Funding Circle CTO on Software Engineers, AI Native Org, £17bn Lending Engine

Speaker A: Your creation of code itself is now becoming commoditized. Right, because the tools can do large parts of that for you. We product engineers, we don't have software engineers. That's an important distinction that we make. The role is to create products to solve problems, not to develop software. Today on the show we're joined by Greg McEwen, Chief Technology Officer, uh, at Funding Circle, the UK's leading SME finance platform, which has extended over £17 billion in credit to more than 125,000 small businesses. In this episode we'll explore Grai's journey from earning a non technical degree in Glasgow to leading technology across multiple companies. What it really means to become an AI native organization, how Funding Circle makes lending decisions in seconds.

Speaker B: When someone clicks a button and they're waiting 5, 10 seconds to get a response, what's going on in that kind of technology stage?

Speaker A: We have owned proprietary decision engine, we're now a ninth generation of that, uh, we have 50, 15 years worth of lending data that we can use to augment that decision making process. Our kind of risk discrimination is about three times better.

Speaker B: Is it constantly evolving?

Speaker A: Our uh, customer interaction every 38 seconds and all of that feeds into our intelligence and enables us to say yes to more businesses.

Speaker B: You got Anthropic and Mythos and the new version which everyone is a bit scared of.

Speaker A: Yeah, I mean it's something that we are keeping a very close eye on. Obviously in the wrong hands could be a real threat, but on the other hand me is thinking I'd really like to get my hands on it and use it.

Speaker B: This is Matthew Chung from work in fintech. And today on the show we're joined by Greg McEwin, chief technology officer at Funding Circle, which is the UK's leading SME finance platform. It's extended over £17 billion in credit to more than 125,000 small businesses. Greg spent his career leading technology at scale, from Paddy Power, Betfair as CTO operating across 100 countries to advising world Remit on payment strategy and now as CTO at Funding Circle. So in this episode we'll get into Greg's early career, how Funding Circle's AI powered credit system makes a lending decision in nine seconds and the biggest opportunities in fintech right now and lots more. So Greg, great to have you on the show.

Speaker A: Fantastic, thanks for having me, M. Matt, really appreciate it. Um, great to be involved.

Speaker B: So I think to start off with, can we go to your journey before we talk about Funding Circle? What's your background? Where did you Grow up, I think you did an interesting degree and things that are quite different to what you're doing now. Can you talk about.

Speaker A: Yeah, exactly. It's not necessarily been a straight path as I think is the case for a lot of people. But um, as you can maybe tell from my accent, I grew up in Scotland, although I've not lived in Scotland for more than half my life now. But I grew up in actually a beautiful place on the west coast in Ayrshire. Um, and for people who don't know Scotland that well it's not far from Glasgow um, so lovely place, um, great area to grow up in. Seaside countryside, um, and all the rest of that. Um, I then moved to Glasgow um, to go to university as a lot of my friends did as well at the time. Um, uh, as you said. Yeah, I've got a degree which is not technical uh in its basis and I think that's probably because at the time frankly I didn't like a lot of people really know exactly what I wanted to do M and people think that's uncommon. So I opted for a course which uh, basically uh, includes some subjects I enjoyed um, and that was the basis of the decision. So things like politics and economics, a bit of psychology and sociology in there. So it was definitely a broad spectrum, um, as you say not directly related to technology. And yet here I am doing what I'm doing today. And I guess my path in uh, was I guess somewhat fortuitous. So I graduated, I got my degree um, and uh, was looking for what should I do next and an opportunity came up, um, as basically a trainee programmer as it was called then. Um, so not exactly a graduate scheme per se but it was you know a company was willing to take me on with you know, no background, no real experience other than you know, demonstrating that I had an aptitude for learning um, and was taken on as a trainee analyst programmer and started to cut my teeth then on you know, developing. Uh, it was an in house technology function in an organization.

Speaker B: And were you quite techie anyway? Were you tinkering with you know, things at home or.

