1031. Insights: How OpenAI is shaping the future of financial services

Retail investment in the UK is the lowest in the G7. According to the Bank of England, there is over £280 billion sitting in UK accounts earning no interest. Something has to change. Financial firms in the UK must look at making investing accessible, contextual and trusted through everyday platforms. That means bringing investment journeys to the point of need, alongside spending, saving and budgeting. And within platforms that already play a meaningful role in customer's lives. We dive into this and more in our latest report, taking advantage of the embedding investing opportunity produced in association with Seckel. Download your copy today at alumnifest.com/embedded-investing. Hello and welcome to another killer episode of Fintech Insider, where we cut through all the noise and get straight to the people who are actually shaping the future of finance. AI has certainly been someone of a buzzword in 2025, with everyone from Fintech's like Revolute to global institutions like El Seg adopting it. But aside from who's using it today, we want to drill into the brains behind it and explore what's actually doing to reshape financial services. So without further ado, I'm thrilled to welcome Matt Weaver, head of solutions for Amir at OpenAI to today's show. Thanks for joining us, Matt, how are you doing? Thanks for having me, yeah, I'm doing great, thank you. Awesome, yeah, I'm super excited for our discussion today. So a little bit of background about Matt and what we're going to be talking about today. So Matt leads a specialist team dedicated to helping organizations from universities to start-ups to the world's largest enterprises unlock the full potential of OpenAI's models and products. With a deep background spending, AI engineering and financial services, Matt brings a value-focused hands-on approach to solution design and technical advisory in this rapidly moving space. Under his leadership, Matt's team partners closely with customers to identify their most pressing challenges and develop secure scalable deployments of chat GPT and other OpenAI tech. They collaborate on everything from solutions designed to managing complex security and compliance requirements, ensuring that businesses achieve meaningful results and sustain competitive advantage through AI. So it's quite a critical discussion as we sit here today. OpenAI is announcing major momentum across Europe with new customers including Revolut, Alaka Bank, HG, EQT, and Primera along with a fresh impact data from Zopa and Oak North. So as stated on stage at the FT Global Banking Summit on December 3rd, OpenAI is highlighting how generative AI is fast becoming the operating system for core financial services workflows, from floor direction and investment analysis to lending and customer support. On top of that, Elseg revealed its collaboration with OpenAI, rolling out chat GPT Enterprise to 4,000 employees, and building a market connector platform as MCP that brings its financial data and used directly into chat GPT. So today, Matt and I will explore what all this means. How AI is becoming the operating system for financial services, as financial institutions move beyond chat bots and AI experimentation, how OpenAI is helping Europe's financial sector adopt AI safely and responsibly, and how AI can strengthen Europe's competitiveness in financial services. So let's dive in. And we're going to start, Matt, with just a bit of a dive into your career and what brought you to where you are today at OpenAI? Well, yeah, that's a big question. I guess a lot of things led me here. So I've been doing roles like this one for the last 10 years or so. And that's kind of sitting on the fault line between technology and how that actually gets applied inside of businesses. As you mentioned in the introduction there, my team today is guiding our enterprise customers through understanding the pace of everything that's happening in AI right now, and not only that, how to best apply it in their industry. So I started my career actually in engineering software, like physical engineering. So working with companies like Rolls Royce and Dyson and some of the F1 teams helping them design their physical products. From there, I moved into kind of a more retail space. So working with application monitoring software. So kind of retail websites, making sure they don't crash and stay up in the checkouts all working smoothly. And then I spent the four years before OpenAI working inside a FinTech startup as a classic Silicon Valley early-stage series B company. I was one of the first hires in Europe. And I guess we were in AI company there before everybody was an AI company. This is before ChatGPT, so writing lots of Python code and doing classical machine learning. And the thing we were solving there was extracting unstructured data out of documents, which financial institutions have at huge scale. Imagine if you're submitting documents for a loan application. Someone has to transcribe all that data out of those or doing KYC and onboarding. What was really interesting about that experience for me in that startup before I came to this role was that I had a front row seat to the way that the entire industry was disrupted as this new wave of transformers based AI models, which is a new technology that came out around 2019. And then large language models. And the moment ChatGPT burst onto the scene just over three years ago, that kind of changed everything. And it became clear to me that kind of the best way for me to have an impact and to continue working on these super interesting use cases inside of large enterprises and the financial services industry was to actually come on board at OpenAI. So we've been rapidly scaling the team out here. It's a super eclectic mix of tasks. Some weeks on stage talking about the way our products are used. Other times working closely with security teams to help them kind of unblock and deploy our technology. And really the thing that interests me most about this area. I think that the financial services industry can sometimes have an unfair reputation for being slow to