Why AI needs to raise the bar of underwriting, not just the floor 

Matthew: Jamie, great to speak to you. You are vice president of strategy for hyperexponential, a company that we’ve enjoyed getting to know over the last few years and follow your success. We’re gonna use the abbreviation HX today, just to make it a bit simpler. 

Founded back in 2017. It raised $73 million in January 2024. You initially started off as a pricing platform, but we’re gonna be talking about how that’s developed now across the whole of underwriting, and also actually getting some input from a survey you did, with an extraordinary number of underwriters contributing to that. 

You’ve expanded into the US. We’re gonna hear a bit about that. And Jamie, prior to joining HX three years ago, you were an insurance actuary at Allianz and WTW, welcome. 

Jamie: Awesome. Thanks for having me here, Matthew. Looking forward to the conversation. 

Matthew: The question everybody is always interested in: people who’ve got stable careers in conventional insurance companies leave to go — I guess you were a scale-up when you joined HX. But what was it that took you from a stable life of being an actuary into something a little bit more risky with a scale-up? 

[01:00] 

Jamie: I have to admit, I wasn’t looking for a job at the time. I was very, very comfortable working at Allianz. I’d been there for a number of years. I’d had lots of very interesting and exciting roles. But a couple of years before I left Allianz, I got the opportunity to actually play around with the HX platform. In my role, I was helping to lead a technology transformation project. We were looking at lots of different applications at that point. So I got access to the HX platform well before I joined the company, had a half day of training, and I then spent that very first evening after the day of training playing around the entire evening with the platform. Much to my wife’s frustration, I then spent every evening of that week playing with the HX platform, seeing what I could build out, trying to build out new pricing tools, seeing how I could push the boundaries of what it could do. And every day I’d come back into the office and I would jump on a call with the HX team and basically ask them all of these really gnarly technical questions. [02:00] 

They had exceptional answers for every single difficult question I had. I loved it. It was one of my favourite weeks of work. I was genuinely excited about how we could leverage technology like that to deploy improvements to the front line of underwriting much, much faster, especially on the pricing side of things. So two years after that event of playing around with the HX platform, I got tapped on the shoulder from someone I knew at the company at HX, who basically invited me to apply for a role as head of pricing and innovation at HX. 

And as you were saying, I had never at any point in my career thought of moving into tech. But actually I kind of weighed it up and I was like, “Well, this is an opportunity to keep playing with a product, some technology that I’m particularly excited about,” and also an opportunity to work with the wider market as well on their transformations — getting the opportunity to not just see how this is working within the company I’m working in today, but how the whole [03:00] market is facing pricing and underwriting transformation. So that for me was the big driver: both product, and also the opportunity to see how the wider market was working on these problems. 

Matthew: It’s a great story. It’s always interesting talking to people who’ve founded companies because they had a problem and couldn’t solve it, and went off and built a product, which I think is actually Amrit’s story about why he founded hyperexponential. But, I mean, you’re the other side of that equation really, aren’t you? You find a product, someone’s built it, they’re kind of solving your problems, and then you go and join the company. And a reminder for all of us actually how serendipity plays such an important part in life — you come across that, and then someone offers you a job, and you are working at a tech company, which is going really well. 

And, and of course, in role as well. So what does it mean to move from being an actuary into VP of strategy? What’s your day job look like? 

Jamie: Yeah, the day job’s an interesting one. VP strategy, I get the joy of working on lots of different areas of the company, but at the core of my role, I’m involved very heavily in longer-term product strategy. So where do we place our bets? What [04:00] are the things we build as a tech company that will drive the greatest value for our customers? And crucially, what are the things we choose not to build as well? Both of those things are equally as important. So I’m very involved on that side of things, and then also very involved in the go-to-market of new products or the go-to-market in new regions as well. Wherever we’re starting to release new products or new capabilities, I get very involved in conversations with customers, and making sure that process runs smoothly. And then when we look to enter new regions or markets, I also get heavily involved in that side of things as well, just leveraging my experience working across different regions and markets historically. 

Matthew: And I’m glad you mentioned that strategy’s about what you don’t do, because so often companies fail because they try to do too much, and that discipline to zero in on what’s really important. But on that topic — and I hinted this earlier on with the description of how you’ve moved from the pricing platform — what problem are you solving for people today as you look beyond offering a pricing platform? 