Speaker A: Yeah, a little bit. But I mean I'm probably showing my age now. This was at a point where um, through the majority of the university we weren't actually using uh, a PC that much. Even exams are still all handwritten and things. So I'm definitely aging myself now. But yeah, I was very, very curious about technology always. But actually the aspect of it that interested me the most was actually about the change and transformation that technology can enable. That was always my hook and still today to be honest. Um, and I knew even back then that uh, and obviously the world has changed beyond recognition since then, but I knew even then that technology was going to be a force for good change and what you could do with technology to improve all sorts of things, um, was evident to me even then. And that's why I thought, yeah, that's an avenue from a standing start basically that I want to pursue in some fashion. So that was how I got, got into this route I guess.

Speaker B: And when you, when you then started to kind of move through your career, there's a few different industries and sectors that you've been in. How, how have you kind of learned from those different areas and what have you taken to bring you into what you are in fintech now?

Speaker A: Yeah, I think there's, there's two things that I would say about that that I've learned through that John, me, is that um, I found that there's a lot of commonality uh, from a technology perspective regardless of sector. So whether it's the betting industry, as you said, um, whether it's ah, fintech, the underlying technologies are often similar and certainly the underlying technology challenges are similar. It's great customer experiences, it's scale, it's stability, all those things. Um, and so whilst absolutely the context is different if you're in one sector compared to another or even some organizations versus another within the same sector. And you have to learn that. And it's really, really important to learn that. And I think it's extremely important for anyone working in technology to have a high context about the industry they're working in and what obviously the business they're working in. So it's really important to learn that. But there's actually underlying commonalities across sectors and organizations when it comes to the actual technology and the challenges and opportunities that technology represents.

Speaker B: And so how does um, your remit and your breadth change from when you're a developer, when you started out to being a cto? What's the day in the life of a cto?

Speaker A: Day in life of a cto? I mean I thoroughly enjoy what I do and it's because it's so varied. Um, uh, and you're. Because, because the breadth of, of my responsibility is, is ah, is, is obviously there. So it's everything from at the very top making sure that the technology strategy for funding circle in this case as it is now aligns uh, to the business strategy. Right. So the business has got a very clear plan of what we want to do and how we want to grow technology has got to enable that obviously. And then you've got to demonstrate how your investments in technology are supporting what the business. There's at that top level, there's the overall technology strategy and then the other aspects are the responsibility of the delivery of new products and features. So the customer facing products and features, um, that we provide uh, and the ongoing maintenance and running of them. Data, um, as we'll probably go on to is a huge part of funding Circle's environment and success and ability to operate frankly. Uh, and so that's another big aspect of my remit. Then there's everything that uh, underpins what those products and services and data sit on. So there's a cloud infrastructure, cloud platform and increasingly nowadays, um, there's obviously cybersecurity as well. And so uh, we have a big emphasis on ensuring that we are ready, prepared and protected um, from potential cyber external threat. And then because we're a listed company there's a whole, whole bunch of other things that come with that as well. So we have obviously listed company governance. I'm responsible for, of risk to the board from a technology and data perspective. Um, uh, and so there's a reasonably significant aspect of my role that covers that too. Tying that back to what does that look like on a given day. I could be involved, most likely involved in just about every aspect of that most days. So that's the variety that you get and that's what I really enjoy. And one thing I should mention is also the leadership of the team.

Speaker B: Right?

Speaker A: I mean I think that I put a lot of emphasis on leadership of the team, developing the capabilities within the team and growing um, the team that we have. And I don't mean that by the number of people in the team, I mean the skills and capabilities that we have in the team. Especially in a world like today where you know, there's so much change through AI happening. So you know there's a lot in there obviously and uh, that's what I love about it.

Speaker B: How much time do you dedicate on a daily basis actually following what's going on in technology? Because it's a fast market at the moment.

