Podcast 47. James Slaughter; Chief Underwriting Officer, Global Risk Solutions at Liberty Mutual Insurance

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Liberty Mutual is the world's fifth-largest property & casualty insurer. James Slaughter is responsible for setting the overall underwriting strategy, which includes pricing, risk appetite, distribution and innovation, for the global commercial & specialty lines of business of Liberty Mutual.

Matthew talks to James about what drew him to the insurance sector and the challenges of bringing change and new technology to the underwriting of large and complex risks. Topics include lessons to be learnt from personal lines, creating an internal culture of innovation and how the market will evolve over the next few years. 

More information is available on James as well as the head of innovation partnerships, Premal Gohil.

For your free copy of Insurance Insider go to  http://campaigns.insuranceinsider.com/instechlondon/

Listen here to podcast 47. It is also available on iTunes, Spotify and Podbean.

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Transcript for this podcast

00:00 James Slaughter: Technology is not doing something for the underwriter that they couldn't do, but it's doing it in a way that allows us to be much more effective and more efficient in our underwriting application.

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00:17 Matthew Grant : Hello, this is Matthew Grant, and welcome back for this week's InsTech London Podcast, or if this is your first time, thank you for joining us. If you like this, there is a great back catalogue now of interviews and chat from our events to dig into. Now underwriters are very much at the heart of insurance, and chief underwriting officers, in particular, have a tricky role of insuring their teams are writing profitable business but also sourcing new opportunities. So if you're building a company selling analytics, data, or even new solutions or products for insurance companies, then at some point, you're going to need to convince the underwriters to use your product.

01:00 MG: James Slaughter is CEO of Global Risk Solutions at Liberty Mutual. They're the third-largest property and casualty insurer in the US, and they've got a major presence in London. Now like many of the most successful underwriters, James has a strong analytical background, but he combines it with creativity and an eye for opportunity. We're bringing this to you with the support of Insurance Insider, whom we find to be one of the most insightful sources of information in the wholesale insurance, specialty, and reinsurance market. Take a look at the episode notes for a link to download your free issue of Insurance Insider.

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01:43 MG: James, great of you to join us on the InsTech London Podcast. One of the things that is always really interesting to hear from people is how they got into insurance. Now you did a degree in engineering, which is great, and then you went straight into insurance after that, I believe. So what was it that took you from engineering into insurance?

02:02 JS: I was at university reading engineering, didn't have any great ambition to be an engineer as it was, although I was pursuing, at the time, a career in the military as a sapper role engineer. That didn't quite pay off, and I got home with the bad news for my parents and promptly got told to go and find a living and packed my bag and walked to the city. My best friend was in the insurance industry and he gave me some insight and seemed appealing. So I applied, I got in and I'm stuck here, really.

02:30 MG: We'll talk a little bit about your role in a minute, but it'll just be useful to understand a bit about Liberty's specialty as part of Liberty Mutual. I guess one question for me, actually, is the mutual, in the Liberty name, what's the sort of history behind that, and then the bit about how Liberty specialty fits into the overall group.

02:48 JS: So, Liberty Mutual is a genuine US mutual. Very much at the heart of the Liberty Mutual creed and purpose is about helping people to lead safer, more productive lives, and that's true for our whole history, and we continue to pursue many of the founding sort of endeavours of the original Liberty Mutual and the many firms that make Liberty up. So I'm the CEO of Global Risk Solutions, which is one of our two insurance businesses at Liberty Mutual. We have a small commercial and personal business, GRM and GRS is really our specialty, our large commercial and our reinsurance business. I was a graduate trainee at what is now Aviva Commercial Union in the day just at the time they merged twice with General Accident and then Norwich Union. We were then offered the chance to join Berkshire Hathaway as part of the sale of the Marlborough agency. So I then spent six years working for Berkshire Hathaway with Tom Bolt, and then I moved to Liberty originally as a reinsurance underwriter doing a range of products, and then in 2010, I was consulting for the group on reinsurance strategy, ERM, and CAT modelling and I took a permanent job to set up our internal and global reinsurance function, and I ran that until the end of 2017.

04:13 MG: So James, the CEO role in itself must be almost all-consuming, just keeping the wheels turning on the business, but I'm sure you're looking at some other things outside of that. What are the main areas of focus for you? Is it to look beyond the day job?