Jamie: As you referred to earlier, [05:00] HX started with Amrit looking to solve a particular problem, Amrit and M.J., the two co-founders, and they were looking to solve problems around the pricing part of the underwriter journey. And part of the reason for that was that’s a very high-leverage decision point in any underwriter workflow. But in particular in the commercial specialty world, at least at that point in time, in the history of HX, it was a part of the workflow where there was a lot of pain points associated with it. I knew that from my own time working in industry. I knew that from industry peers, people that I’d worked with. And so HX effectively looked to try and solve for that pricing problem: can we help our customers deploy pricing models, deploy pricing tools to underwriters at speed, and can they iterate on these pricing tools, drive greater insight to underwriters, again trying to do that quickly and at speed. And so we saw really great success in that space. But one of the interesting things that started to happen, I’d say more like three or four years ago, we started to see customers pulling us left and right of that pricing part of the underwriter workflow. Pricing is really at the heart of, or the centre of, the underwriter workflow. We started to see customers basically saying, “Can we — [06:00] that calculation engine, HX, that we currently use for pricing, can we use that for other parts of the workflow?” And they said, “Can we deploy a risk appetite model here, or a predictive model there, that’s not really pricing?” And then we’d have other customers start saying, “Can we use the workflow components or the flexibility of the platform to start pulling you into other areas like peer review, referrals, authorities?” And so we had customers doing that, and they were pushing the platform beyond what it was originally intended to be. 

So about three years ago, we then started responding to that and building out new product capabilities, features, to effectively allow our customers to actually build out a full end-to-end workbench within the HX platform. So that’s where we’ve got to today. The way I would describe the platform, or I would have described the platform a few months [07:00] ago, would have been as effectively an AI-assisted workbench. I can take a submission in, I can use AI at various points to help the underwriter on their journey, and I can go all the way from the submission going in to a quote going out, all within the HX platform. 

Matthew: There’s a very interesting Harvard Business Review article which, to your point about looking left, looking right, sometimes referred to as upstream, downstream, on where to look to grow the business. And there’s actually another quote I’m sure you’re familiar with, which is that when you get to the top two or three in the market you’re in, you basically have to expand your market, and then you go into a bigger market, which sounds like you’ve done both of those things. 

Jamie: Yeah, that point around looking left, right, or looking upstream, downstream, that drives a lot of our product strategy. If a customer is pulling us in that direction already, that’s the best place to move. It may be that you as a tech company are like, “This would be great if we could go and do X, Y, and Z.” [08:00] 

Ideally, if you’ve got customers trying to do it in your platform already, but the platform’s not built for it, that is the ideal indicator that this is a great space to start moving into. And that’s exactly what we did about three years ago as we started to branch out across the end-to-end underwriting workflow. 

Matthew: Yeah. Albeit, of course, you’ve got that tension of not doing what you don’t want to do, so I guess sometimes you just have to say no, or maybe not yet, to the client that’s trying to drive you too fast. And then this underwriting survey — you had 350 people respond to it. I’ve run surveys before. I know how impressive that is. So before we talk about the survey, how did you actually even manage to get that many people to contribute to giving you feedback? 

Jamie: Yeah, so we partnered with a company called Coleman Parkes. We’ve worked with them before on these types of surveys, and they’ve been absolutely fantastic at driving that engagement with the surveys. 

[09:00] 

And so we managed to get, exactly as you were saying, 350 underwriters responding across the US and the UK, all senior underwriters, so lead underwriters, or heads of, or CUOs, all working in commercial and specialty. So trying to tailor it to the audience that’s most relevant to the underwriters and companies that we work with day in, day out at the company. 

Matthew: Just before we go on with that — you’re going to make this report available to the listeners of the InsTech podcast, I think, as well as other people. And that’s available on the website, I believe, and they can contact you directly to learn more about it. 

Jamie: Yes. Yeah, so exactly that. So if anyone wants access to that, we’re going to be releasing it in September of this year. It’ll be available through the website. You’ll probably see some bits and pieces on LinkedIn about it. If you can’t, for whatever reason, find it, please feel free to contact me on LinkedIn. I’ll happily point you in the right direction so you can get access. 

Matthew: You gave me a preview of the report, and it is fascinating, and, no surprise, heavily orientated around AI. And one of the themes that came out of this — perhaps you could just explain it a little bit, I can sort of understand it, but — was that AI is pointed at the floor, not at the ceiling. [10:00] 

I’m guessing that’s all about fundamentals, but could you just explain a little bit more about what feedback you’re getting related to AI? 

