Managing risk with data-driven insurance solutions

Interviewer: Joining me here in the studio we have Chris Weller and Richard Archer to discuss exposure management and how leveraging software solutions can come within that. Chris, welcome to you. Give us a bit of an intro into the company. What's the ethos there?

Chris: Yeah, absolutely! Thanks for having us here. Inigo was formed in 2021 with data really being at the core of decision-making and trying to differentiate between us and what others are currently doing. And data really is a key driver on that and how we deliver that sort of performance through our client insights.

Interviewer: And Rich similarly, welcome to you. Where does Insurwave come into all of this?

Rich: Yeah, I mean thanks for having us as well. I mean I think in Insurwave again data is definitely the hot topic. I think we, Insurwave, started about four or five years ago with a very much a focus about bringing a single source of truth of data together to enable a corporate risk manager to have a better conversation with their broker and their insurance markets that serve them. And putting data at the core of that enabled them to kind of have a different way in which they started to transfer risks into the market.

Interviewer: So you mentioned data a fair bit but of course with the plethora of data that there is, how do you ensure that you have the confidence in it and that it's accurate to what you need?

Rich: Yeah, it's a great question. I think from our perspective, the data is only as good as the data you've got. And so it starts really at the point of entry. For us, the importance of getting the data coming in at the front end into our platform is a really important step in the process. So leveraging AI and a number of other sorts of tools that we have. It's very much about how you assess the data, identify gaps in it. We often find that the contracted data that's being provided; there might be gaps in the data, there are incorrect geocoded locations, there is some missing information about certain aircraft characteristics for example. So part of what we have to be able to do is leverage AI to identify those gaps, alert them to us so we can enrich the data all in a sort of seamless way before we put it into the platform. So ultimately what we know is then on the platform - what you're tracking and making decisions on is accurate.

Chris: I think one of the things around confidence from an insurer's standpoint is really it comes down to sort of looking at our pricing metrics, ensuring that those risks are being priced appropriately. But then the proof is also when something happens and being able to identify what you've got in that area, whether that be a hurricane or otherwise. So yeah, a very simple sort of concept around exposure management is what is it, where is it and what's it worth? And the confidence comes from being able to answer those three questions. If an exposure management team can't answer those three questions, then that's something that needs to be improved.

Rich: Interestingly, we were at a conference a couple of weeks ago and we polled the audience on what percentage of the assets that you currently insure within your portfolio do you have a view on? Do you know where they are? And I think the average answer was between 40 and 60% in terms of actually what they knew genuinely within their portfolios that they can manage. Again, the confidence of having that data and answering those questions that you just raised is kind of an increasingly important part of the exposure agenda.

Interviewer: And how long have you been working with Inigo? What's the experience been like so far?

Rich: Yeah, we've been working together for just over 12 months. Initially we started kind of formalising a view of how we could look at using data more broadly in the exposure management world about 18 months ago. And we kind of looked at exploring a starting area and we kind of identified aviation war as a hot topic especially coming out of the back of the Ukraine incident. And so we looked at how we could use data to better inform some of the exposure management decisions that were being made in the aviation space.

Interviewer: Without making it sound too obvious, the big issue now is exposure management?

Chris: It's definitely one of them and I think one of the one of the big challenges is around assets that move and historically that's not always been something that's easy to track and monitor. So it's been a bit of a gap in most insurers skill sets really and that's where the relationship with Insurwave has come about.

Interviewer: And in terms of monitoring, how does that work and can new tech really help in that?

Chris: Yeah, absolutely. It's key to actually the solution that we're looking to deploy. So aviation just like a car has a registration number and that can be plugged into a system such as the Insurwave platform and that links to global tracking of the fleet. So much like very popular airline tracking websites out there. But the big difference between what they offer and what we get with the relationship with Insurwave is we're able to enter in our insurance terms and our liabilities into that system. So that when something happens or we were looking for a certain aircraft in a certain area that we can actually see what the impact is to us as an insurer as opposed to knowing that there is an aircraft that's just landed at London Heathrow for example.