adopt new technology. It's true that this space is regulated and that can sometimes slow things down or cause friction. But actually the opportunity inside of financial services is bigger than almost any other industry. It's one of our fastest growing segments in terms of the customers that we work with at OpenAI. And the enterprises that are doing this really well today, and I'm sure we'll talk about some of them, are seeing huge gains already. And what's most exciting there is that as this technology continues to improve, as the pace of AI continues to develop, we're going to fundamentally transform the way that this entire industry operates. Yeah, it's an exciting place to be in financial services for a change. Because I've also been in the industry for very, very long and I agree there's so many interesting use cases. Your background, I think, just gives you this very interesting helicopter view of how different types of institutions are approaching an adopting AI. Are there any patterns that you've noticed, FinTech versus incumbent government institution versus private as to how they're approaching AI? Yeah, for sure. I would actually say what's been most interesting, and you're right, I get to kind of speak to hundreds of companies at a time around how they're thinking about deploying AI. And I actually would say that the patterns that we see aren't necessarily fragmented by a legacy or newer kind of challenger institution. We see examples such as BBVA, which is like one of the largest banks in Spain and Latin America, adopting our technology. They just rolled out, they're rolling out now a Chatchee PT Enterprise, the secure enterprise grade version of our platform to all employees globally, which is like really exciting. And then at the same time, you know, smaller organizations such as Zopa, kind of a more of a FinTech player, successfully deploying our technology, both Chatchee PT Enterprise, and then also using our APIs to develop ways to increase the quality of their customer service interactions. But the patterns, so it's not really broken down by if you're an older, older, more legacy institution or a newer one. But there are these common patterns that we're seeing of the companies that are doing it really well. I think the components of an AI strategy for a financial institution that really work, you kind of have to tackle it from two angles, right, bottoms up and tops down. That bottoms up component is really about employee AI literacy. We see that, you know, it doesn't matter how brilliantly written your AI strategy is. If it's not kind of just built into the foundation of the way that everyone inside your organization is doing work, if they're not intuitively able to understand the strengths and limitations of AI as it stands today. We often see that you can get stuck, so it's really important that you're just deploying tools like a Chatchee PT Enterprise, so that everyone's familiar and able to use those. And then on the top-stown component, then having a really strong buy-in from the board level of what are the big priorities, the bets that we want to make as an organization to really invest in, truly transforming the way that our business operates. Whether that's, you know, the process of lending out credit, whether that's the way that you interact with your customers through customer service, picking some of those really strategic things. And this is not something which is done on the side. You know, as like a side project, it kind of needs to be core to the overall strategy of the overall business, because there are companies out there that are moving really quickly. And so what we see there is that, you know, when you have that top-stown sponsorship from the senior executive level, those companies are the ones that are making great progress. Yeah, absolutely. And like, what would you say are the key lessons that you've learned, or maybe some of the risks that you see in getting financial institutions to move beyond experimentation? Because I think what you said, though, was key in that, like, it's not about doing this side of justice, about doing this core. So core means in production level, you know, maybe customer facing, maybe not customer facing, but certainly touching critical processes within the bank to actually make a change. How are you seeing institutions sort of push beyond experimentation, or what would you say are some of the principles that you're pushing for to get it more production? Yeah, exactly. And to double-click on that and expand on it, I think I heard from a customer recently that they felt like, in the first half of the year, they were stuck in POC hell, you know, like lots of experiments happening, but not a lot of progress to production. And I think what's really promising is that, you know, in 2025, we have seen these organizations move from POC to production for the first time. So your question is a good one, which is like, how are people doing that? What is it that's breaking beyond the POC stage? And one of the first things, AI literacy is important. Executives and leaders actually leading from the front in their own personal use of AI is another key one, just to around overall adoption. And when we talk about going to production, there's, I guess there's two categories, right? Employee productivity overall as a topic, whether that's software developers using tools like OpenAI has codex agent to accelerate their workflows or the use of a tool like chat GPT, connected into all enterprise data, you know, that those are things that you can go live really quickly with because they're internal. And so, like the risk is lower, you're really just trying to accelerate people's productivity. When you're building externally facing experiences, for example, Revolut, one of our customers have built their Rita assistant, the Genai Assistant on GPT-5, which is used in kind of customer support queries for end customers. So, how on earth do you do that and a regulated environment? How can you take an AI model and expose it to your customers? Well, it's actually the good news here is that there is a solution and that is to build the right guardrails and evils. I'll