Speaker A: Do you know what? Uh, there's so much happening at the moment. Um, I spend a reasonable amount of time actually and I've created my own little gems that will uh, literally run on a daily basis with here's the latest news, I get a digest in the morning and I'll spend some time just um, going through that. Uh, that's mainly Focused on AI right now obviously, um, and actually internally, uh, across funding circle, we've got some really good comms channels where everyone is invited to share information about what's happening in uh, fintech, in technology, in AI specifically. So we've got a good kind of heartbeat of um, news updates and sharing going on, which I think is great because especially now, I think the more you can absorb yourself in that to understand it the better. Having said that, I could easily spend my entire day reading all the things that are going out there, which obviously would never work. So you do have to find a balance. But I'm also a big fan of podcasts you train into the office, uh, podcast listening, uh, to what's going on as well. So I do spend a fair amount of time because you have to.

Speaker B: And so something that's in the news. And you mentioned cybersecurity earlier. Obviously you got Anthropic and Mythos and the new, the new version which everyone is a bit scared of to be honest, and quite rightly so. Um, not so much for your funding circle ham, but just more generally and probably you talking to other CTOs and so on. What's the general feeling and approach to something like that?

Speaker A: That we are keeping a very close eye on. Obviously I think they're committed to, um, although they're not allowing access, um, they're committing to report on findings in about 90 days. I think it is, um, and I see it from two perspectives. Um, obviously in the wrong hands that could, based on the information that's been shared, could be a real threat. Um, there's no doubt about that. Ah, but on the other hand, part of me is thinking I'd really like to get my hands on it and use it for our own purposes as well. Um, so there's definitely two sides to it. Um, uh, I think the purpose sounds like a good purpose. Um, but obviously um, with all these things there's some potential risk associated with as well. So watching brief from our perspective, we invest a lot in our cyber, um, controls, uh, uh, and we are always reviewing and continually assessing our landscape on an ongoing basis. This um, obviously even further. What makes you want to kind of sharpen your pencil on that even further. Um, but as I say we invest a lot of effort, time and resource in these things already

Speaker B: because a lot of ah, our audience are uh, people at the start of their careers. Could you talk to DevOps and SecOps and Cybersecurity and how. Actually that's a really big area to be in. It's only going to get bigger as we go forward.

Speaker A: Yeah, absolutely. We are seeing that. As I say, we um, have been on a two and a half year now program, um, of further investment into all those areas that you just described. Because it is becoming strategically, it's not even strategic, it's becoming an imperative. Right, it's just an imperative. Um, and our investment has been on two planes really. I would say we've invested in people skills, capabilities, experience. We've also invested in um, some of the best tooling out there as well. Because I think that's the way um, that you ultimately succeed there. If you can have a high degree of automation in these areas, then that's ultimately how you succeed. Um, again especially as how things are evolving in AI. And so I completely agree with you. This is an area of growth, it's an area of increasing investment for us and for others. Um, and it's an area of more scrutiny as well actually. And so um, from uh, someone early on in their career interested in technology, I think I would encourage them to consider that area. Whereas maybe typically be more inclined to go down the more conventional product development route or something. I would absolutely encourage people to consider that route as well. Because as you say it's growing, it's going to continue to grow and it is just such a fundamental part of um, business these days. Um, so yeah, um, I would keep it on the list of consideration for sure.

Speaker B: Let's talk about Funding Circle. Can you, can you talk about what, what does Funding Circle do? What problems is it solving? Who's it helping?

Speaker A: Yes, as you said at the top, we are the UK's leading finance, uh, platform for SMEs or small medium sized enterprises. And, and basically what we are here to do is to enable small businesses in the UK to get the money that they need to succeed.

Speaker B: Right.