04:25 JS: So, I'm very lucky. We have a very strong underwriting leadership within the businesses who focus on the day-to-day stuff. So I have I guess the luxury to spend some time looking at longer-term needs for the business and possibly some more existential work. So that's great fun, so that includes our innovation group, which we'll probably talk a little bit about later. I have the advanced analytics group, so, a lot of where we're doing the machine learning and artificial intelligence, predictive modeling kind of work there. I still maintain my role in reinsurance. And then in the underwriting side, I'm really focused on a couple of areas, really. The foundations and the first principles of underwriting, ensuring that we've got those consistently across our organisation, so you might think of those things as appetite. So we were four underwriting organizations coming together. It means we have to ensure that we've got a common appetite and a common go-to-market strategy, so that's important work.

05:23 JS: And then longer term, we encapsulate this under what we call elite underwriting, but trying to think about the role the underwriter plays in the value chain and where we can augment decision-making with technology and data and where we really need to find the efficiency and opportunity for our own writers to contribute their very best. And some of that is freeing them up, and some of that is bringing new tools to the game, and some of it is actually the skills that underwriters need today are probably different to the ones I had when I first sat on the box in Lloyds in 2000. So, a lot of the focus there, really, is on how the future of underwriting and the elite underwriting role is going to develop over time.

06:03 MG: Yeah, and that thing about the role and the relevance, the underwriter seems to becoming more and more prevalent or be has always been known. But I think for a period, there's a view that technology could almost replace the underwriter, when we were talking earlier, you sort of mentioned that actually in your experience, that we were a long way from being able to do that. I mean, what would be an example of somewhere where you see this change coming in from technology, it may be enables the underwriter but it doesn't replace the underwriter, but it does allow them to do something that maybe they couldn't do, say five years ago.

06:31 JS: So I don't think it's things they couldn't do. It's about how much and the capacity and the effective and efficient nature in which they carry out their work. So, a good example would be triage. So, if you have a well-articulated appetite as you have your inbound submissions, there is pretty simple technology that you can screen and start to rank those submissions based on attractiveness, metrics like propensity to bind, and estimates around what sort of rate you might get. So you could have 500 submissions in a week, but your underwriter can only really deal with 100. Instead of picking the first 100 that come through the door, just a simple bit of technology can allow you to rank those against most attractive from an appetite perspective and most likely to bind, so our effort is concentrated in those areas where we're most likely to succeed in those areas that we are most attracted to from an underwriting perspective.

07:31 JS: So very simple, it's not overly advanced technology but it's using some of those machine learning capabilities that we're building. And for me, it's about the underwriter focusing on our best prospects. So an underwriter, I always think about the things that I get rewarded by, so that would be the size of my portfolio, the profitability of my portfolio, the ability to sell the right products to the right customers, is if I can find ways to unlock my efficiency and do more of that as an underwriter, I'm going to feel better. So I think triage is a very good example where technology is not doing something for the underwriter that they couldn't do, but it's doing in a way that allows us to be much more effective and more efficient in our underwriting application.

08:18 MG: Okay, I just want to come back in a minute, before I do that, just for anybody that's not familiar with the term "bind" or you said "propensity to bind," can you just explain what that means in practice?

08:29 JS: So propensity to bind is really a probabilistic measure based on the factors that we might determine, so, that could be anything from a known history of the client and what they've bought in the hot past. It may be our relationship with the broker and the understanding we have of how the broker markets that type of business. It may indeed be around our first-cut view of pricing, so, are we in the game? We have a view of the market and we have a view of our own pricing, and it's safe to say that on occasions, we know the market will be more efficient than we are and where we're not in the game, we wouldn't necessarily go. So, you can, using very standard predictive techniques, you can assimilate some factors as you bring those together, can effectively give you a probability of likelihood to bind. And we can use that to guide our underwriters towards those more likely to bind, and that enables us to increase the efficiency of our underwriters.

09:32 MG: And more likely, meaning more likely to fit the criteria user as opposed to the customer is going to continue the journey, or the broker's going to continue the underwriting journey?

09:42 JS: It's relative, so it's more likely than the other one, so it's a relative ranking. So, if it's perfect appetite match, if it's right in our pricing sweet spot, if it's with a broker that we do a lot of business with or a client that maybe has three, four other lines, we would probably hope that the likelihood to bind that kind of policy is higher than a new piece of business where we're at times over the market with a broker we don't do much other business with. It's relative, it's not an absolute number, but it allows us to essentially rank and then attack those higher likelihood accounts in our priority.