just unpack those two terms because in regulated industries, it's true in financial services, it's true in healthcare and life sciences and pharmaceuticals, you really need the ability to kind of measure the performance of the solution. And when you have a chatbot, you know, it's not like one plus one equals two, there's kind of an open natural language conversation, but an eval, it's basically a test, and you can write a few thousand of these that says, hey, based on the user's input request, what kind of response do we expect the model to return? And you can build those in an automated way. And a lot of the time that we spend my team and the technical teams at OpenAI who partner closely with these large enterprises, we can provide expert advice, guidance often will come in and like show you how to do this directly. Which means that you can actually then measure with the percentage accuracy, the performance of the solution and this enables you to go live with confidence, but also satisfy the regulator that the solution that you've built is incredibly performance. And so you'll often any advice anytime you speak to an open AI engineer giving your advice on this topic, they'll say start with evils. We often spend, you know, the first three months of a project writing the tests before we even start building the agent, the agent, because if you don't have the tests written up front, you can't possibly know how you're tracking as you're building that solution out, you can have to have that as the as the foundation. And then having guard rails in place, you know, sometimes guard rails could be something simple, like a calculation, if I'm extracting data from someone's paystub to validate their income for a loan, I can add the numbers up and check that they compute correctly. So not everything has to be an AI LLM request, it could just be a traditional kind of calculation or something that's more what we call deterministic, you can just calculate it with logic. And then the other really powerful component of guard rails is often will have one large language model, check the work of a previous one. In many critical banking processes today, you may have a code maker checker process, right, if you're doing very large transactions, often one person will do the work in a second person will check the work of the first person before that transaction goes through. And then we can do the same thing inside an LLM solution. So, you know, you might say, here's the output that we produce that we're thinking of sending that to the user. Here's the inputs we gave and then you have the LLM kind of double check all of that work, and this enables you to maximize the accuracy, put the right guard rails in place. So these are just two examples, e-vows and guard rails of techniques that you have to look kind of layer on top of the AI model itself to make it really robust and reliable inside those production environments. And we see this, you know, BBVA also, you know, a bank that I mentioned them earlier, they have an AI assistant built into their mobile banking app called blue. And blue can query your transaction history, you can actually set up payments to send money to a friend using this AI chatbot. And that's only possible because they've done a really amazing job at building these guard rails and the e-vows into the end to end development process, which takes them from POC all the way to production. That makes total sense. And I guess those e-vows and guard rails are also then how they can manage their internal audit and risk reporting reporting to the regulator and all that. It's just amazing to see how an LLM can in some ways mirror an internal audit process. You would have somebody checking this anyway. So instead of somebody, you can have another LLM doing that, which is super interesting. You say that. Yeah, exactly. And I think sometimes like a mistake that's easy to make is to think, well, I've just got this model, and I'm going to ask the model a question, and it has one shot to get it right. And if it makes a single mistake, then the whole thing isn't going to work. None of our internal processes today work like that. Often for important decisions or for critical processes, you have a few people collaborating together to check each other's work and make sure that everything's really high quality. And we can create that same construct inside of these agentic AI applications. And this is where we partner deeply to give that advice and work closely with customers to kind of guide them through that process. Let's talk a little bit about BBVO because I think it's actually very interesting. You know, you're doing the chatbot, but I understand there's a whole bunch of stuff you're planning with them. Just tell us a little bit about that. Yeah, really exciting. We recently announced this new strategic phase of our partnership together. I spent a lot of time traveling back and forth to Madrid this year working closely with them on this. And I think what's exciting there and the scale of their ambition is about deploying chat, ebt enterprise to all employees. They really see this as kind of the super assistant that's going to assist everybody to be more productive. Some important things there, you know, chat, ebt enterprise from a business context, we never train on any of the data. It's a secure enterprise grade version of the tool. You can also connect it into your enterprise systems, right? So think about connecting into SharePoint, connecting into Google Drive. You know, actually adding the intelligence of your entire organization into a tool like chat, ebt, and not just relying on kind of searching the web. So you can have a really like enterprise context enriched experience. But then beyond that, you know, whether that's across customer service and kind of the external facing applications. Like this blue assistant in the mobile banking app, which they built where we're going to enhance that much further. But then also internal processes when you think about how to empower bankers who are very often interfacing with clients, helping them do account research, provide the best possible advice, preparing for meetings, following