From days to minutes: rethinking facultative reinsurance

Matthew: Dom, Stephen, great to have you. You are respectively head of digital broking and head of analytics for one of the world’s largest reinsurance brokers, Marsh Re, formerly known as Guy Carpenter, or Guy Carpenter Facultative Reinsurance in your case. So welcome. 

Dom Sindall: Thank you, Matthew. It’s a pleasure to be on the show today. 

Stephen Sheehan: Yeah. Thank you, Matthew. Really excited to speak with you today. 

Matthew: Well, I’m also very excited to really properly understand facultative insurance or facultative reinsurance, I should say, because it’s something we all hear about. But actually, when we try to understand who the client is and what problem you’re solving for them, it is quite important to unpack that a little bit. So does one of you wanna have a crack at talking about, first of all, who are your clients, and then what is the problem you’re solving for them in the business? And then I’ll get us onto the solution you’re offering. 

Dom Sindall: I’m glad you’re fascinated. Our clients, being Marsh Re, are the insurance companies ultimately, and when we talk about facultative reinsurance, we’re talking about insurance of insurance. [01:00] So I’m gonna pretend for a minute you are a multimillionaire. Say you own 10 condominiums in North America. They’re worth $100 million in terms of its insured value. What do you do when you need to go and get that insured? You’re gonna go to your retail broker and ask them to sort coverage for you, and when that information and that submission, as we would call it, gets received by the retail broker, they’re gonna go into the insurance market and seek out coverage. 

Now, if I, just for the sake of this example, give you two possible outcomes. Now, as a retail broker, if you approach the insurance market and they do not have capacity or appetite to write condominiums more than, say, fifty million dollars, then they’re just gonna decline your business opportunity and not insure you. 

And that means the retail broker has to go on to somebody else, and they find another insurance company who could maybe do that. [02:00] Now, the alternative is that retail broker approaches the insurance company, and that insurance company goes, “Well, I know I can only retain fifty million,” but they then go into the facultative reinsurance market, and then they go and seek out the additional million. 

So to you, you’re just working with one insurance company via the retail broker. But in reality is you might be working with two or three different insurance or reinsurance companies to then support your business insurance. And when we think about the facultative reinsurance market or the insurance market more generally, you can segment that into large complex risk. 

You can segment that into more transactional, smaller risks. And, essentially, a hundred million dollar insured value, that’s a small risk or a medium-sized risk. The large ones are more than half a billion, going up to hundreds, potentially billions of dollars. Now, when a risk is large, time isn’t necessarily a problem. 

Time doesn’t matter so much. [03:00] 

There’s a lot of detailed analysis that goes on. There’s a lot of negotiation, and it could take seven days, it could take fourteen days, it could take longer to formulate a facultative reinsurance placement to support an insurance client of ours. Now, the smaller and the more transactional the risk, speed does matter. 

Price becomes less of a issue. There’s less time to negotiate, and days doesn’t necessarily work. It has to be hours, otherwise the insurance company has missed opportunities if they’re spending more than an hour or two on a particular submission, especially if it’s small. Now, the problem that we’re solving for here is speed. 

If you can make facultative reinsurance frictionless, and you can compress that time from days to hours, if not minutes, what opportunity does that open? And how does the facultative market, if it can achieve that, play a role in that? And what happens in the market if that type of financial vehicle becomes commonplace? 

[04:00] That’s the problem we’re solving for, and that’s what we’d like to talk to you about today. 

Matthew: Well, thank you, ’cause that was a very helpful explanation, and I wish someone had given me that explanation of facultative reinsurance twenty-five years ago. But just to restate it, because I think what is important about this is that your client’s client is the condominium holder, but your client is the insurance company. And then when you talk about speed and then the friction, so what we’re gonna be talking about is how to provide the speed with all the necessary analytics and things. But what’s causing that friction these days? Why is it taking longer for someone to be able to get a quote from the reinsurance market? 

Dom Sindall: Matthew, it’s an interesting question. We think about how placement operates in the insurance industry, in the reinsurance industry for individual risks. It ultimately involves people, involves brokers, and you’re communicating via email, telephone call, having in-person or virtual meetings, and your reliance on people being present and available is often causing placement to take days as opposed to hours. [05:00] 

Now, if you think about how long it actually takes people to work on a particular placement, if you are looking at the stakeholders involved, the actual time taken is relatively short, but it’s the total elapsed time that causes the main challenge. So when we think about what needs to happen in order to create a frictionless environment in the facultative space in particular, it’s like, well, inevitably you do need to take the human out of the loop to a degree here. Now, there needs to be controls, naturally, that need to be in place both on the broking side, on the underwriting side. As soon as you put people in the loop, you’re then creating risk of friction to then occur, whether that’s going from one hour to four hours or four hours to a couple of days. That’s primarily what’s causing this. Now, that’s nothing new, right? This is a market that’s existed for a long time, and it’s operated like that for a long time. 