Speaker A: So that's basically our mission and we do that in a number of ways. So we have a whole range of different credit products that we offer to small businesses that satisfy different needs. Um, so uh, for example we've got our, ah, original product that Funding Circle was created, uh, to deliver, which is a term loan product which is kind of larger, uh, loan sizes over 3, 4, 5, 6 year term. Uh, and actually Funding Circle was successful on that single product for about 10 years actually. And it's only recently in the past four years or so where we've diversified that uh, product portfolio, um, in order to help small businesses in more ways. So now we've got for example flexible lines of credit that small businesses can draw down on and pay back in installments, uh, which is interest free but has a fee associated with it that also has a payment card that sits on top for and that's primarily for cash flow management. We know that the SMEs cash flow management is a real problem for them because they're relying on their suppliers paying them and things and having this line of credit really helps them smooth um, their cash flow. We also last year launched um, a business credit card um because again we know that that's a real need m, uh and an underserved market uh, uh as well. So we've launched a business credit card and that's a major part of our growth strategy as well. So yeah, we are here to support small uh, businesses in any way we can through those credit products and we'll continue to evolve that product portfolio to continue to serve the needs that are currently unmet by other lenders.

Speaker B: And in terms of the technology solution to that, uh, is it all technology or is there kind of people involved in some of those processes as well?

Speaker A: Yeah, absolutely. It's a mix uh, and that's by design. What differentiates funding circle uh, is what customers tell us that they want, which is ease and speed. Right. So you can apply for a loan in minutes, uh, you'll get a decision in seconds. Like 70% or more actually of applicants will get an instant decision within seconds.

Speaker B: Right.

Speaker A: And so that's what different differentiates us from maybe some of the more conventional lenders. Obviously that is technology driven. Right. So you've got a very slick application process. You then ah, at the core uh, of our platform we have our AI powered decision uh, models or risk models, uh, which enables us to you know, uh, make ah, an assessment within seconds on that initial application and then come to a decision.

Speaker B: Um,

Speaker A: but to your point we also um, do have um, dedicated account managers for our customers too. So the people aspect of it, um, and you might think well that's slightly unusual to hear from a fintech that prides itself on its technology and its data. But actually again our customers tell us that they really, really value that, that one to one connection with a dedicated person to um, help them through the final stages of the process where needed or to answer their queries along the way. Um, so it's definitely a mixture, um, and that's as I say by design

Speaker B: point in time from when someone clicks a button and they're waiting 5, 10 seconds to get a response. What's going on in that kind of technology state you mentioned about AI doing decision making Credit scoring and things. How much of that, that are you building and how much are you kind of using Best in Class elsewhere as well?

Speaker A: Yeah, well we have our uh, own proprietary decision engine. Uh, we are on our 9th generation of AI risk uh model now as well. So that is the model that we have built in house over the many years that we've been operating. We're now our ninth generation of that and what that does is it pulls in obviously um, uh, publicly available information from company's house and things like that from the credit bureaus. We have the data from there but what we're able to do, which again uh, is a competitive advantage for us is we can augment that with our own data. So we have 15 years worth of uh, lending data ah, uh, from the products and services that we've offered that we can use to augment that decision making process. And what that means is that actually we are able to uh, our kind of risk discrimination is about three times better than if you were just to use a bureau to make that decision because we've got that additional uh, the benefit of that additional, that moat if you like of data that we have that others simply don't. Um, uh, and so that's basically what's happening. But under the covers all of that data is brought in together, uh, uh, risk model, AI per risk models, uh, does the crunching and we'll come out with a decision and that decision is do we lend, do we not lend? Um how much do we lend, uh what price do we lend, over what duration do we lend basically?

Speaker B: And how's that different from say uh, a retail bank that's doing consumer loans on their own websites or something is it? Very. What you've got is very much what you've built with that decision making process. That's the kind of the crown jewels is it?

Speaker A: Yeah, absolutely, absolutely. And it's the interplay between that um, risk decision making capability and the rest of the platform as well. Which means that we can do the get to the end of the process in minutes and actually in many cases funding in the hands of the customer within hours as well. So it's that combination of an uh, in house proprietary originations platform with that uh, risk, uh, the gen 9 risk model sitting underneath it.