Jamie: Yeah, definitely. It’s this idea around: where are companies actually investing with AI at the moment, and where do underwriters want support? What I mean by that is one of the questions we asked was asking all of these 350 underwriters, “Where has AI delivered the most meaningful value in your underwriting to date?” Unsurprisingly, 51 percent of them said it was on saving time on manual admin. So it’s very much lots of efficiency gains, that’s where the AI has been adding value. Amusingly, 3 percent of them said they have received no value from AI yet, so there’s probably 3 percent of CIOs who are very upset about that statement. But 21 percent said that AI had driven improvements in the quality of the decisions. So you’ve got effectively [11:00] 50 percent saying AI to date, it’s all been about efficiency, that’s where the value’s come from. 20 percent saying, actually, AI is helping us make better decisions. But we asked underwriters a different question, which was: what makes an underwriter excellent? We got them to imagine the best underwriter you’ve ever worked with, what about them made them exceptional? The top things that came up time and time again were all expertise- and judgement-led. So on that list was risk selection and appetite discipline, and then technical depth on complex risks, or commercial instinct on price. So all of these things were around technical underwriting, expertise, judgement. That’s what makes a truly exceptional underwriter. Speed, efficiency, barely registers on that list at all. And so it’s this really interesting thing: the industry has effectively focused effort on how do we do things that make us more efficient, that’s effectively raising the floor, everyone gets a little bit faster. But what underwriters really care about is exceptional underwriting, [12:00] how can I make better decisions than anyone else, how can I be the best underwriter? And in reality, that decision insight, how is AI driving value there, underwriters, the response we’re getting back is actually that’s not where most of the value from AI to date is coming from. So it’s this idea of: the floor’s being raised, everyone’s going a little bit faster, but the aspiration, the exceptional underwriting, is that roof being raised of what could possibly be achieved today. 

Matthew: It’s so interesting. It’s a bit of a tortoise and the hare, in the sense that people are probably overreacting to AI being all about speed, but recognising you don’t always have the expert in there, and so they’re now saying, “Well, it has to be right.” And then, of course, once you’ve got it right, then it becomes about speed and efficiency. So neither is wrong, but you just need to be careful, you’ve got to get one right before you get the other one, and then you can’t get complacent because you got one right. I don’t know if you saw it, but there was a fascinating video today in The Times of the Robot Olympic Games, [13:00] and when you see what they can do with robots, you just think, well, the robot can do backflips and dance, and then is underwriting really that difficult? I don’t know if that was part of your survey, or you’ve got a view on that, but it’s pretty extraordinary. 

Jamie: Yeah, we didn’t ask underwriters their views on robotic Olympics yet. But I think you’re exactly right, looking at how that’s evolved over the course of the last few years is a nice indication that everything’s up for grabs in the next few years as well. 

Matthew: And then AI as well, we started to talk about agentic. We’ve run an event on agentic. But can you just give your perception, or what you came out with through the survey, about this distinction between what is now becoming the generic term of AI and a slightly more focused term around agentic, and clearly it’s got a slightly different meaning as well? 

Jamie: I go to a lot of industry events, talk to lots of people in industry on the tech side and on the insurance side. I’m also a bit of a pedant when it comes to terminology, probably the joys of being an actuary by trade. I’m listening to talks and people are talking about machine learning, AI, language models, agentic, and a lot of the time these terms are used [14:00] interchangeably, when in reality they often have very specific meanings for very specific contexts. The way I would describe it, talking about AI versus agentic, I like using the phrase AI-assisted versus agentic. And what I mean by that is AI-assisted is where a human is doing a piece of work and occasionally, as part of that piece of work, they will call out to AI to help them with something. So it might be, if I’m an underwriter, let’s talk about that, that’s what the survey was all around, if I’m going through my day-to-day work, a submission comes in, I’m responsible for taking that submission all the way through to quote. It may be that I call upon AI to help me with certain parts of that workflow. So I call upon AI to help me ingest the risk, or I call upon the AI to help me check whether or not this risk fits within my underwriter guidelines, for example. That’s an example of AI-assisted, the underwriter, the human, is in the driving seat. They are moving the work along, but they just get AI to help them at various points. Agentic [15:00] workflows, or agentic underwriting, is the inversion of that model. The agent can actually move work along the workflow and can actually do the work, and calls out to the underwriter, or calls out to the human in whatever workflow it is, to get help when the agent can no longer do the work. And that could be because the agent either doesn’t have the understanding, or it’s met certain guardrails that it can’t go beyond. But crucially, that’s the difference between those two things. 