I think what’s interesting about where we are at is how technology is changing and how that technology is actually entering into our industry [06:00] and driving innovation in ways where thinking about taking humans out the loop when it comes to placement and underwriting risk is now something that you and I, Matthew and Stephen, we would go, “Okay, that makes sense.” It’s discussed, it’s talked a lot. But you go back 10, 15 years, maybe longer, that wasn’t really a reality we could operate in, but it is today, and I think that’s the key. 

And when we talk about innovation, you’ll be surprised, this may surprise you, but go back to 2020. There were three digital trading partners in the facultative space. You go to 2026, so five, six years later, we’ve now got more than a dozen, and that’s a really powerful signal that’s happening into the market. And this is something that’s, for me, for Stephen, this is a really exciting time to be involved in the facultative reinsurance business because the market is changing.

Matthew: But Dom, that’s helpful. So both of you very well qualified to solve this problem. [07:00] Stephen, you’ve worked for 12 years with leading reinsurance brokers. Dom, you had 11 years in broking and actually four years working at a major startup scaleup. Dom, so coming to you first and drawing on your experience both in broking and seeing what it’s like to be working or building the company is actually coming in from the outside. What are the main lessons that you’re bringing to what you’ve done before and to what you’re doing now? 

Dom Sindall: One of the biggest things I’ve learned is in the context of innovation, which goes back to your question, innovation happens at the conflict between different topics. And when we think of insurance and break that into different topics, what’s really interesting and what’s happening and what I’m learning is if you bring data and you bring analytics and you bring technology, broking, and underwriting expertise all together along with the product mindset, which is what I’ve picked up in the scaling startup, you can do really interesting things. [08:00] And the attention to detail to challenge the status quo and think about how you can reinvent workflows and how, in this context, business could flow through the market, opens your eyes to new opportunities. And I’m quite open-minded in that sense. And if I was to say anything, like the insurance industry, some could argue, hasn’t changed in many, many years, but it is receptive to these conversations, and it is now with evidence being receptive to change. And it’s really interesting, and it really keeps me going and gets me up and motivated every day I go to work. 

Matthew: Stephen, so a similar question for you then in terms of what gets you motivated, gets you up going for work. In addition to your broking experience, you cut your teeth in the insurance world or related insurance world in a catastrophe modeling company. What you’re bringing from your past experience to what you’re doing today? 

Stephen Sheehan: Matthew. I could repeat what Dom said. We’ve been fortunate enough to work very closely with Dom from the start of my career, pretty much, so had very similar experiences over the last 13-plus years. [09:00] I cut my teeth very much as an event response intern and paying attention to what was happening in the globe from a natural catastrophe perspective and writing blurbs, and that was the transition for me from a natural science background into an industry background. And since then, I’ve been very fortunate that I’ve worked two of the largest reinsurance brokers in the marketplace, and I’ve been fortunate enough to be in a position where I’ve helped shape the data standards across both of these organizations. Because I’m within the broking team, I’ve seen firsthand how the data flows through the placement process and into the reinsurance marketplace and how it’s then used downstream for decision-making. [10:00] That intersection point between broking, analytics, technology is a key area that really motivates me. I very much enjoy waking up every day and tackling these problems. 

Matthew: Well, I’m delighted that you’re now seeing a revolution in the data world because it seems like it’s taken a long time to really get to the grip of these data issues, which in theory, someone looking at externally might go, “Well, you know the client. Why isn’t the data any good?” So are you now seeing a meaningful shift now in the ability to actually get the data you need from your clients and then get it out to the markets that are providing the reinsurance to them?  

Stephen Sheehan: When I first entered the industry 13 years ago, I was surprised how document-heavy the ecosystem was for insurance, and the last couple of years especially, we’ve seen a real acceleration in what AI can offer from an automation perspective. I’ll pick two predominant data types that we deal with. The first would be policy data. [11:00] So policy data traditionally has been provided in the form of a PDF document or maybe a Word document or similar. These documents have been the way data’s been exchanged from clients through brokers into the marketplace and then into the reinsurance marketplace and downstream, and they are ripe for automation, and there has been a significant amount of investment in this space. Because they generally have a well-formed structure behind them, you are then able to leverage the ability of AI and other machine learning solutions to extract that data in a standardized way, and then you can store it and use it for transmission further down the insurance value chain. So policy data, we’ve made real leaps and bounds in that space in the past few years. [12:00] Now, if we look at another form of data, so exposure data, your information about the properties themselves that you’re looking to get coverage for, and they’re typically stored in something known as a schedule of values or an SOV. Now, these are typically an Excel document, but they can be other formats as well. An SOV, they might have 30 tabs, some of them in different currencies, different languages, and you’re trying to combine that data together and again standardize it into a single form. Tech has grown substantially quickly where we’re now in a position where companies can look to automate that, now moving towards delivering an automated solution for exposure data in a similar way to how policy data has been tackled in the past. [13:00] 

