Making Risk Flow | The Future of Insurance
00:00:06 · Juan de Castro, Simon Cooper-Williams
[00:00:06] S0: Hello. My name is Juan de Castro, and you're listening to Making Risk Flow. Every episode, I sit down with my industry leading guests to demystify digital risk flows, share practical knowledge and help you use them to unlock scalability in commercial insurance. Welcome to another episode of Making Risk Flow. And in previous episodes, we've talked a lot about underwriting. And today I wanted to do an episode which is much more focused on the actuarial function, Talking about what does transformation mean for them and what's going on on that side of the business, which is often overlooked. Who better to have this chat with two of the actuaries I admire the most in the industry. Although, let me first start by disclosing, I feel a bit uncomfortable being outnumbered by actuaries of all, but I think I'll do my best to moderate this discussion. So today I'm joined by Simon Cooper-Williams. So he's the Director of Portfolio Development Solutions at Markel International, and by Jamie Wilson, who is the Head of Pricing and Innovation at Hyperexponential. So first of all, you so much for joining me today. [00:01:23] S1: Thanks for having So [00:01:26] S0: let's start with a brief overview of your background and your current roles, and perhaps starting with you,
00:01:32 · Simon Cooper-Williams
[00:01:32] S1: Yeah, sure. And thanks for having me again. So yeah, I mean, my background was sort of a traditional actuarial upbringing. I started in one of the big four in life insurance, moved across to general insurance quite quickly. Saw I a lot of the actuaries doing spreadsheets over there, and we had pre built models and we got to parameterize them, but we weren't actually making anything ourselves. So I thought, oh, that looks a bit more fun. Let's move to GI. So majority of my time was spent in general insurance there. I've then been in Markel now for just over nine years. I started in a traditional actuarial role, so that was split between reserving and pricing. I think the majority of my time there, I think the things I really enjoyed the most as part of that role was starting to make tools for underwriters. I found myself moving outside of the traditional core of developing models or doing that reserving. And I found myself making tools and seeing if I could just help underwriters in the decision making a little bit outside of the pricing. And then after that, about four years ago, I left that and set up a new team. And that's where I currently am in Markel now. So around four years ago, just before COVID, I went in and completely redid the underwriting management function, which didn't exist as a function in underwriting before that. So before that, it tended to be a single person focusing on peer review, maybe a bit more around technical underwriting. And really, the objective I was given was turn it into a data driven function. Not really with the vision at the time, where we just went in and started doing some stuff and saying, Okay, what data do people have? What are underwriters using? Is there any chance for improvement anywhere that we don't currently have in there? If we fast forward now, I have a team of data people. So we do data analytics and insights. We actually store all our own data. We push out dashboards. We push out tools as part of that function. But we also have a pricing and a portfolio management function. So we had a lot of success in that data function. And off the back of that, we started portfolio management.
00:03:32 · Simon Cooper-Williams, Juan de Castro
And these guys are actuaries within the underwriting division, and they're doing segmentation, slicing and dicing of data, doing sort of non traditional analysis and doing that for the underwriters in the division, not from a regulatory standpoint or from sort of a case pricing standpoint. And then we've also got a pricing function where we've ring fenced and we're solely doing pricing model development. So that as a standalone function, we think it needs that sort of attention across the underwriting division. I think previously, with it being part of a wider function of a traditional pricing function, it really didn't get the attention that we thought it needed. So we took that out and put it right in the front end because we think pricing is absolutely one of the critical part of the underwriting process. So that's where we are now. We've gone from a team of me in 2019, We'll be 12 people. We're 12 people now, and we'll be around 16 people by the end of next year. [00:04:28] S0: Well, that's quite an impressive background. And you've touched on many, many topics that I would like to deep dive in just a second around moving from actuarial being a silo to being really embedded within the underwriting function. We'll touch on that in just a second. But before that, Jamie, do you wanna do the same and provide an overview of your role and background?