10:20 MG: Right, so this part is a reflection of your own portfolio or existing portfolio where you've got opportunities, and then partly these external factors with regards to the client itself.

10:29 JS: Yup.

10:29 MG: And what's the balance between tools you develop internally to enable you to do that versus technology or analytics or data that you'd buy in from third parties?

10:40 JS: So that's a great question, and that's actually one that is burning on my desk right now. So, historically, excluding CAT and capital models, which were traditionally vendor models in all the insurance industry certainly the time I've been here, most of our bought models are built in-house, strong actuarial functions, good connectivity with the underwriters enables us to build, indeed, some of my team have come from those parts of the business that used to build tools and models, so most of it would be in-house. I've just challenged our guys and girls to say, "Are there more efficient ways to build models?" So, if you think in terms of machine learning, for example, as I'm learning in my new job, there are ways and means for you to essentially commoditize the code and import that in an open source environment where we're not having to do much of the plumbing and our guys can spend... Our guys and girls can spend more time on the interpretation and the analytics. So, we're actually running a really interesting experiment as part of our wider elite underwriting where we're standing up external nearly open source, not quite open source, code, versus our own capabilities and just measuring the differences and seeing whether those differences both operationally and output-wise are interesting for us... Enough for us to review our internal build model.

12:10 MG: To find the organizations that have got those applications that you can link into through your open code or open platform, do you channel that through your innovation team at Liberty Mutual, or is that just part of what you can expect people in the team to go out and find themselves?

12:25 JS: So we have a dedicated innovation and insights team as part of my organization, so, across the underwriting organization, and focused really on three areas: The changing nature of risks, so what's being experienced by our customers and trying to address that, whether that's emerging risk, whether that's evolving technology in their own businesses and all the way in which their business has changed from tangible to intangible et cetera. We then have how our world is changing, how our processes are changing and we focus on underwriting the service and thinking of insurance as a service as well, and then we have the future of capital. Where is the capital coming from, what sort of risks they're looking about, how do we solve protection gap issues, what sort of products can close the gap? So inside embedded within that, we have the standard partner invest by build kind of approach, and we've got both at the Liberty Mutual group level but also within GRS where we partner with the Liberty Mutual ventures and the ability to engage either as our partner, either investing, and we've made some investments, or indeed, to look at partnerships more broadly with them.

13:38 JS: So we scan, we go out, with find interesting companies, we've got some great partnerships right now helping us. We announced as a Liberty Mutual a tie-up with MIT recently to really look at how we can turbo-charge machine learning and AI in not just our immediate industry sector but also more broadly. So, how can it bring value to our customers, both personal and commercial? So, a good example where we're partnering with outside bodies to help really drive forward some of the analytic space.

14:12 MG: And on that partnering piece, in the personal lines, there's been quite lot happening with connected car and connected homes and things. It's still... I think some cases still, in my mind, to be proven that really works, but as you look at what's happening more on the commercial side, what would you see as examples where beyond just efficiency and able to help underwriters with triage and managing volume of risks coming in, but are you seeing anything that really is differentiating with regards to, for example, connected homes, connected commercial properties, that whole broad area of IOT?

14:50 JS: If we go back to the mobility piece, we've put together a mobility pod at Liberty Mutual where we've bought our personal and commercial capabilities together to address the fact that as AV becomes more prevalent so the risk shifting needs us to have a different skillset, a different approach to the market, so we recognize that. So, that's relatively new, and we've got some interesting programs, whether that's Care by Volvo, Optimus Ride, or indeed, an education program with PAVE where we're not just doing it for the insurance, but we're actually educating on the safety and benefits of autonomous vehicles. So we've been in that space for a long time. If we go into IOT, I think there's huge opportunity, and I've spoken about how you could envisage IOT-enabled blockchains for immediate claim settlement post-hurricane, for example, where wind speeds are measured. You do fairly parametric-driven measures for a roof damage, given a wind speed, and you could envisage a world in which that's much more digital and efficient.

