Scouting for Growth
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?
00:19:08 · Amrit Santhirasenan
[00:19:08] S2: Yeah. Yeah. It's a great question. And, you know, it's been so interesting because actually, ironically, the last twenty years, the focus has been on what's been our bread and butter, right, the pricing and underwriting models and algorithms. Actually, what we haven't been able to do is do the very quick prioritization, the triage, right, the analysis of the fundamentals of a risk to work out whether you can say yes or no, or maybe quickly. I think one of the things that agentic capabilities have brought is the ability to do this, right? It's the ability to dramatically increase the quality of service, because for a very large amount of risks, you know quickly, right, whether you're in or out. Then the ones in the middle are the ones that takes the time and effort, right? The ones where you're not sure. I think we're seeing, know, we have a triage offering, right, that's being added to our suite of capabilities. And what we've seen really clearly is that using AI now, AI has unlocked particularly even, you know, before it was agentic. Right? But the generative AI models have unlocked the ability to pull data so quickly out of the information required for underwriting that you can put a very quick red, amber, green status on these risks. You know, again, several orders of magnitude faster than ever before. You know, I think when I talk to our clients, absolutely, they are obsessed about profitability, particularly in today's market. And that's, I think, a differentiating factor of the next generation technology providers will be not just focused on the administrative side, but the actual optimisation of profit, which is something we think about very deeply. But they also care about service. So I think what we're seeing right now, and your example is a very pertinent one, is that the technology hasn't been able around to actually provide that service feedback loop as quickly as we can see. And, you know, it's amazing, isn't it? You were saying two weeks and now a day, you know, I think we've got even in the specialty space, we'll get even faster, right? Think it'll be hours soon. Yeah, right. Mean, cyber
00:21:03 · Sabine VanderLinden
[00:21:03] S1: and all those new risks that we are seeing, right? Know, SME, cyber, I think the technology is there. And so now it's about the underwriter feeling comfortable to have enough data to actually win the risk. They are comfortable to win faster. And I think the term faster is a critical one right now. So another piece of research I was reading while getting ready for our conversation today, Amrit, is the multi agent ecosystem. So McKinsey has put some research out describing multi agent systems as those systems being able to handle intake, risk profile, pricing, and compliance together. Can you describe how the Pypex Financial or Hx sees these agents working together and where the human underwriter comes into play?