Matthew: Yeah, I used to call that the humble algorithm, where the critical thing is it does know it doesn’t understand, and doesn’t get into that whole world of hallucination, which is onto what you’re doing at HX. So you released a product, Hyper Operator, which has extended your reach. Can you just talk a little bit about what that is doing? 

Jamie: We announced the Hyper Operator in July of this year, at our flagship event in London, HX Live. It’s an extension of the general product direction in some respects. We’ve got to this point [16:00] where HX, the platform, is effectively an AI-assisted workbench, an end-to-end underwriter workbench. I’m going to use that term AI-assisted very purposefully. The Hyper Operator is effectively, sometimes we describe it as the AI agent for underwriting work. It runs across the HX platform, and it has the ability to leverage specific tools and specialist agents for whatever it’s working on, and it can effectively act on the underwriter workflow rather than just advise or support on individual work points. It does all of that within the platform’s guardrails that the insurer can set up. That’s a bit of a mouthful, but it’s that inversion of agentic to AI-assisted, in the sense that the Hyper Operator can take a submission and bring it into the platform and move it through the workflow, take it maybe through ingestion, through clearance, get it through some sanction screening and basic checks, get it to triage. [17:00] 

And maybe at the point of triage, the guardrails are set up such that this is on the boundary of being in appetite, so I now need to call out to an underwriter to take a look and make an actual judgement decision about whether or not we should move on to the next stage of underwriting, if that makes sense. So the underwriter, or the insurance company, can set those guardrails. Those guardrails are really key, because it may be that I’ve got one line of business over here that’s really complex, my appetite for agentic workflows currently isn’t there, and I want an underwriter to be involved in every single decision point. And so what I almost do there is dial up the guardrails so that agentic autonomy, the agent’s autonomy, is dialled down, in essence. So the submission comes in, and maybe the agent does ingest the risk, but someone has to approve that ingestion of data. And then the agent moves it into clearance and populates everything, but a human has to get involved at that step. So you have the ability to effectively dial up those guardrails. You also have the ability to dial them down, because [18:00] it may be you have another portfolio in another part of your business, maybe it’s higher volume, lower premium, maybe it’s renewal risks in a particular region. You might think, “These particular policies that I’m renewing, I’m happy for those to be agentically underwritten.” And a submission comes in for the renewal, as long as it meets all the criteria, it can go all the way through from ingestion, triage, clearance, pricing and rating, peer review, authority, all the way to generating a quote. And you can make a decision about whether that quote goes out without an underwriter looking, or whether the underwriter has to be involved in that workflow. And crucially, the Hyper Operator is effectively us taking the existing HX platform, the existing capabilities we had, and allowing our customers to decide which parts of their portfolios they want to dial up that agentic capability within. 

Matthew: And as you’re talking about that, I’m thinking that all makes sense, but the role of the person who is managing those dials has become really critical. 

[19:00] 

And a lot of underwriters, not all of them, there are quite some very technical ones, but quite a lot come from a kind of relationship-based world. How’s that working? How do you find, with your clients, when you can walk in there and say, “Right, you’ve now got to control what this is doing,” but someone has to literally set those dials? That must be changing, is it, what people have been trained to do, and the roles they’ve got? 

Jamie: It’s a great question. I think five years ago, I’d have been much more nervous about this than I am today. There’s obviously going to be lots of learnings as this becomes a capability that didn’t exist before. But I think over the course of the last five years, we’ve definitely seen, across a lot of insurers, the building out of technical portfolio teams. Because effectively, this is what you’re talking about there, defining those guardrails, that’s in reality portfolio underwriting at that point. You’re saying, “Where do I want my portfolio to go?” And I want to make tweaks to my appetite, or make tweaks to where I want to grow, where I want to shrink. [20:00] 