up after meetings when they have those external ones. So that's like a key component. Back office operations, I mentioned much earlier in my career, I was working on kind of automating all the paper that flows through a bank. And there's lots of things we're going to be doing with BBVA there to streamline some of those back office processes, which can ultimately just shorten approval timelines, shorten client onboarding, give a much better experience for BBVA's customers. And then we're working with them deeply on kind of expanding how their software developers can be productive. I think many banks today, and this probably resonates with many of your listeners, banks kind of are software companies. In some ways, there's a lot of internal systems to build and maintain. There's also a lot of legacy knocking around, you know, banks were adopting technical systems back in the 70s, 80s, 90s. And so anyone who's worked with an IBM mainframe and some of the legacy cobalt code there will know that it's a pretty challenging project to migrate a lot of that code. And so we're supporting them through some of those things as well. And then finally, you know, in this kind of multi-disciplinary approach across the entire business, the other one is around managing risk. You know, how can you actually use AI to make better risk decisions? Because ultimately, many financial institutions, the business is about offering credit, which helps to stimulate small businesses by lending them money to stimulate the overall economy. So there's a lot of good that can be done by shortening credit approval timelines and actually making higher quality risk-based decisions, which is good for the bank's own balance sheet and a book of risk as well. Yeah, there's just so much value potential there, isn't there? I'm just curious to know from your perspective, if there are any surprises maybe since you started and these sort of surprising successes or failures or things that, you know, were kind of an assumption that maybe obvious about how people adopt AI versus what you've seen. I'm just curious to know if there's anything there. Yeah, absolutely. I think 18 months ago, also when I joined OpenAI, there was still a narrative around AI being quite expensive, like an extra cost. And that was true actually at the time. I'll give you one of my favorite statistics. So when we first launched GPT-4, if you were to consume it via our AI, it would cost around $60 per million tokens, doesn't really matter what tokens mean in this context, but about $60 per million something. Today, for the same level of intelligence for our smartest model, GPT-5, it's 99% cheaper. It's just a few cents for the same amount of intelligence. And that trend has been pretty consistent over the last couple of years of the cost of intelligence falling as the models themselves get even smarter. And what that means in practice for business leaders is that often, you know, people will be in a planning period now for how 2022 is going to unfold and where investments are going to be made. And so even if a use case looks a little bit out of reach from a cost perspective, if AI is like maybe not quite cheap enough today to make that viable. I recommend like still start building and start experimenting because in six months from now, in nine months from now, the cost will come down and all of a sudden a whole new wave of use cases becomes accessible. So like the cost coming down like to me was surprising. I don't think many didn't expect it and some still have this idea that AI is really expensive, but the cost has just been falling dramatically. Whilst at the same time, the pace of model intelligence, increasing is accelerating. We launched GPT-5 back in August. A month ago, we launched GPT-5.1 last, and then, you know, just one month later, we launched GPT-5.2. So, you know, you're going from like two or three months between model releases to one month between model releases. And I think you can expect that to accelerate even more into next year. And these are trends that it's kind of hard for our brains to kind of grasp this exponential. But we've seen this now continuing every period of a couple of years, and you can kind of bet on that moving forward as you plan your investments into next year. I think that's really fascinating. There's also something in there about the kind of long tail legacy of, shall we say, slightly older models and how those get a hell of a lot cheaper over time. Because one of the things that I've picked up on as well is that intelligence in many ways is very important. But actually, there are a lot of back-offers processes where maybe you could argue that the existing models today or maybe some of the older models are kind of okay. And so if that price sort of just drops so, so much, then the business cases to implement those things across some of the back-offers processes just become a lot clearer as well. I mean, it's a horrible analogy, but it's a little bit like you can have, I don't know, like an iPhone, but also you can use a feature phone for some things as well. And people still sell feature phones, and people still buy feature phones. There might be something about the tech that in the fullness of time that remains. It's not always about the kind of the thing that is approaching AGI or AGI, but it might be about these other things as well that kind of came along up along the way. Yeah. And at OpenAI, we provide like a full suite of models. I think most people are familiar with the ones that they see inside of ChatGPT that people are using every day. And you know, we now have over 800 million people every week using ChatGPT in the consumer space, which is like 10% of the world's population, so that for many people, AGI is ChatGPT. But in the background, we have, you know, GPT-5, GPT-5 Mini, GPT-5 Nano. So we launch these kind of like smaller, cheaper, faster models because many use cases, you know, they don't need that frontier level of intelligence. We now have, you know, over a million business customers who are building on our technology and millions of developers worldwide, kind of building their own AI experiences on top. And