15:56 JS: But right now, I think there's a long way to go in terms of our ability to ingest and manage data more broadly across the industry, and I think there's still an element in the commercial space, certainly, where there's a lot more value being created just in what we're doing today, and we can do a better job at that rather than take our focus off the core and explore too far onto the edge. But we have some interesting drone programs where you're using devices to help us assess risk or indeed collect data. Pre-bind. So there are some interesting areas where we're exploring. But I would say the pace of acceleration there is fairly low, and I wouldn't say that's at the heart of how we see commercial over the next three to five years evolving.

16:48 MG: I think it's thematically true across the industry where IT, intuitively you can see why it makes sense to be able to get our access to data from machines or buildings, but people are still struggling to really build that into the underwriting process, both in terms of adjusting the price and also ultimately impacting their raw risk mitigation in the cover. So as you say, it's going to happen at some point, but it sounds like there's nothing you're looking at this now also nothing you were going to talk about where you actually see something that's coming up as a significant opportunity in the commercial space.

17:20 JS: No, and I think... So my learnings over the last two to three years and certainly within Sure Tech emerging as a hot button for everyone to try and understand and some of my involvement with some of the interesting projects that have been out there from B3I to some of the stuff we do at Liberty tells me that the agile, quick failure, the sort of POC approach tells me that it's always going to be in small doses and it's going to be gradual. I think Big Bang doesn't feel like the way in which these technologies will emerge into the of normal day-to-day way of doing things.

18:03 JS: So we do have a lab obviously in Boston Solaria Labs where we do quite lot testing of this stuff, but they are very small scale. And if you find an interesting one, you can scale it to a degree, but I don't see a sort of overnight Black Wednesday moment where underwriters are suddenly replaced by a pure interconnected, machine-learned underwriting algorithms. I find that extremely difficult to envisage, and I particularly put value on the fact that however hard we try to capture the risk incumbent in a schedule of locations or intangible assets, you still need to have some sort of judgment applied to it. And so, as you know, in previous commentary, I've talked about augmented underwriting and I think, for me, the great value in the emerging opportunities in data and analytics technology are about augmenting great underwriting decision-making. And for me, I talk right at beginning about elite underwriting being my focus, I want underwriters to feel very comfortable making great decisions.

19:12 MG: Well, you'll be pleased to know that when I spoke to Mark Gagan last week, he felt that journalism was going to survive longer than brain surgeon 'cause you could replace a brain surgeon with a machine. And I guess it sounds like what you're saying is that maybe underwriters might survive beyond brain surgeons because the complexity of the risk decision is such that it's only for near-term, or for the rest of our careers, you're still going to need people to take a view on the data that's coming in. You can't solve that independently of some human intervention.

19:40 JS: Yeah, I'm not sure I would let a robot in my brain even if it's quite small, but I genuinely think that there is an argument for uniqueness amongst our customers. They do have generally different risk profiles, needs, and you need to be able to capture that. And I think that's hard to capture in a codified, tacit way. I think it's much easier to do that as a human. I think you just can look afield. Now, can you make more efficient decision-making? Can you improve the efficiency and all of that? Yeah, I absolutely think you can. But I still genuinely believe that human augmented decision-making will be at the heart of certainly commercial decision-making for a very long time to come.

20:28 MG: Now, big insurance companies, or even medium-sized insurance companies, have been a target for the last few years of people either looking from outside of the industry or even those in the industry, saying they're moving too slowly, they don't get it, they can't do innovation. From your point of view, you're obviously being in an insurance organization that's already committed quite a lot to innovation, but for a really practical point of view, what do you see as the industry as a whole being challenged with? Is it a perception, or is it just a lack of appreciation that both the technology is there and there is a need to change as... Then get it, or do they get it but it's just trying to implement, it's actually very difficult in all industries when you've got an established company, and therefore, this takes time to sort of figure out what the right things are to do?

21:15 JS: That's a complex question, and I think I'm going to split my answer into a couple of parts, if I may. So, I think inherently, outside-in perspectives miss one of the key aspects of insurance, which is we're extremely highly-regulated industry. That regulation serves a very important purpose, which is ensuring that we're here to pay valid claims hopefully in perpetuity, and that is not something that is... You can't rip that piece up. That doesn't get broken up. So, in terms of the fundamentals of an insurance company, taking the premiums, investing them safely, and ensuring that we are suitably capitalized to pay due claims, I think people misunderstand how complicated that is and how important that is absolutely to the delivery of insurance as a valuable product to its customers. So I think people looking outside in, probably don't understand that as well as they could, and perhaps because that's not a particularly sexy, or groovy, or tech-driven area of the business, it gets not a lot of focus, probably rightly so. But if you don't understand that, I think it's very easy to just throw sort of wide dispersions that we're a bunch of Luddites and don't know what we're doing.