That’s technical portfolio underwriting. So I think we’ve started to see the rise of those teams more and more over the course of the last five years. And yes, there’s probably lessons to learn, I have no doubt about that, I’ve got lots of thoughts around some of the nuances of the challenges people will face. What will be interesting is underwriters on the ground, this will also, for them, potentially impact how they’re working. For me, the dream scenario is that actually underwriters on the ground are freed up to work on the most complex risks. They’re freed up to apply their judgement, their expertise. We were talking again at the beginning of the podcast around what makes an excellent underwriter, all of those technical, judgement, insight criteria. What you want is for that exceptional underwriter to be freed up from looking at boring admin tasks, or simple risks that are actually worth them going through and saying, “Yes, this renewal is very similar to last year, and yes, I’m happy to go out with a [21:00] 3 percent rate reduction because I want to retain this risk.” That type of decisioning is taking them away from the really complex new business that’s come in, where they’re having to do a full assessment. That’s where you want your expertise to be spent. 

Matthew: And now that you’ve got this entire solution, and you’ve expanded the footprint of what you’re doing with the company, how are you finding insurance companies are buying these days? Because they might already have their own preferred ingestion tool, is it kind of an all-or-nothing solution now, or can people pick and choose and integrate with what you’re offering on a modular basis? 

Jamie: Yeah, it’s a great question. HX, we’ve always been very architecturally flexible. So even when we were just a pricing solution, we had some customers who wanted to use us in particular ways with their policy admin systems or workbenches, and we’ve always made it possible for our customers to integrate in all sorts of different ways. With all of the end-to-end workbench capabilities, as we’ve been building that out, that’s exactly the question you’ve asked has come up time and time again: [22:00] “I’ve already got this capability, can I plug that into HX?” And classically, the answer is yes. The interesting thing, however, is if you are looking for agentic workflows, a system or an agent that can only see one step, or can only manage one or two steps, will be limited to only being able to manage those one or two steps, if that makes sense. So there’s going to be very interesting conversations, I think, across the industry around integration patterns, around how you enable agentic workflows, where actually I’ve currently got point solution one for ingestion, point solution two for triage, point solution three for pricing and rating, and point solution four for managing my document generation. That will, although buying the best of breed is valuable to the extent I’ve got really good solutions for each of those things, it might limit what I’m able to do [23:00] from a full end-to-end agentic workflow. Despite that, we remain very architecturally flexible. We’re very, very pro-integration, because there’s maybe one or two of our customers who come along a year with a pure greenfield site, “We’ve got no existing architecture, we’re moving into a new product space, we’d love to build with HX.” Most of our customers come with, “I’ve already got this policy admin system,” or, “I’ve already got this part of a workbench,” or, “I’ve already got this ingestion tool.” And so we then just work with them to make sure that they can get the full value out of the HX platform. 

Matthew: Yeah, that whole integration piece, we’ve been talking about it for a while, but clearly it’s a big difference for the ability to expand, and no longer those long cycles to replace technology. And then you touched on the US, I spoke to Richard Gunn last year. He’s obviously in the US. He had, I think, 20 people at the time. I know that’s gone from strength to strength, but it’d be great to get an update on what’s happening with your US clients and footprint. 

Jamie: The US side of things has been going really well over the course of the last couple of years. You’ll have heard from Richard a year ago, I think we’ve got to the point of about [24:00] $35 billion of GWP bound on the platform each year in the US. So we’re seeing a lot of growth. That’s coming from two key areas. One is our existing multinational customers, even when we were, in 2017, 2018, 2019, a pricing platform, one or two of our very early customers were actually US domiciled. Moving to the US has allowed us to partner more closely with existing customers. But obviously a large part of the reason to move out there is to start partnering with new insurers, new MGAs, that we wouldn’t have been able to reach on the London market. And so we’ve seen more and more growth in that area, which is really exciting, and it also brings about its own challenges, as you then have to make sure the product works really well for the London market and also really well for the US market as well. 

