:19] S0: Yeah. So I I want to ask you the inspirational question now, the future trend question, actually, Richard. So for you, where does pricing decision intelligence going to go? You know, what are some of the future trend we should be thinking about?
00:36:19 · Sabine VanderLinden, Richard Gunn
[00:36:19] S0: Yeah. So I I want to ask you the inspirational question now, the future trend question, actually, Richard. So for you, where does pricing decision intelligence going to go? You know, what are some of the future trend we should be thinking about? [00:36:37] S1: Yeah. Good question. So you've got, like, the macro part of that, and I won't I won't kinda, like, retread where where where a lot of the market is talking about being ready for a softening market. And, you know, I think also, like, tangential to that is also, like, being ready to react to an evolving market irrespective of whether it's softening or hardening. Right? Like, I think CrowdStrike was great It's [00:36:59] S0: changing all the time. I mean, you know, the market crashed just two days ago. Right? If you had stuck in AI, you probably wanted to get out of it a few a few days before. But, hey, that's what happens. It's uncertainty. Right?