Speaker B: Right. And how's that changed? I suppose in like you said the ninth generation before Genai was using machine learning and more traditional approaches. And is it constantly evolving?

Speaker A: Yeah it is, it's evolving um, because there's new data sources that you can add into the model. Um, uh, uh, and as I say, we've got almost this continuous loop where, and this is increasing even more recently because we're getting more and more data. So I Talked about this 15 years worth of data. Uh, but the volume and richness of the data we are getting now is increasing exponentially. And that's because of the nature of our products. So if you imagine when you just had a term loan product that was, you would have very infrequent interactions with our customers actually where we'd apply for a loan, they would get a loan, they'd pay it back on a monthly basis and they might come back in 3, 4, 5 years time for another loan. But now what we've got with the high frequency of a credit card, for example, we've got so much more data and insight coming from that high frequency of engagement of our products. We now have uh, a customer interaction every 38 seconds now. Right. Uh, and all of that feeds into our intelligence and enables us to open up new pockets of lending that we can do to customers. Because one of our key strategic priorities is to say yes to more businesses. And that really enables us to do it. We can open up more pockets of, of risk that we are willing to accept and therefore we can help more businesses through that cycle.

Speaker B: In terms of, um, AI more broadly, uh, if you're kind of, uh, the person on the street, people always see the doomongering side of AI, um, one area of that, uh, is obviously the explainability side. It's not just talking about exactly what you're doing, but more broadly, but that kind of ethical bias piece as well. Because, uh, the data that is in a large language model may have been trained on a data set that M might be inherently biased. You've got your own data set and you already said you're kind of plugging in lots of other things as well. So is that something you're actively making sure that uh, everything is as balanced as possible?

Speaker A: Yeah, absolutely. Within our own, uh, the AI, uh, powered decision risk models that I talked about, we have mechanisms in there to avoid bias, for example. So, uh, we have controls in there where we don't factor in things like gender and other obvious aspects like that that could lead to bias. We also have regular, um, checks, obviously we've got an underwriting team. So as well as all the automated decision making, we clearly have humans in the loop of that process as well. So regular testing of the outcomes of those decisions, uh, and the outcome testing there as well. So that, that's really, really important to Us. And it's actually really important to funding Circle as an organization, as well as having the guardrails within the technology to ensure that we're being responsible and, uh, ensuring there's no bias that creeps in. That's true for us as an organization as well, in terms of how we want our colleagues to be. Uh, and the circles, as we call them, it's kind of ingrained in our culture as well.

Speaker B: So you spoke earlier around, um, technology is actually as a specialism. Ah, as an individual, you're quite horizontal and flexible in terms of different industries you can work in. Because under the hood, when you lift up the bonnet, there's a lot of similarities, irrespective of what the sector is. So there are any, um, opportunities. Obviously everyone's talking about AI, but kind of generally, is there any areas of technology already kind of touched on AI and cybersecurity and so on? Is there some areas of technology where you see lots of opportunities for people looking at it as a career as well? Like if you were kind of going back in time and looking at some of these things again, you'd be like, that's really interesting. I can see that kind of exploding. And it might be broadly and it might be very specific as well. Maybe specific parts within some of the things we've been speaking about. Yeah.

