Scouting for Growth
00:03:15 · Amrit Santhirasenan
[00:03:15] S1: what would I say? So, yeah, so, you know, I always make the same rubbish joke, which I'll make here is that I was one of the few idiots who graduated at the time of the .com bubble bursting in software engineering and thought it would be a bad idea to be a software engineer. So there's a small number of people in the world who studied software engineering and didn't become an engineer. I'm one of them. Yeah, graduated as a software engineer, decided to do something else. Just like many people, found myself in insurance. I'm a huge maths nerd as well as a technology nerd and I had some people I knew who said well why don't you consider being an actuary? You get to stretch your mathematical legs, you get to work in business and you get to solve interesting problems with people. So like many people, I fell into insurance that way. I thought I'd give being an actuary a go, found I loved it, worked at a company called Catlin, one of the legendary businesses that is now part of the AXA Group. And those people went on to found Convex, one of our early trusted flagship clients. So I worked in insurance, had a wonderful career for the first twelve years. As you said, I started my career there in the Lloyd's market. I ended up running the analytics team for Tokyo Marine Kiln. I built that from scratch. I was the first actuary who'd worked in fifty plus year history of the Kiln Syndicate. And then as Tokyo Marine who owned that parent company merged into a broader company merged a couple of its entities, I became in charge of that. It got to that point where I was tasked with building a kind of modern pricing platform. And I thought to myself, well, it's the twenty first century, world shifting to the cloud, open source technology is leading the way. You know, most people are using Python, R at the time, and other, you know, open source technologies to build analytical tools. There must be a framework, a platform I can use to solve my job as an actuary. And much to my chagrin, I found that there was nothing. There were some tough software solutions out there at the time. They're still around. They weren't fit for the needs that we had. And so a very classic founder story. I was very frustrated. I banged my hand on the table. I said someone's got to do something about it.
00:05:25 · Amrit Santhirasenan, Sabine VanderLinden
Who's who better to give it a go at least than a software engineer turned actuary who's worked in the market for a long time? And so that's how Hx was born. [00:05:35] S0: Beautiful story. And, you know, as you are speaking about some of the companies you've worked with, I have to say that, as I started my career as well at Lloyd's London, I was fortunate enough to have great mentors such as Bill Pironi or Paul Jardine at the time at Campaign. You know, we actually, a small world, but I've been fortunate to have amazing And so I want to hear from you, you know, when you think about our industry, which is insurance, you know, what does insurance brings you as a, you know, a founder of a company, a tech ventures, you know, what energy insurance bring you, partly in commercial lines, right? Because when I think about Hyperexponential, specialty lines, I get small and medium enterprises as well. Tell us about the energy this amazing market brings to you.
00:06:28 · Amrit Santhirasenan
[00:06:28] S1: Yeah, I love that question. That's a fantastic question I've never been asked before. I have an easy answer for this actually. I love insurance. I'm a huge insurance nerd. I think insurance is a societal pillar. Know, there are no rockets launched. There are no large buildings built. There are no planes that take off without insurance. And, you know, without the mechanisms by which we can absorb risk, we can't take it. And when we take risks, we reap the rewards of those risks and society moves on. So I'm a huge insurance nerd. I think it's important I tell everyone who joins Hx you need to learn to love insurance. Know, come for the fun opportunities, stay for the insurance, right, is what I would say. I think that insurance is one of these fascinating sectors that intersects technology, mathematics and human judgment in a way for people like me in a way that's very unusual. You have people who are tasked with working out how risky something is and you know if by definition if it's perfectly predictable, it's not risky. And so you have to analyse that risk. You do that in a variety of different ways. You use tools to do that, use techniques to do that. And I think it's a wonderful place for people who love solving hard problems in a variety of different ways. The actuaries and underwriters have to huddle together around the underwriting decision that's made is the essence of the problem that we're trying to solve. But I think this intersection of making human based judgments, combining it with interesting kind of mathematical analytical problems and using technology is like actually insurance is one of the best places to combine those things. And it's a heady cocktail for me. Again, I'm a huge nerd in this space, but I think it's an awesome place to spend your time.
00:08:09 · Sabine VanderLinden
[00:08:09] S0: You know, I really enjoyed my chat with Richard again recently. And we actually talked about for sure, Hyperexponential, but we decided to call the podcast, The Pricing Nerds, because he said, Sabine, we are nerds about insurance. We are nerds about pricing. I said, you know what? We found the title for this discussion. And so that, you know, it's doing very well over 3,000 downloads. But let's go to the decision intelligence. So decision intelligence is not a new concept you won't believe. You can see, you know, lines on my face, but I've been in the industry for quite a while. And I have a guest, I work with FICO and my job there was all about decision intelligence. And so I would love for you to decide... To actually define what decision intelligence is for Hyperexponential. And what does it mean when we actually talk about advanced pricing mechanism?
00:09:11 · Amrit Santhirasenan
[00:09:11] S1: Yeah. Yeah. It's a great question. You're absolutely right. Decision intelligence, the essence of what it is has been around for a really long time. I think it's really important to distinguish between a kind of decision intelligence as a mindset and a way of solving a problem and then the technology side. And I think probably the area where Hx has been very novel is in crafting a technology solution around actually the act of obtaining, if you will, or applying decision intelligence to the problems that you're solving. So that's probably where the novel aspect of Hx comes. But for us decision intelligence, you know, it's a straightforward concept which is where you go about the act of solving a pricing problem in such a way that you bring together the data, the insights and decisions together so that you're better equipped to make those decisions over time. Right? So every single time you do some work in the pricing as a pricing exercise or writing exercise where underwriters and actuaries work together to design and build the models and then to use them and to capture that data. Actually, just you set up a system such that the people who are doing that work automatically create a rich data asset that's coherent, that's evergreen, that's growing, and that the data asset can be fed back to make better decisions next time. And one of the things that I saw over time and, you know, people spend a huge amount of time and energy on technology. That's not new. That's happened for a really long time, but the technology didn't create this kind of virtuous feedback loop, right, of the data, the insights, the decisions over time growing. We would capture loads and loads of data. I remember this in the mid two thousands. I would watch as a software engineer us capture all this data and then throw it away, right, and never use it again. And it was hard, right? You know, one of the things about our space that people often underestimate, you know, I'm very fortunate to be the founder of a moderately to very successful enterprise technology estate company. But what that means is our clients have enterprise technology data landscapes. If you've a $100,000,000,000 balance sheet like some of our clients do, you've got data sets that are far and wide over multiple countries, territories.
00:11:17 · Amrit Santhirasenan
Some are still stored in green screen, like, underneath green screen systems. Some are in modern databases like Snowflake. Some are stored in PDFs and email systems. Getting this data together, operationalizing it so it can be used to make a decision, and then setting that process up so that actually that data gets get better over time is really hard. And that's where Hx came together... Came really onto the scene because we crafted the technology to actually take away all the boring act of kind of doing database manipulation or building algorithm containers or building web forms to let our clients focus on the problems that they actually cared about, you know, building the algorithms, using the data to make better decisions, surfacing that to the underwriters at the point of transaction. And that's where I think we really started to add value in a way that had never been seen before.