Matthew: Yeah, it’s great for HX, great for the insurance world, and it’s great for the UK as well, having companies that started in the UK expanding into the US, congratulations on that. What else should I be asking you about? Anything we haven’t covered that you want to talk about? [25:00] 

Jamie: Yeah, there was one additional point that came out of the Underwriting Edge survey, which is this concept of the succession paradox, as we’re terming it within the report. This is the idea, we asked the underwriters in the report, “What’s the one thing you are most concerned about over the course of the coming years?” So flag one thing, what are you most anxious about? The number one future concern was senior judgement leaving the industry before it’s had a chance to be passed on to more junior team members. So this idea of: we’ve got lots of very experienced, very talented underwriters working at senior levels, but how many of them are going to leave the industry through retirement, through exiting the insurance industry, before they’ve had a chance to pass on that domain experience to more junior underwriters? So that was the thing people were most concerned about. The paradox piece comes in when we asked those same [26:00] underwriters, “What are your current company’s top 10 investment priorities?” And at the very end of those investment priorities, ranked 10th, was coaching of junior team members. So you’ve got, effectively, we are investing heavily in technology, heavily in AI, and so on, but the biggest anxiety on the underwriter side was what is effectively loss of expertise, and that’s not being met by the investment itself. So I think that for me was a really interesting finding of the report. And also, my hopefully not naive optimism is that actually AI might even play a role to support that. If you imagine a system where I, as a junior underwriter, am looking at an individual risk, and an AI, or an agentic support setup, is telling me, “By the way, did you know that senior underwriters in your company [27:00] classically look at X, Y, Z, when they have looked at risks like these?” Or, “Your underwriter guidelines are telling you X, Y, Z, A, B, C,” or, “Did you know there’s something very odd about this risk that you might have overlooked?” AI might not be the only thing that solves for that potential loss of talent, but maybe it has a role to play in helping to coach and support junior underwriters. 

Matthew: And I think the other thing that helps in this, and maybe it’s just familiarity bias, in that I’m in this space, but I’m sure I’m hearing more and more from the children of friends and acquaintances, because I’m at that stage where they’re starting to look for jobs, that they’re actually very interested in insurance. And the word has got out that insurance is a good place to go. So even if part of the problem is underwriters who don’t want to leave, there is a sort of, or at least I shouldn’t say pressure, there’s availability of a lot of talent coming into the industry as well, who are of course all very much tied into understanding technology and AI. [28:00]  

Good. Well, Jamie, we’ve covered a lot there. Before I let you go, though, I do need to get your recommendation on a book or a podcast, in your spare time. And given that you spent your spare time a few years ago working on your product, you can have a technical offering, or you can have one that’s got nothing to do with technology or insurance at all. But I’d love to hear your recommendation for a book or a podcast. 

Jamie: I’m looking at my bookcase in front of me right now. I would probably go with Thinking, Fast and Slow. It’s a book that I’ve actually read about, I want to say four times, in reality it’s more like three and a half times. But it’s a fantastic book. I read it for the first time when my career was taking me more towards data science, predictive modelling. And it’s got some really interesting stories, analogies, around the rise of data-driven models augmenting expert decisioning. So some fantastic stories around how [29:00] data scientists were predicting the value and quality of wine years before connoisseurs had had a chance to taste it, for example. So some really interesting examples there. And a lot of it very relevant to if you are trying to deploy data-driven models or deploy technology, what’s the behavioural aspect, or the psychology aspect, associated with people having to adopt that. And every time I read it, I’m like, “That applies to what I was talking to so-and-so about last week,” or, “Three months ago I was working on something that’s exactly like that.” So it’s a book that I’ve come back to time and time again. 

Matthew: Yeah, well, I think it’s absolutely fundamental. Daniel Kahneman was the originator of the term behavioural economics. And the interesting thing is you talk there about data, but it’s actually also, in fact, I just used an example in my familiarity bias, which is sort of what he talks about. It covers so many things, and I sort of cheat, I take notes on my Kindle and then reread the notes, so I haven’t quite read the whole thing, but I do go back to my notes and reread it. I probably should go back and reread the whole thing and get some inspiration. [30:00] 

Great choice, definitely up there with one of my favourite books, and actually a very influential book as well. So, Jamie, that kind of brings us towards the end. We said people could contact you on LinkedIn if they want to know anything. Any other final takeaways for people before we wrap this up? 

Jamie: If you’re in New York in September, we’ve got HX Live happening there. If you want to hear more about some of the things we’ve talked about, exactly as Matthew was saying, feel free to reach out to me on LinkedIn, always happy to grab a coffee and talk a little bit more about technology transformation, whatever the topic. 

Matthew: Right. Well, Jamie, thank you for your support. Thank you to all your colleagues at HX. It’s been great watching you grow over all the years, and you’ve been a big part of the success of InsTech as well, so thank you very much. 

Jamie: Thank you very much.