Speaker A: To answer that, it's hard not to go to AI. Ah, it really genuinely is, because I do think that is the, uh. Obviously it takes so many different forms, but I do think that adoption of AI is the kind of biggest opportunity that there is around, um, and probably has been for generations. I know there's a lot of hyperbole about this, but I actually believe that, that, uh. I'm not at all skeptical. I think that it could fundamentally. We're probably already seeing it change, not just the technology landscape, but our lives. I don't feel that's too bold a thing to say, and I know that's not the opinion of everybody, but I believe that that is the case. Um, and it can be applied in any aspect of technology. And we're seeing that right across, uh, all the aspects that you've talked about. We are looking at it from a product engineering perspective as everyone else is. How can you equip, uh, product engineers and uh, product managers to completely reimagine how you create new products for customers and do it in a fraction of the time that you used to be able to do it? Um, uh, as an example, um, and at Funding Circle, we are, um, not only focusing on IT from a technology perspective. But we are looking. We've made a commitment to become an AI Native organization, um, this year. And that's deliberately not AI first, because AI isn't necessarily the right answer for everything. But, ah, that is about, uh, introducing AI into the fabric of how we operate as a business. Right across the business as well. So we are doing fluency programs for everyone, all of our circles, as we call them, right across the organization. Because I think we feel like, and I feel like we have an obligation to help people understand how to use it, uh, and equip them with the skills to be able to use it in whatever role that they are doing across funding circle or outside as well. So, um, I know that's not necessarily the specific areas you may have been looking for, but as I say, it's hard not to go to the impact of AI when you're thinking about opportunities in tech right now.

Speaker B: And how would you define AI Native versus AI First?

Speaker A: Yeah, AI Native is. So AI first to me is, um, that should default. You go to AI for every solution. Uh, and I don't think that's necessarily the case. And I think actually there's a bit of a danger, certainly now, of doing AI for AI's sake. So what it means for us is we'll make conscious decisions as to where and how and when, when we'll deploy AI and what for. Right. I mean, that's what it means. But it also means that, as I said, it's in the fabric of how we operate. Um, uh, as opposed to something that's off on the side that a few people use. Um, so I think that's what Native means for us. If you think about what Native means, if it's your native language, it's natural to you. So there's an essence of that about it. It's just part of how we operate, that we're comfortable with it as being part of how we operate, that people are comfortable using it. Um, and we are doing that in a, ah, responsible, ethical way with the right guardrails, both for, uh, our circle as our employees, but also for our customers as well. So that's the essence of AI Native for me.

Speaker B: Um, and would you see, because your role is CTO and that's a role that's been around forever, you're beginning to see Chief AI officers come up in some firms as well. What would be the difference?

Speaker A: This is an interesting one. We've had a bit of a conversation about this internally. Um, there's maybe a need for Chief AI officers for a while because uh, there's momentum that needs to be found in order to go through the transition and I think something like a focus role like that can help. It's similar to Chief Digital Officer in a way, I suppose in the recent past where it requires, requires that real sharp focus, that real momentum and to get it going. And so I can understand why some organizations go down that route. My view, and actually Funding circle's view is that because we want to be AI native, it has to be embedded in everything we do and almost to have someone's job over there to do that doesn't uh, really fit with our whole um, perspective on AI native. And so clearly I've got a huge role to play in the transformation. But we see it as a funding Circle wide transformation and all of the exec are equally accountable for the AI transformation as uh, are all the other leaders below that as well. So that's how, and obviously as I say, I've got a key role to play in that. So that's how we're approaching it because we feel that's more uh, true to the AI native ambition that we have.

Speaker B: So in this world of AI and uh, the leverage that if you're a developer or a coder, that's been the big area where people are seeing huge returns on investment. Who do you then look for when you're hiring people now given the backdrop of what's going on with AI?

Speaker A: Yeah, it's such a great question and it's changing rapidly. I mean I think what you're getting at ah, is the, your creation of code itself is now becoming commoditized, right, because the tools can do large parts of that for you, but there are still very key skill sets that are required nonetheless. Um, and I think things like um, systems thinking, problem solving, I know that may sound a bit nebulous but actually rather than, I don't see, we call our team like product engineers. We don't have software engineers. Right. And that's an important distinction that we make. And it was true before and it will be true going forward as well. And what that means to me is that the role is to um, create products to solve problems, right? It's not to develop software. And so if you've got that mindset where your role is to identify and solve customer problems through the skill set that you bring, then actually it matters slightly less whether that's done by writing some code yourself or using tooling to create amazing outcomes really quickly. Uh, and to a high degree, high quality, it's just a different way of thinking about what you are already doing. That's how I see it. Right, that's how I see it.