Matthew: So Stephen, just to clarify what you said on there. So the way you’re solving this data issue is essentially using tools to make it faster and more efficient to get the information between the different parties. But you’re not talking here about getting it augmented information, are you? The core of what you’re talking about here is just making sure that data moves more smoothly. 

Stephen Sheehan: Correct. That’s right, Matthew. As brokers, we have to represent our client. When you get into areas like data enrichment, et cetera, that’s when you need to be careful that the risk isn’t misrepresented. And for us in Facultative, we are very much focused on the transaction, at least for the small and medium-sized risks. And so the flow of data and the transfer of data is the key aspect. [14:00] 

Dom Sindall: I’m conflicted on this subject a little bit. If you can compress the timeframe and you can get something done in minutes, there’s a commercial opportunity. But if you take three hours, there isn’t a commercial opportunity. What’s the right answer to that? Because it’s not perfection, but it needs to be good enough, and the question is: What’s good enough? And what’s good enough for the insurance company, what’s good enough for the broker, and what’s good enough for the reinsurance company in our context? And that’s the question we debate a lot because there’s a middle ground to be found here, and the opportunity is really interesting because it opens so many doors. 

Matthew: Thanks, Dom. Just before I come back on that point there, you mentioned TIV, which, of course, is total insured value for anybody who doesn’t speak insurance abbreviations. And this just gets us into the question about the technology you’re building or using. [15:00] The more clarity and visibility and transparency you give the users, your clients, their clients, about the assumptions that were made and where that trade-off is, then people don’t have complete information, but at least they’ve got insight into the level of information they’re using. So on that topic of technology, it’d be good to understand a little bit about how do you make that build versus buy decision, and how are you packaging this up as part of the application you’re offering? 

Stephen Sheehan: So if we’re thinking, let’s say, exposure data specifically, you need to think about three key areas for exposure data. That’s how you’re getting from the raw unstructured data into standardised data, and those three processes are ingestion, transform, and quality. Ingestion is absorbing the data from a document that is normally in an Excel file but can be other formats, and identifying the data block, so [16:00] understanding what parts of the file contain information and data that you want to capture. Then it’s understanding what that data is. There are many different ways you can write what the construction of a building is or how much property damage exposure is on an SOV, and sometimes they can be very clear, and you have clear columns that literally say, “Construction,” or say, “Property damage.” Other times, they can be more nuanced, or you may have multiple columns that all look to say something similar. So do you want all of that data, or do you want to make a decision at this point to take some of it but not take all of it? So that’s the ingestion process. Downstream applications typically require a much narrower breadth of information, so you have to structure that data in a standardized way. [17:00] It gets very difficult to start finding data standards that will work for everyone, because if you don’t have clear data standards, then what’s your tolerance going to be like in terms of data completeness and data accuracy? And even when you have your data standards, the next question is, well, okay, I know what I want to do for these scenarios where I’m given a paragraph for construction. I know how I want to interpret that kind of data. The next question is, how much of that do you want to be fully automated? Is there any edge cases or any scenarios where you will want a human to review? The problem we’re trying to solve is speed to market. So automate everything, you get speed. But what’s the trade-off look like? Are there false positives that can go through that process where [18:00] you’re leveraging AI or you’re leveraging some form of automation tech that’s making decisions based on data, but the data it’s making a decision off is not really proper qualitative data or quantitative data. So as part of this exercise, you want to be thinking about whether you want humans to interact with this process and at what points that is. So where are the validation gates in your automated process where you want a human in the loop? Do you want your data team, your underwriting team, your brokers, whoever you are, do you want them to be making a decision, or do you want a third party to make that decision for you? And depending on the organization and the team, you’ll have a range of answers there. So really, the prep work is the most [19:00] important bit because when you’ve done that, you can then start thinking, does this solution exist in the wider tech sphere, or is it something where we need to build something, or can we leverage a bit of tech here and we do a build in between so you get a joint solution that is a bit of both build and buy. 

Matthew: Stephen, Dom, we’re covering a lot here, but what haven’t I asked you about that we should also be talking about? 