Interviewer: And Rich, from your side, how seamless is the tech?

Rich: Yeah, I mean I think we spent a lot of time working on you know Insurwave has been let's say around for a few years. But we traditionally started in the marine space. So we've already been used to tracking vessels, moving around the world, working with a number of our key clients. And so really as part of that it was about understanding how do you track the relevant unique ID of an asset, of a ship or a plane. But then, importantly, being able to link that to the insurance terms that are in place. So you know the value of the asset, you know the characteristics of that asset, but what are the covers, the terms associated with the insurance that go with that asset? So when you bring those things together in a kind of extensible data model, which is what we've built, you can then really start to understand the value and the impact of a potential incident arising. And being able to see that all surfaced through a map that you can visualise in real time is a very powerful method for the insurers to view their risks in a different way.

Interviewer: Starting with marine and then making the journey to where you are now. I guess there's a lot of similarities between the two, but how was that journey along the way?

Rich: Yeah, I think we were always, we've always been very passionate about anything that moves. So dynamic assets are really where we focus. Marine, as I mentioned, yeah, definitely where we started. But then the natural extension for that was then working in areas like cargo. So one of the things we did last year again was looking at establishing a facility for tracking Ukraine grain and a facility that the insurance market set up to support shippers that were moving Ukraine grain out of Ukraine and around the Black Sea. So for us being able to go from ships to then connecting that to cargo was the first natural step. Some of our clients also had planes. So we then naturally extended into that space. And again, it's something that moved, but as we're going to look to grow, a lot of our clients have big property portfolios. They're not things that actually physically move, but realistically, but I think really importantly the things around a property do move. So worrying about things like a hurricane or a flood, a cat event, all of those things that are still dynamic that could affect a physical asset is really what we care about. So there was a natural extension really for everything that's moved into the kind of other physical areas of the world that we live in.

Interviewer: I'm sure with dynamic assets as well there are so many moving parts. Is there any part or any things that people don't naturally consider to be one of the big risks, but it's still one that you really look at and consider?

Chris: Well, I think that up until recently it has very much been knowing what assets you have insured anywhere on the globe, at any point on the planet as Rich has just talked about there around property, it has a fixed location on the Earth, so therefore it's much easier to locate. But with aircraft, I know it sounds like a very simple and basic component, but they do move and they can move very rapidly through airspace as well. So I think that's something that actually isn't well considered beyond just knowing where those assets are. It's also the accumulation between what's on the ground and what's in the air. So I’m talking about things like vertical accumulation. In an airspace, you may have aircraft that are at an airport and on the jetties, etc. But then you've got aircraft that are flying overhead. So, in a war scenario, you could have instances where both aircraft that are flying as well as on the ground being targeted in a worst-case scenario.

Rich: And I think that's a really interesting point around the kind of the geopolitical situation. And obviously with the world events that are going on around us, insurers being able to understand in real time when they're at an event, that does kick off. Whether there's, you know, something that's going on in Israel or there's a kind of prison outbreak in Haiti or an attack in Khartoum, you need to better understand actually of the things that we're ensuring around the world, what are those things that could be in one of those locations. And these events are obviously happening all the time and at different points. So being able to kind of be better informed and be confident that you know where things are when things happen is a very powerful step change, I think, in terms of how insurers operate.

Interviewer: Yeah. Well, this is what my next point is, with so much going on and so many moving pieces and dynamic as well as it is, how do you stay informed? How do you stay ahead and what kind of insights you're looking for to make sure you stay ahead of the curve?

Rich: I think there's a few things there. Where we certainly started, it was about step one: get your assets into a place where you can, you know, you can view them, you can track them and be confident you know where they are and anywhere in the world at any given point. From then it's about actually what other kinds of insights do you want to get from that data? So for us the natural extension was to then start looking at other third party data feeds. How can you start to leverage other threat intelligence providers, for example, that can give you a view that there is an incident that might be coming down the track that you can be better prepared for or it could be a weather event. So what can you do to start leveraging other hazard feed data, for example, to give you a view of the hurricane season? Are there certain things that are going to be coming up that you can overlay on your data? So it starts to give you a much more proactive view of your risks.