Speaker B: You sound more like the Chief Product Officer than the CTO saying that.

Speaker A: Yeah, yeah, yeah, yeah. I mean, and obviously there's a shift in emphasis and skills that are required that go with that. Obviously. Right. And obviously we still hire for software skills and we still test and assess for software skills, but we also assess for AI fluency in it. We also assess for the extent to which, um, people that we hire have got hands on experience of using agents in the product and software development life cycle. Obviously we obviously do that and there's more emphasis on that upfront definition and understanding of the problem statement and the validation at the other end. And perhaps there is in that middle section of coding. But as I say, the way I see it is if you're motivated by solving customer problems through technology, then that's still the challenge, regardless of the specific, uh, mechanism.

Speaker B: Perhaps if you're a young person starting out now and getting into this, is there a, a core set of tools you would suggest with starting with, in regards to the AI tools out there? Because at the moment Anthropic's having its day in the sun with all the things it's doing and it was chatgpt six months ago. It must be something else in another six months. Where would someone start?

Speaker A: Um, I would advise, um, it actually doesn't matter. And it doesn't matter for the reason that you've just said, right, start somewhere is what I would say. Right. Whether it's um, gemini, whether it's ChatGPT, whether it's Claude, actually just start, it doesn't really matter because I think the most important thing is you become familiar with how these tools work and they've all got their own variations, obviously, and the models that sit underneath them perform differently and perform better for different things, obviously. But that just familiarity of the landscape of the tools that are out there and the fact that they are good for some things and better than others, uh, at different things is a really important thing to learn. So I don't have personally a tool of choice either. So I would say immerse yourself in it. And even if it's in your personal life, because that's kind of a safe way of experimenting and trying out, uh, I'd be a massive advocate of saying to people, just use it in your personal life, get familiar with these tools and also remove the constraints of what you think is possible. I think there's still, uh, a little bit of. We are limiting ourselves by what we believe is actually possible with these tools. And it's changing so rapidly. I mean, obviously even in the last three, four months, especially with Claude Cord specifically, the advancement in the past three to four months has been phenomenal. And if you assume that rate of change continues, then you've got to unleash yourself from the constraints of what's possible in your mind and try it. Just try it. And it's so funny. We did an amazing moment in the team this week where one of the most experienced and highly technically capable people in my team did something with quad code this week and they had a shock, they were shocked. They came to me and said, greg, I've done this and demoed it. I can't even believe I've been able to do that. And it was like three days worth of work. It was phenomenal. It was honestly phenomenal. So that just is proof, ah, to me, that just get involved, just start trying these tools, get familiar with them, get comfortable with them, um, and try a variety of them as well.

Speaker B: To on that point, then if I'm that person trying these tools, discovering something quite amazing, what would impress you? You mentioned some of those kind of softer skills and approaches and problem solving and things, but is there something like if they built that and be like, whoa, that's pretty. How did you do that?

Speaker A: Well, that is the example we had in the team this week.

Speaker B: And I don't want to get into

Speaker A: too much details of what it actually was, but. But let's put it this way, it's something that, uh, it was a working prototype, but it's not a solution. Right. So I think, again, a bit of caveat there, but it was something that has taken other organizations many, many, many years to do. I mean, it's that level of wow. Um, and I say it's not a fully operational system, but we get so much closer in the three days than you would ever dream of. I mean it is in the realms of, of like, what do you need to believe? Right. It really was that phenomenal. So that's impressed me for sure. But also it doesn't have to be the huge things I would say again, with people starting out, um, little incremental automations and improvements are great as well.

Speaker B: Right.

Speaker A: So I wouldn't say to people you have to be shooting for the most amazing things, um, just to start small, uh, and see where it takes you and just, as I say, unshackle yourselves from the art of the possible.