many of them are using kind of some of the smaller models for those faster use cases, where it makes sense. Yeah, that's awesome. All right. On that note, that wraps up part one. So in part one, we laid the situation. So what's going on right now? We talked about Matt's background. We talked about how AI is being adopted across Europe, and we sat the stage for the future. So we're going to shift it into what's next for the sector coming right up. Hey folks, if you're anything like us, you're on the road constantly. Airports, hotels, conference centres, half the time the Wi-Fi feels as safe as shouting, my pin is 1, 2, 3, 4 across the departure lounge. That's where NordVPN comes in. One click or no clicks with AutoConnect. And suddenly every sketchy airport network becomes a whole lot safer. You still get blazing fast speeds and 8,700 servers in 129 countries. So you can switch your virtual location while you're waiting for yet another delayed flight. One account covers 10 devices, and you get Threat Protection Pro to block malicious links and ads, plus dark web monitor if your details show up where they shouldn't. We've teamed up with them to offer Fintech Insider fans an exclusive deal. Head to nordvpn.com/fintechinsider to get a huge discount and four extra months on top. Completely risk-free with their 30-day money back guarantee. Links in the description. Welcome back. Now in this section, we're going to focus on AI's future in financial services and how banks, Fintechs and insurers could be leveraging it strategically into the future. So to kick this off, I'd like to talk a little bit about the concept of AI becoming an operating system. Leveling up a little bit beyond that, we're using it as a tool for a productivity or a tool for a specific use case, but into that broader view of how do you operate a financial institution around this new technology? This is a really great question. And I think I was actually speaking to a very senior banker from one of the world's biggest banks yesterday about this exact question. Imagine you were to start a bank today from scratch with all the technology that's available. How might you design the entire organization to work differently? I think for many years there have been some processes that have been incredibly manual that has created a slow and user experience for customers, has created a lot of cost and the way that those processes operate. So in terms of thinking, how can we actually redesign an organization and then for many companies, given that they already exist, transition into that new way of working? The first thing is we were kind of discussing in part one is just around continuing to bet on this trend of continued intelligence increasing and cost decreasing. So what does that look like in practice? We're seeing, if I take the example of private equity, we've spoken a bit about banks before we look at the private equity space. And a previously analyst research was a limiting factor in terms of the speed and quality of decision making that could be made around investments. And now we're seeing that become totally unblocked. It's not just about one person using a tool to explore some ideas or run a research query. You could actually systematically and automatically repeat research in response to changing global macroeconomic conditions. Think about when tariffs change, which happened a lot this year. How could you reevaluate your entire portfolio in a matter of minutes to understand how these new policy or global changes actually impact the balance of risk and opportunity in a portfolio and perhaps inform where you might take action next. So I think one of the key things that I'm spending time advising global financial institutions to do is to kind of reimagine a process from scratch, if you just take the inputs and the outputs that you're trying to achieve and almost forget what exists today. How might you redesign that entire process from scratch and in some of these cases around research, you can kind of see how there are certain triggers in the news events which then actually go and run research across an entire portfolio of investments. We're seeing across many private equity firms as well some of the highest rates of adoption of kind of chat GPT which you know itself as a tool for enterprise productivity. But over time, we're now starting to add not just read actions for doing research, but also write actions, you know, the ability for an agent on your behalf to go and actually start taking actions inside of some of your other enterprise systems. And then finally, you know, another area, I think when we're seeing a total reimagining of the way that business currently operates is around the way that customers and consumers get to interact with and experience many of these financial institutions as brands. I hope that the days are almost longer and where I have to sit on hold for 30 minutes and punch the numbers into the key pair to try and get to speak to the right person and actually the opportunity to have a 24/7 always on access to kind of the information that I need at my fingertips. And customer service isn't always just about like solving a problem that's gone wrong or getting help with a specific request. Sometimes that can also be proactive. You know, how can I look at my changing financial circumstances and then proactively receive guidance tips or advice unique to me that actually transform my relationship with my bank versus today which can often be quite reactive. What's interesting about that is that we we see and there is a statistic out there you guys will probably have it that so many people a large sort of double digit percentage of people are already using chat if you see for, you know, financial advice they're doing it themselves are just doing it themselves. You know, there's people uploading their entire transaction history and be like, tell me how to do better or like, you know, tell me what stocks I should be investing and all of this kind of stuff and and banks and financial institutions are quite rightly far behind that. But to your point, you can already see that some of these initial deployments of intelligence