Chris: Yeah. And I think one thing that that we often think or don't think about as often, especially in the EMS space is ultimately as insurers, we're here to help service our clients and clients who at a time of critical need, IE a loss of an asset are looking for that financial security that comes from having an insurance policy. So by having additional insights into our data other than just purely where those aircraft are, for example, we can help to inform a company or a client's own risk strategy and provide that information back to them as well. So you know, if we're made aware that something might be happening in a location in the world, we can speak to the risk managers of those aviation companies and say, look, have you thought of this? Are you aware of this? I'm sure 9.9 times out of 10 they are because they have their own departments that are looking at this and they have a much bigger history in terms of their own assets and knowing where they are. But it certainly is something that we as an industry can provide a service back to the client beyond being there as a financial safety net.

Interviewer: I want to go into more detail on the tech in a second, but just from your perspective, is this one of the more difficult periods in your career in this space? Given how much is going on?

Chris: I would argue that there's no sort of difficulty to it if it's done in the right way. I would say that obviously with heightened geopolitical tensions that does make understanding risk more complicated. But we have subject matter expertise in those areas and that's what we're here to do. That's our job. So whilst there's more nuance to that area, it's something that we as insurance companies should be well versed in. We shouldn't have any or we shouldn't be surprised, should I say if there was an event that took place, it's just how we then respond to that.

Interviewer: As Chris mentioned there, but I guess from my perspective, it seems that with so much going on and so many moving pieces in your career in this space, is this one of the more difficult periods that you've had to deal with?

Rich: Yeah. I mean I think for me looking at it slightly differently and bringing it back to the data. For me it's the explosion of data, the things that are going on around the world. There are always new sources of data that you can leverage to understand or get a different insight or a different perspective on what's happening. And with that wealth of data, how do you harness it? And I think that's the biggest sort of shift I've seen. We've been talking about data-driven underwriting, different approaches to doing things in insurance for a long time. But now, when you really start to have access to these growing number of sources and actually much more modern technology and advanced technology to be able to harness that data, how do you orchestrate it and visualise it in the right way?

Interviewer: Staying with that, where does AI come into all of this?

Rich: I think AI is a really key enabler for that. So again back to that kind of explosion of data, how do you really, genuinely start to interrogate it and understand it in a meaningful way? AI, I think, certainly where we've been starting to deploy it already is used as very much as an enabler for the underwriter or for a claims team to be able to more quickly access the right insights into that data that they would have to normally manually look to interrogate or define some rules about how they could find the right report or the right analytics. AI really kind of accelerates the way in which you can identify patterns in the data that enables better decision making.

Interviewer: Do you agree Chris?

Chris: No, I absolutely agree. I think there's been so much talk around AI, especially with the launch of ChatGPT and everything else sort of 18 months or so ago. I think there's a lot of negativity that comes with the association with AI. But in my perspective, I can't see how it can't be useful to help in that world, especially like you say, Richard, around being able to analyse information quickly. But there is always going to be an element of reviewing that information, which is where a human and using your own underwriting teams and subject matter expertise internally or externally comes to play. So I would say it's a constructive tool as opposed to a replacement of anything that we're currently doing.

Rich: Yeah, I'd agree there. I think as you said, it's very much an enabler, a job aid at its simplest level, but actually an enabler for helping skilled workers to do their jobs better. But there's still an oversight that's needed in order to make sure you're training the models in the right way, helping models to identify the right things to be looking for. And there is certain validation that you want to better do to say yes, it's the right kind of insight or no, this isn't to make sure you're training it effectively. But yeah, ultimately if you think about the different types of AI use cases, whether you've got lots of structured data that you're just looking to assemble and look to analyse, that's kind of one great example. And using large language models to do that is obviously a hot topic. But actually just within the world of unstructured data, the reality within the insurance market is that we still operate within slips. We still operate schedules of values that are very unstructured. How do you get the data out of those formats before you can even start to interrogate them in a meaningful way? And I think that's obviously another area of AI being used in a much bigger way.