22:35 JS: The reality is we're hiring extremely capable, talented people into our organizations, and this is a general view across the insurance industry now, at a faster rate than ever before, that companies like Liberty Mutual have made specific allocations to invest in technology. Without going through the list of all of our technology, some of the technology solutions Liberty Mutual has brought, particularly in the personal lines space over the last four or five years, are as good, if not better, than anything that's coming from outside the industry. And I can think of claims apps that we have on our iPhones for your own fault claims if you're in an auto incident. I can think of some of our drones... Use of drones to capture damage which transforms the way in which we can adjust claims, particularly in weather-related events. I think it's a dangerous accusation to throw at the insurance industry. Having said that, I also think you need to be large and you need the capacity to absorb the fact that, as many VC portfolios know, your success rate is a very strong bell curve with a 5%-10% upswing and a 95%, 90% failure rate, and you need to be big to absorb those costs.

24:00 JS: And I also think you need the data and the creativity and the ability to focus on it in an appropriate scale for it to bring value. Now, like at Lloyd's, I think... I like the way Lloyd's is leveraging their lab. We're obviously involved in that program. I think that's a nice way, again, to bring some scale and effectiveness to exploring opportunities in the tech and innovation space.

24:25 MG: And so picking up from the lab, it's interesting, this third cohort, they're now focusing more on established companies with a solution more closely linked to the future of Lloyd's manifesto. In your own experience of what's happening in Liberty, what do you say is that balance between where we were three years ago when there was a lot of emphasis around the new people coming into the industry startups, really well-funded, that didn't have a lot of experience versus... My views are seeing more now where you've got more established companies that may be coming from that side of the industry. And you mentioned Volvo is one of your partners. So that balance between the ability from a sort of small team or a bit well-funded to actually make a difference versus companies that have made a significant sort of... Or significant access to clients and data and solutions outside of the industry coming in, and actually can probably move things much more quickly. How do you deal with that balance between this sort of new... The new... New-new and then the kind of new to the industry but actually already established organizations?

25:33 JS: So I think there's a very important piece to all of this innovation, and that is the... Most of it's focused on an aspect of the value chain, not the whole value chain typically. And depending on where it's focused, almost all of it relies on data. And you hear it all the time, they're about all of it, Open Access, third-party data, lots of chat about that, it's pretty much open source, third-party data, it's available to anyone. So, as a competitive advantage, that's quite a low barrier to entry. What really differentiates the lot of this is that the partnership with a firm with lots of data, and that's really finding a large incumbent because that's where the data is going to come from.

26:16 JS: So what I think is the learning curve that's going on in the innovation space and the insurance industry that I've witnessed is really that people have realized that you can't really attack this alone. There is just a scale and data need, and so what you've seen is probably more established companies, but they're more established 'cause they've created some partnerships. They've probably done a couple of pilots with insurance companies and they've learned the value that actually, it really is a partnership in the space that they're challenging. If you think of some of the bigger companies looking outside in, we've seen Tesla announce recently their own insurance program, Care by Volvo, I mentioned that.

27:01 JS: Again, that's about the advantage of scale and data, and coming together to apply the technologies in a way that leverages insights and value from those. I think it's... What we've seen is people got very excited, particularly in decent mediation and direct and efficiency-focused innovation and quickly realized that that's all well and good, but you've actually got a partner with someone who can help drive that. So I think there's a realization that you can't just take the incumbents and assume that they're rubbish and off-point and going to fail, and think you're going to beat them. And for those two reasons I mentioned, the first being regulation and structure, and the second one being you need us to help with the data to help verify the model to then help create the value.

27:52 MG: And so this looking for solutions bit, what's the balance between looking for things that help you in the core business just get better at what you're already doing versus looking for new opportunities for new lines of business and bringing in new revenue that you might not have been able to underwrite before because you didn't have the tools or the data or the confidence and being able to price it?