00:04:50 · Jamie Wilson
[00:04:50] S2: Yeah, sure thing. So yeah, I'm a, I guess a pricing actuary through and through a bit like Simon was describing. So I kind of started my career in consultancy after a few years of kind of perfecting my PowerPoint art. I moved in house and was a pricing actuary in the London market, kind of moved across various lines of business, kind of always working quite closely with underwriters. Spent a few years in Chicago in The US heading up a pricing team over there where I started to move a little bit more towards kind of like data driven predictive analytics, machine learning, data science type kind of like work. And then after a few years of being in Chicago, I actually then came back to The UK to head up a effectively establish a kind of a global predictive analytics team within the pricing function. So kind of it was building out a team of data engineers, data scientists, and pricing actuaries who kind of effectively became data scientists to try and really start pushing for like more data driven pricing within the company I was at at the time. And kind of we got involved in all sorts of things. So we kind of, we were there predominantly for predictive analytics, but we got involved in all of the discussions around data and technology. And we were trying to push things out and like come up with like really innovative solutions. And it was a lot of fun, but it was just, it was quite an interesting blend. It was a very, very different role to I think a classic pricing actuarial or a classic actuarial role. And then at the beginning of this year, I actually then moved from being in house in an insurance company to joining Hyperexponential. So kind of a technology company. And here I effectively partner with our customers and try and help them to get the most out of the platforms. So getting them to think about, okay, well, if I'm moving pricing models onto a kind of a really innovative technology platform, how do I get the most out of that? So like trying to get actuaries to think more around technology and transformation. And then there's also an innovation arm that comes with that as well of like, how can our customers innovate on the platform as well? So that's a little bit about my background. Very similar to Simon, very much starting in pricing and then slowly starting to meander away from classical roles, I'd say.
00:07:06 · Juan de Castro
[00:07:06] S0: And that is one of the reasons why I wanted to bring both of you into this episode. I think listening to your introductions, you talk a lot about insights, about predictive analytics, right? Which is, are things that probably often are not top of mind when people think about an actuary. Well, probably wrongly so, but reality. So again, we'll deep dive into that into a second. But perhaps following up with you, Jamie, let's start with what is an actuary? Like what is the role of an actuary in an insurance company? And I'm sure many people are familiar with it, but other listeners might not be that familiar.
00:07:44 · Jamie Wilson
[00:07:44] S2: Yeah. I'm never too offended when someone doesn't know what an actuary is. I feel like it's quite a niche job. But yeah, I guess one of the ways I'd describe it, so kind of maybe a little bit of history of actuaries really. So kind of like the actuarial profession really started in kind of life insurance and pensions. So kind of it was effectively this statistical study of when are you most likely to die? And like, how can I then like use that analysis to figure out what money do I have to hold in order to pay out for life insurance and things like that? So it kind of very much started in that space. And it was very much embedded on this idea of statistical analysis, but kind of starting in mortality. And then I think kind of over time, there was this realisation that actually, well, within general insurance, there's a similar need for kind of like statistical analysis to try and figure out, okay, I'm gonna have to pay out claims over a number of years. So therefore I need to know how much money I need to hold back for that. And so kind of you started to get the movement of actuaries into kind of the insurance world, but very much in that kind of reserving space. So very much in like a regulatory, how much money do I need to hold in order to pay out claims? And so kind of much more like that third line of second, third line of defense of, okay, well, once I've written the business, how much money do I need to hold? And then I think kind of what's happened over the last twenty years is that kind of the actuarial profession has kind of become more and more embedded in the kind of the insurance industry and moved away from just reserving. And now you kind of hear about capital modelling actuaries, reserving actuaries, then and crucially kind of the latest breed, let's call it, of pricing actuaries who are actually there, who are meant to be kind of like helping underwriters to figure out how they should be pricing business. And that obviously looks very different. A pricing actuary looks very different in retail insurance to kind of SME insurance to kind of large commercial specialty insurance. But kind of always, I think kind of coming from that background of like trying what you would hope that they're kind trying to apply statistical analysis to figure out some kind of outcome for the business.