Interviewer: I guess the technology is coming on so fast as well?

Chris: Yeah, absolutely. It's coming on so fast. And I think that's where we just have to take a check of where we are as an industry and ensure that we're using it in the most appropriate way. I think the one area that's still fully to be explored is around predictive analytics and especially in a world of geopolitical uncertainty, there are patterns that are out there, you know, that's what we as analysts identify. But how can we use that and link that into what we were speaking about a few moments ago around helping the clients better understand their own risks or risks that they've not anticipated yet. There's often talk of Black Swan events but, truly, is there such a thing as a Black Swan?, Everything's probably been thought about. It's just been moved down, down the order. So yeah, I think it's very powerful. There's a lot more that we can do to utilise it and harness its power as an industry. So some very exciting times are coming up.

Rich: Yeah and I think taking it back to exposure specifically I think where we get really excited at Insurwave is where we start from the point at which we're just tracking something and you're able to link that to its insurance contracted terms and values. So you understand the value and the impact if there's an event or an incident that impacts that asset. But then really what you want to do is overlay other third party data sources to give you a different view of the potential impact in a certain area for a hurricane for example or a terrorist attack. But then when we get really excited is bringing it back to AI as that final step which is then saying, Ok, now I've built up an historical view of this data over a long period of time. What are the patterns in that data? Are there certain peaks and troughs in terms of where we see impacts go up and down at certain airports it could be or a certain port or a certain city or geography and being able to understand throughout the year, what are the points where we see certain trends emerge that might mean we have a different appetite for how we look at risk and how you support your clients.

Interviewer: That's a good point. I'm also just thinking of the viewer of watching this and if they're looking at this big area of the specialty insurance market here and they're looking at all the technology that's available. Would you give them any advice if they were looking at a blank sheet of paper and what would they do or what should they do?

Rich: I think for me the key thing is definitely get started. I think you know as we've all experienced in our careers you know it's becoming increasingly agile in the way in which we want to think and behave and we need to be iterative. And I think historically, there's always that view that we need to think back, step back and just think about this for a long period of time and come up with a strategy before we do anything. And I think the best way to learn is actually just by getting started. And for us at Insurwave way, you know, starting with being able to track things and understand how you do that well, overlay it into a visualised way in which you can kind of monitor things, learn from the mistakes. Because some things are easy to track, some things aren't. But then what comes next? What's the next best thing? What's the next best action we can do to inform the next decision or support the next process that's in line? So for me it's very much just about getting started. Innovating early. Start small but ultimately to get going.

Chris: I would say from the sort of ultimately the end user perspective there is as you rightly point out, there is so much out there that you can employ to help you do your job or better have better insights into the world of insurance. However, when considering what you are going to adopt, it has to answer a use case. So setting that out succinctly and saying, right, I need to answer these 2,3,5 questions. Maybe the board's asking for information, senior management, execs, etc, asking for more data, more information about a risk, a certain risk, maybe aviation, it may be something else. But can you answer the questions that they're asking? And then within that, what's next? We shouldn't necessarily always be blinkered by what's currently possible. We should always have an eye on future proofing situations or future proofing ideas of where we want to get to within that realm as well. So yeah, answer the questions today, but be able to think about what those questions might be in the future as well.

Rich: I think by getting started, you know, I mean even with us working together. We've learned a lot as we've gone on. And by starting and getting that first question answered, it uncovers another five questions that are exciting to explore next, but you only start to explore that and innovate when you actually get started.

Interviewer: Well, I think that's a great place to leave it. Chris, Rich, thank you very much.

Chris and Rich: Thank you.