and insights like what you're doing with BBVA are already kind of showing you where this is going like the trend line is all of these very complex personalization insights all of this kind of stuff can in the future be sort of provided by by anybody. You know, if you were a re-architecting a bank from the start, you would think about how you provide that personalization from the beginning to the end. So it's just so curious to see when you work in the sector whenever you see a drop where a fintech or a bank does a thing that it just edges things slightly forward into into that kind of a gentick world. You kind of wonder, okay, what's next? What is the next implementation of this? Because the way I'm already using chat GPT is like 10 times further ahead. So how long is it going to take for the kind of the risk culture in these organizations to move there? It's so fascinating. Yeah, I think we are seeing I felt a shift in the last kind of six months or so of these institutions really seeing the size of the opportunity and taking a step forwards and I'll give you another example kind of an internal one where we're seeing transformation in the way that business happens today. So the London Stock Exchange Group, you know, they they they provide data to many financial institutions that helps them to make great decisions to understand kind of current market prices to get access to market news. They recently launched a brand new connector or an app that's available inside of of chat GPT fair and a prices, which means that now if I'm an investment banker and I want to get a spot price on a particular commodity or I want to understand the spread on a particular currency, then actually I can do that in a conversational manner directly inside of chat GPT. And so I think we're probably also going to start to see, you know, there's this new way that users are going to interact with with your institution, you know, this entire new this entire new surface, which is which is Genai conversational chat box like chat GPT. You're going to start to bring in more data from external sources and for the user, that's great. It means that they can from one single pane of glass, get the insights that they need. And for enterprises, I think the way to think about that shift is just happening and we've already seen it in search like in the retail space now many people are moving from traditional search engines to a Genai led search experience because they can actually investigate further. I think we're going to start to see a similar level of disruption happening, you know, across banking financial services, financial data. And you know, the really innovative companies are kind of riding that wave and being some of the first in the market, such as Elsec to offer their data embedded directly inside those experiences. Well, as you mentioned, the Elsec example actually, because I was going to ask you like how important are MCPs, model context protocol to to all of this. And maybe actually maybe start by giving us a bit of a definition for our audience around like what what is an MCP? Yeah, this is a good question and it's always good to zoom out and take a second. I'll go even one step further back, which is. It was actually only around just over a year ago that these kind of AI chat pots started to get the ability to search the web. So some of you may remember, maybe two years ago, you'd ask a question and the large language model would just answer it based on the data it was trained on, but it couldn't really do current affairs. It couldn't really do news because it couldn't search. And so we gave the large language models access to this tool of a web search that it could then use. So every time you ask a question, you know, if it was about current affairs or something recent, then you know, chat GBT could go and search the web and find the most relevant content and answers. So, you know, the web is one valuable source of information that these models can get access to, but especially in an enterprise context, you know, there's so much data that's critical to the way that a Fintech or a financial services company operates. There's not on the web, right? It's where the market data provider, it's internal to your own systems, perhaps, you know, not something as simple as SharePoint could even be an old system from from the AT's that you need to connect to. And so, MCP or model context protocol, you can kind of think of it for those who are familiar with like an API, you know, the way that traditionally software is able to interact with other pieces of software. And MCP is like a little server, which runs like a modern API to really simplify it and it enables AI tools like chat GBT to actually go and retrieve data from other systems. So the short answer to your first question is how, you know, how critical are they going to be? I think the answer is very critical. We see the value that people get out of tools like chat GBT exponentially increase when you start connecting it into the data that you actually use to drive your business decisions, whether you connect that to your data lake, you know, your CRN, your customer database, or, you know, to other data from other trusted for party providers. All of a sudden now, you know, the questions that you ask the answers you'll receive will be enriched by all this high quality data, which, you know, forget AI for a second, how do you actually normally make decisions in your business? It's usually by searching across all that internal data, not just doing your job based on what you found on the web. And so I think that's a trend, the adoption of these MCP connectors, the ability to get that data is going to accelerate throughout 2026. Yeah, amazing. No, it'll be really interesting. Really interesting to see that. I want to just change tack a little bit and talk about the financial sector in Europe. So your role is looking across Europe, I'm helping, you know, boost the financial sector by integrating open AI. How does this boost competitiveness, would you say, in Europe? Because it's quite a hot topic at the moment for, you know, I don't know, like there's a lot of regulation going on at the