Dom Sindall: The biggest question in my mind is, well, what’s changed? Why now? Why are we having this conversation? We’ve talked about frictionless FAC, we’ve talked about data transformation, and some of these topics have been around a while. Data transformation has been around a little bit. But what’s really interesting in facultative, the reinsurance market in particular, there is this recognition that the demand for FAC is artificially constrained by the way the market works. And if you do reduce the friction, inevitably you should be able to increase the transaction volume and grow the market ultimately. [20:00] So the mindset of people in senior positions within reinsurance companies, within brokers, they’re recognizing this. And the fact that technology is now quite pervasive, not just in our personal lives, but it’s a topic that comes up every day in our professional world, and this desire to want to get involved in digital trading or AI is like I’ve never seen. You go back 10 years, the types of conversations, it was quite inward focused, and today you’re talking about, “Well, how can we build an agent between one organization and another?” Or, “How can we create this frictionless experience around placement without humans involved?” And I would say, like going back to Stephen’s comment, is some companies going back 10 or so years, they invested a lot in data standards. They built the solid foundations to which when these other topics came along, we weren’t talking about AI in a meaningful sense 10 years ago, but we started to talk about it five years ago. [21:00] 

And if you already had that foundation, it was a relatively easy leap, then go to these next topics. And if you look at who’s providing digital trading capabilities, those first movers, they were the ones who were really involved in this early on and did invest in the data. And as a consequence, they also invested in and focused on changing organizational culture. And so there’s been a transition period that a lot of people have gone through, and it’s very easy for them to build. And if there’s actually a resistance to buy because they have this belief they can do it themselves. But if you’re the second cohort of reinsurer that’s seen what the first movers have done and the opportunity that they’ve managed to attract as a consequence of getting involved in that, to catch up, it’s gonna take a lot, especially if you’re starting from a zero base. And so the propensity with that second cohort is to partner because it gets them up and running relatively quickly. You can get up and running in six months if you wanted to. But if you’re starting from scratch, you can’t do that. 

Matthew: It’s a really fascinating topic, and it’s just great to see as [22:00] things come together and you actually can build a solution that actually works. Before I let you go, I do have to ask you for a recommendation each, either or both a podcast or a book. So Stephen, back to you. 

Stephen Sheehan: My book that I recommend is maybe not fully work related, but it’s definitely helped from a professional perspective, and that’s called Don’t Believe Everything You Think. It’s by Joseph Nguyen, and mental health has taken much more of a center stage in the professional world and personal world over the last decades. [23:00] And those of us who are in more of an analytical role, overthinking comes quite naturally and going into detail, and this book has been really useful for me because it teaches you how your brain, a thought and turns it into thinking, and it helps teach you the process that your brain goes through in order to get there and how destructive thoughts can happen. And with that understanding, you then can start recognizing that in yourself. It’s been extremely, extremely important from my perspective to help manage any thought processes. So anyone who struggles with overthinking this book is a great read. I massively recommend it. 

Matthew: Yeah. Well, thanks for sharing that, Stephen. I’ll be intrigued to take a look at that. Dom, what about you? 

Dom Sindall: So mine is Innovator’s Dilemma by Clayton Christensen and must read. If you are thinking about trying to challenge the status quo and the challenges of doing that in an established business context are very real. The hurdles that you have to overcome and think through very deeply are assessed and researched at such a length in that book, mainly from other industries. But you can draw parallels over [24:00] to the insurance industry around how you seed opportunities and value propositions and then how you allow them to flourish through time. 

Matthew: Yeah, well then classic, which I actually do need to read ’cause I haven’t read it. So that’s also one to add to my reading list. And then as we wrap up, couple quick ones. How should people get hold of you who wanna know more about what you’re offering, and what’s one thing you want to leave people remembering about our discussion. 

Dom Sindall: So Matthew, for me, yeah, just reach out to me on LinkedIn, Dom Sindall. I’m the only one on LinkedIn with that name, so it should be pretty easy to find. In the facultative reinsurance space, the first movers around digital trading have moved, and the second cohort of reinsurers are now emerging. So at Marsh Re, we’ve built a platform that connects to all of these digital trading platforms that the reinsurers have, and we deliver one access point to our clients, and that is about delivering frictionless back to our clients. [25:00] 

So we’re well on the journey. It’s been an incredible ride, but we’ve got a lot of work to do. If anybody wants to learn more about this going forward, please contact your Marsh Re representative, or if you have found me on LinkedIn, I’ll happily respond, and we can set up a call. 

Matthew: Great. Well, thanks to you both. Really enjoyed that, and I’ll let you both get back to making reinsurance frictionless. Thanks. 

Dom Sindall: Thanks, Matthew.