28:14 JS: So, absolutely at the heart of our own Innovation Insights Program is the split between sort of fortifying the core and then exploring and learning about the edge of the opportunity set, if you like, for what it can do for Liberty and our customers in the space. We're probably 70% core, 30% new, in terms of portfolio split. I've got no idea if that's right or wrong, it feels about right. And the reason it feels about right is that there is plenty of opportunity within our own firm, and I'm sure true of others, to build those insights into our execution model and use that, as I've talked about before, to bring our underwriters into a more efficient and effective space so their decision-making is that much more valuable. So, yeah, we explore, and we examine, and we take some funky ideas, and sometimes we play with them, and sometimes we don't. But the focus really is on fortifying that core to really improve what we do for our customers today.

29:21 MG: And on that point about the underwriters, what do you find is the appetite amongst your team for coming up with new ideas and then they can get through space to go and do that. Are they... Been knocking your doors? They're asking for time to go and explore something, or do you have to sort of drag them away from their day job to get them to think about the new things?

29:39 JS: That's one of the leadership challenges you face. I think in innovation, new business-type structures and in large companies, quite often, people have good ideas but quite often they've got their day job and it's difficult. So what we've done is created a pathway for ideas. So, if you've got a great idea and you're sat in our western office and you think... You probably don't have the time to do it, but you think it's a great idea and it's going to bring loads of value for our customers, we have a mechanism by which you can bring that through to our innovation team, and we can scan it and do some preliminary work with your support. And if it's a really good idea, it will go up to our innovation council, and we'll give it some corporate backing. So, trying to create the culture in which ideas get visibility and air time is really important.

30:33 MG: James, you mentioned earlier that you've got a lot of reading due to sort of catch up with what's going on, but how do you actually go and learn and find the time to learn what's going on in what is a fast-moving marketplace? What's your preferred source of information?

30:48 JS: Mostly it's talking, and many of our business partners have innovation going on and so we do a fair amount of sharing, we bring business partners into the lab quite often, so we share constructively on how we can develop it. And then for me, I'm fortunate enough to have people knocking on the door to come and chat about the market, normally trying to sell me something, but that allows me to get their insights, and I've got a very, very strong team across all of my areas, but in our innovation team, I've got a really diverse group of individuals who bring different skills and different ideas and they're very creative and very keen to share those ideas and get them in front of you. So, in the sort of environment we're in today, you need to take different sources and different opinions, and enable that to help form your perspective.

31:36 MG: Yeah, and I think it's one of the powers of London and links back to your point about underwriters aren't going to be replaced by machines, is that that human contact and that whether it's serving... Organised meeting or just bumping into somebody on the street, and I guess you're off to Monte Carlo next week and he has a lot of us bumping into people, that sometimes that's where you get the best insights just by sharing some information.

31:58 JS: I had the pleasure to be invited to speak at Insider Tech, not that long ago, which I learned a huge amount from preparing for that, for example. That's a great opportunity for me to challenge my own thinking 'cause I'm then going to put myself in the spotlight, and I have to be able to defend my position. And actually, I quite enjoy the opportunity to put my ideas into the public arena and get the feedback. And that allows you, again, to reflect and challenge your own ideas and make sure you're still taking in the right concepts and you're processing those in the right way.

32:32 MG: Yeah, and I was there, and I took a lot of notes about your underwriter of the future. So maybe we should get together in five years and see if what you said has actually come real. And James, if anybody's got an idea they want to talk to Liberty about, what's the best way for them to connect with you or any of your team?

32:51 JS: I would say if you're in London or Europe, Premal Gohil is my innovations insights man here. He deals with our partnerships, and he's very much focused on scanning and looking for opportunities that could excite us. So, he's an active member of the LinkedIn community, so you can find him on there, and he's quite often commentating on various things in the market. He has his own podcast for internal use as well, so he enjoys a podcast.

33:21 MG: Tremendous. Okay, well, we'll make sure we connect with Premal, and I'll let you get back to your job. I'm sure you're watching anxiously what Dorian is doing and for the rest of the hurricane season, but James, thank you very much.

33:34 JS: Thank you.

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33:40 MG: Well, clearly, there's a lot going on at Liberty and I'm sure getting up on stage soon. In the meantime, as James mentioned, Premal Gohil is responsible for Innovation Partnerships at Liberty. He's based in London. You can find him on LinkedIn, and I just noticed, he had close to 20,000 followers. Premal will also be coming along to our events later this year, so, if you're in London and haven't signed up yet for one of our monthly events, you can do so at www.instech.london.