moment. There's a lot of competition between the US and Europe to try to attract and foster innovation. Yeah, it's a good question. And I think one that's, you know, the European competitiveness topic has been, has been litigated in many places. I think what I'm hearing from customers today, you know, when I speak with net West in a UK or or revolute or when we're engaging with, you know, more specialist banks, you've got like Alaka Bank and Oak North, they were working with small and medium sized enterprises. I'm seeing innovation everywhere. I think Europe actually can sometimes, you know, people can be a bit down on down on us and the way that regulation gets in the way. And it's true. You know, compliance with regulation does take time. It does take resources and attention. But there are many examples. Alongside BVGA, Santander, also in Spain, you know, European financial services, regulated enterprise rolling out a charity to more than 15,000 employees deploying AI and customer facing scenarios. So I often challenge business leaders. Sometimes I do get the objection, you know, well, we'd love to do all that, but we're not a tech company. We're a bank and therefore, you know, it can't be done for these reasons. I think there's plenty of examples now that actually where there's a world as a way. And of course, you have to do that in partnership with the regulator, keep them in the loop, provide the right data through the e-vows that we were discussing earlier, whether you evaluate the solution performance. But I think Europe has an opportunity here through adopting AI to dramatically increase its competitiveness in the financial services industry. You know, I also was chatting with the US colleague yesterday, you know, I said, like, who is the revolute in the US? You know, where is, where is the the challenger, the challenger bank of the US? And it's just the ecosystem that looks slightly different. There are some so much larger players who kind of dominate. But you know, I think we can hold that up here in the UK. We have Monzo, you know, there are other kind of challenger banks that have done really well. And so I think it's exciting to see that trend has the potential to continue and hopefully the way that Europe chooses to regulate moving forwards continues to stimulate and how it's innovation and not stifle it. But I have high hopes for that. Yeah, I think you're right. There are a lot more present and close, very successful fintechs here, the big, big banks and big institutions can look to. And I hope, I hope and maybe it's a question as well. How have those objections been evolving over time? Because I absolutely like maybe a year or two years ago, the objections were exactly as you said, just, you know, no, how are you going to do this? Not possible or that kind of stuff. What are the typical types of objections you're getting? Or is it actually a question of, no, my god, we need this and how on earth are we going to do this? It's the how more than the what? I think there is a how I think sometimes you know that there is a the regulatory landscape can can look a little overwhelming. So, you know, sometimes it can be a bit scary to get started. But once you start working through it, actually, there are great answers there. At OpenAI, we offer full data residency within Europe where we now recently announced UK data residency dedicated as well. So you can ensure that your data will be stored encrypted at rest, you know, within Europe. And we also then see, you know, there's a to flip it around a little bit. There's a huge opportunity for regulated industries to use AI to increase compliance with regulation. You know, net West governs AI through an internal code of conduct, which ensures privacy and transparency and then uses it to assist in mitigating financial crime. You know, revenue also have a fin crime agent that they're using to try and detect and block financial crime from happening. So I think, you know, whilst adversaries, you know, bad actors out there in the world are using AI today to try and get past the defenses that many of these financial institutions have built up. There's also a huge opportunity to use them to improve the compliance posture of your organization. If you embed AI into those workflows that today take time, if you think about KYC and client onboarding, you know, trying to do the necessary research to validate that someone is who they say they are. You can actually then be applying AI to accelerate and improve that process, which gives you a high quality decision and actually improves the end customer experience. Yeah. And you know, this is also something that I say to some of the objections around compliance is, no, you're not going to be able to audit how the neurons fire to give you the result, but you can ask it what it did in it will tell you. And if you ask a human to ask you what it did, you ask a human, you can ask, you can ask the LLM 10,000 times, 100,000 times. And it's that sort of scale of feedback answer response that I think is just so important for his organization. Yeah, exactly. And I think right now, you know, pointing to all these examples that we've been discussing of institutions that have like what's worked through the regulatory requirements and actually now deployed at scale inside the globally inside their organizations, including in Europe, I think is, you know, just is the light that leads the way for other institutions to follow in their footsteps. Yeah, great. So we've been talking about it like lots of future use cases. One of the areas, maybe we haven't talked about so much as insurance guys will might be good to talk about that, because insurance is so interesting, right, like how you quantify and measure risk and how you pay out that there's like a lot of very interesting use cases that I've seen in in US and Silicon Valley is very interesting, like verticalized businesses that are doing kind of end to end all the way from the kind of the phone call through to the claims processing. Are you seeing anything in an insurance that's interesting for you? Yeah, absolutely. And from my time before OpenAI, you know, when I was working