Scouting for Growth
00:10:12 · Amrit Santhirasenan
[00:10:12] S2: Yeah. Yeah. I mean, you know, this is my work, and so I spent a lot of my time thinking really about what this looks like. And I think, you know, insurance is just one of these incredible domains where I was talking to someone, I was doing a job interview this morning with someone, I was explaining to them that you're not going to see that many places with a $7,000,000,000,000 contribution to GDP with such a small number of companies responsible for this and such a small number of people in the grand scheme of things. Don't get me wrong. Some of our clients are just, you know, the size of cities, but, you know, it's still a very small number of people compared to many other sectors in the world. And so for me, the frontier is to think a little bit about like this very specialised in the grand scheme of things skill set, right? The specialised art and science of actuarial and underwriting and the IT work in insurance. How can we contribute to that? What does it mean for us? Does it mean... And you know, for Hx we will have the world's leading AI research team in insurance next year. Right? What is it to like encapsulate the skills of an actuary, the subset of an LLM, these 170,000,000,000 parameter models, like what subset of those represents an actuarial or underwriting skill set? How can we fine tune and optimize these models to do really, really, really great tasks in underwriting and actuarial. They probably don't need the complete works of Shakespeare. Right? Some of the stuff that's embedded inside some of these models. So what does that like? What does it mean? So for me, sorry to give you the focused answers. It's really looking at like what the applications of the leading technology that we see today, how those can be focused and really refined for the very actually quite unique, quite like specialized skills inside insurance. And that's what motivates me. I'm a technology nerd as well as an actuary at heart. So thinking a little bit about how we can tie those things together and do some of that work for our clients. Super exciting stuff actually.
00:12:03 · Sabine VanderLinden
[00:12:03] S1: It's leading edge. Some people said bleeding edge. And actually when you start looking at our industry, our sector, and you've said, small amount of company being accountable for so much GDP. We are moving into a world where we have specialty exposure. The conversation I'm having every day with the Fortune 500 I work with is around emerging risk. From cyber to marine, to current societal changes actually, Amrit, to looking at changing faster with historical losses, historical data. How can underwriters stay ahead of these emerging risks that are affecting us leveraging AI?
00:12:48 · Amrit Santhirasenan
[00:12:48] S2: Yeah. Yeah. So when you asked me this question, gave me nostalgia because I remember the very first presentation I did, the launch presentation of Hx seven years, nearly eight years ago now, talking about how the fact that the frontier of the world's risks, our market, the specialty market, is the place where Apple, Tesco, John Lewis, right, where the leading providers in retail technology, They come to buy their insurance. Right? And these companies are changing so fast. They've been changing for decades. And actually, us as a market, we haven't had the ability to keep up. And one of Hx's core missions from the very beginning was to use technology to synthesize better data. Right? To be able to actually keep up with the pace of change because our clients were getting more sophisticated in the way they were thinking about risk. Ironically, we were unable to do so. So Hx's motivations, you know, it feels so quiet now to talk about having an API or, you know, machine vision algorithm. To be able to pull some third party data sets. I remember we demonstrated the first API machine vision algorithm in the market in 2017. Now that just feels like table stakes. Kids who come out of university are doing that for toy projects before they get get to to our clients. But now deep risk research. Right? Why can't you have deep risk research on every single risk? Why can't you say, tell me the most important characteristics you can find in the world about the top three exposures I'm doing. No human could do this work. Right? It was just the cost benefit for trade off. Right? The frontier in different centers of the world. The cost benefit trade off there isn't right. It wasn't economic. But actually, you can you can run have an OpenAI deep risk API call to go and do that on every single risk you underwrite today. And if your FHX is underwriting agent, sorry, founder doing his bit of sales, right, we do it for you. Right? This is what we do. And so I think it's all of a sudden making it dramatically easier to bring that level of differentiation and specialism in the way underwriting has always been done at the kind of the very best... From the... In the best underwriting to every single risk you want to touch because you can have an army of digital agents helping you now. Yeah. It's just incredible, really. All for $20 a month. It is incredible.
00:15:03 · Amrit Santhirasenan, Sabine VanderLinden
How much Hx costs? [00:15:05] S1: I know and you know what you just said is I think we need to unpack that because I, you know, do a lot of research, R and D for leading organization And I currently have to do a piece of research where we have to read a lot of reports, not only annual reports, but also looking deep into all the research which has happened since the first of January of this year. So we found something like 100 reports. But because there's deep research, I mean, imagine once upon a time, which was before 2022 or 2023, I had to read all this stuff, right? I had to read these thousands of pages. But with deep research, now I can be asking questions, but then unpacking the information in different ways, leveraging my knowledge, my expertise, my experience in the industry to answer the question. Like you would actually say, the same things would happen with an underwriter. So what gaps do traditional models miss out that you are able to solve today, Amrit?
00:16:10 · Amrit Santhirasenan
[00:16:10] S2: Yeah. You know, we're always looking to close those gaps because I think if you look at the models in the world, been built over the last twenty years, they just haven't changed very much. Right? You know, notwithstanding the fantastic advancements in the calibration rate making benchmarking space of actuarial work, The GLM side of things and obviously more advanced machine learning models now. GLMs have been wonderful for twenty years, but they're not state of the art anymore. You know, there's so much more that can be done now in the analytical world. And so for me, I think when I think about this question, I think it's like actually less important about the model is what information can we surface? What information can we surface to the people making the decisions that they just don't have right now? Your example is such a good one, right? Because, you know, you've all you will have always known that that data is out there, but it just wasn't practical for you to read 100 reports. And the same thing applies for every single risk that... Pretty much every single risk that an underwriter looks at today. Right? There's a world of third party data. For the biggest ones, if you're insuring the international group or you're insuring Zencouran, the biggest risks in the world. Yeah, you know what, it's extremely worthwhile for the actuaries and underwriting assistants and the underwriters to go and do as much research as they can to get that information. But you can't do that on every single risk, right? You can't do that. Whereas... So now I think, know, to answer the question about the gaps that the models have. It's not necessarily even about the models. It's about like, what's the information that you put into them? I think our models actually, the for the most part, you know, it's not too difficult to design a model, you know, that actually is very capable, but for the data that it doesn't have. And plugging that gap is just just a key part of what we do. You know, we talk about data, we talk about insights, you know, so the mixture of facts, the mixture of judgments and then the decisions that come out of that. Just the amount of data and insights that you can put into a model before you make decisions. So it should be a hundredfold.
00:18:06 · Sabine VanderLinden
[00:18:06] S1: It should be a hundredfold. And the quality of data is actually critical. But one thing which is happening, and as you are speaking, I mean, thinking about my own experience as a business owner, right? I go to loads to get my business insurance as we do in the world we are in. And, you know, once upon a time, I remember it would take maybe two weeks to get a quote back. And today, you know, in the summer, my broker reach out and within, I would say twenty four hours, in two hours, it's Sabine here, it's a new quote. Are you happy with it? And it is done, right? So fast quote to buying cycles are critical for us to differentiate. Probably when you have good risk, the one you are seeing here. What competitive, I would say strategies, how does agentic AI start helping carrier meet the needs of their brokers so that they can continuously differentiate and actually grow as well alongside the carriers AI survey?