Speaker B: Um, so yeah, There's a lot of bad press about AI taking all the entry level jobs and so on. Can you talk to. Actually probably. And I'm assuming you are thinking the flip side of that is more of an opportunity. Can you kind of talk to that and give comfort to people that might be studying at a moment and think it's going to be tough when they go and it is tough out there, but actually there's a lot of opportunity.

Speaker A: Uh, I have a view which is not necessarily the view that's held by the majority, which is I think the new intake is absolutely critical for the future and we are not far from having the first waves of what I would call AI native graduates coming through who have just, that's uh, all they've known. Right. They've grown up with AI. It's everything that they've known. And tapping into that potential I think is absolutely critical and so exciting. Right. So I'm not in the school of thought which is actually we don't need that anymore. I'm the complete opposite. I think there's a huge opportunity to tap into that. And personally I think you would be missing out on opportunity you can't even imagine yet by not tapping into that population of people coming through. And we're about to see the first cohorts of that for sure. And that's why again, going back to my point, I'm saying use it, get used to it, get familiar with it, um, embed it in what you do. Because I think that is going to provide organizations like Funding Circle huge opportunity

Speaker B: to tap into what's next for you and Funding Circle.

Speaker A: Yeah, I mean we've got big plans, right? We've got huge growth plans. Um, as I said, we have spent the last three, four years building out our ah, portfolio of products and now we are focusing on how do we scale them even further because we talked a little bit earlier. Untapped market, sorry, unserved market. Uh, and so there's huge amount of headroom for us to help small businesses with the products we have today, even nevermind the new ones we're going to build. So scaling our credit card is a key part of that. That um, and so that is obviously, um, got technology underpinning it. Um, so that's a big strategic priority for us. We're also more from a technology perspective, we're investing in our data environment at the moment. So we are upgrading our data platform, uh, and our data environment more broadly as I say, because of that never ending richness of data that we are getting. We need to make sure that our platforms and capabilities are keeping up with that. So that's another key, key, uh, priority for us. Um, clearly this AI native, uh, ambition that we have permeates right across the business. So that's a key priority for us too. Um, and actually um, again from my world, more particularly the cyber resilience. So this is something that is now board level priority. Um, and so that's a big part of what we are doing, um, uh, uh, this year and as you say in the coming years. So there's a lot of transformation stuff that we're doing actually, um, as well as the ongoing continuing to improve customer experience, um, and knitting those products that we have now together into a really compelling lifetime customer experience, uh, for SMEs. So it's really exciting. There's loads, we're doing loads of transformational stuff that we are doing. Um, and I would say this wouldn't have it. It's just that it's a great time to be, to be, it's great stuff to be involved in. So I'm like thoroughly enjoying it, I have to say.

Speaker B: Great stuff. So, so I suppose the last question is if you were to go back to when you were uh, studying when you were 20 years old, would you have done anything differently?

Speaker A: Um, would I have done anything differently? Um, that's a great question. Almost certainly, yeah. I mean almost certainly.

Speaker B: Um,

Speaker A: I think that I would have in my early career pushed myself much further,

Speaker B: uh,

Speaker A: to be more curious. I think that's the one thing.

Speaker B: Right.

Speaker A: If I had to push myself to be much, much more curious and what that would mean, I think for me personally would be involved in many more different sectors, um, and companies than I was in the early days. I think I kind of was guilty of, graduated, got a good job, I was being invested in my skills, my training and my development was being invested in and um, therefore there's no real need to look elsewhere and broaden my horizons and broaden my experience at that stage. So that's something I would do differently. Now I was like, get out there, get involved in lots of different things, experience lots of different things, be curious. Um, that's what I would do differently or more of.

Speaker B: Is there a good way for people to kind of follow you? You on LinkedIn or X?

Speaker A: Yep, LinkedIn would probably be the main one, uh, to where to find me and get in touch with me for sure, yeah.

Speaker B: Okay. Well, Greg McEwen, CTO of Funding Circle, thank you very much for being on the show.

Speaker A: My pleasure. Thanks Matt.