on that document processing startup, you know, there's so much paper when you think about claims, think about underwriting in reinsurance when you're kind of analyzing and working with reinsurance contracts, you know, we have a huge reinsurance industry here in here in the UK through the London markets. And so the opportunity there for automation is enormous. You know, we recently announced we're working closely with the AA, you know, a lighthouse brand here in the UK and who offer insurance to their customers and they're taking kind of a multi disciplinary approach to the use of AI through empowering employees with with Chatchy PT, but then also solving many internal and back office processes and streamlining those using our models. So I think insurance is an area which there's a huge opportunity for disruption, especially because there's a use case that I found particularly interesting. When in commercial insurance, when an insurance broker is trying to gather quotes on behalf of their client, they build this PDF presentation and they send it rounds to, you know, 10 insurers and say, hey, can you give me a quote on this on this, you know, thing I'm trying to ensure. It's not like each of these companies are doing something similar, you know, with different is literally the same document being fired off to 10 different places. And so I think there's a huge opportunity there, you know, if you could just automate that process with AI to take that unstructured data to all the information about the the asset you're trying to underwrite and then automate that process with AI, it then becomes all about. You know, just gathering the context information during research and then being able to make a fast decision. Okay, so in the last little bit, what I want to do is I just want to kind of put it into a drive a little bit and talk about like the future future or that they're there particularly when it comes to. Human orientation or human replacement because I think a lot of the discussion that we've been having on these use cases is yeah, how do you. How do you maybe he'd be human in the loop and how do you automate those processes you can kind of see where this is going does the human leave that loop eventually like what what what do you think about that like do you think it will replace roles in our in our sector or not. So I think you know there's a lot of worthy discuss worthwhile discussion around jobs when it comes to AI I actually think maybe the framing needs to be thought about a bit a little bit differently, though, which is the AI AI isn't automating jobs is automating tasks. And our jobs are composed of tasks that is true. But what we're consistently seeing across the industry is that you know there are some tasks which are repetitive manual time consuming which once automated actually free up those people to take on much higher value much more strategic work. A nice example of this is software development, which has been one of the earliest kind of job roles to be massively disrupted by AI. We've gone from a place you know a year ago where you know AI was being used to assist software code creation to now where you know at open AI 80% of all the code that we ship is written by an AI you know managed managed by a software developer. And what's happened through that transition you might say well okay do we need less software developers. The answer is no actually it turns out we were massively constrained by the by the number of developer software developers that were available. And so with our ability to create incredibly high quality robust production grade code. When I'm just writing lots more software you know that the roadmap of things that you planned for your technical teams that might have taken a year previously you can now deliver in a single quarter. And so we see organizations just pulling forward things on their roadmap that they couldn't previously do because they've actually unlocked the potential of of these really valuable workers by automating the tasks which were kind of more manual and more repetitive and letting them the software developers now spend their time. Working on the kind of the more high value really interesting work and we see this happening across other roles as well. If you take the example of an underwriter in insurance you know how much time does an underwriter spend reading and analyzing risk contracts doing research transcribing data for between systems to try and like compose. Like a quote based on the risk models that they're using turnily when really that you know the value of an underwriter is not their ability to copy and paste data between systems. It's to make a decision using their expertise and experience about the risk that's being underwritten. And so you just dramatically increase the the amount of work that that one of those people can do. And I think over time and this is true even in my own team I think we will raise out the bar our expectation of what one person is capable of doing. A similar example might be you know when we move from having calculators to the computer in mathematics you know the kind of the kind of test that you would set for students at school. You know you just make it much harder you kind of you set them a bigger task because with the available tools at their fingertips that people can just do more. I'm really excited for that means across the financial services industry as this value starts to be unlocked. I think people we will always find more interesting more valuable things to do and especially once we clear some of the kind of more repetitive work and tasks that actually creates a lot of capacity for us to work on. Yeah, much more valuable tasks and people can actually spend more time thinking and making decisions as you say is what people would people want to do. All right, that's amazing. Thank you, Matt on that note that wraps up today's discussion a big thank you to a special guest Matt. Tell us where can people connect and find more about you. Yeah, so thanks for the conversation and we'd love to have a conversation if you're thinking about your own AI strategy and how you might transform your own organization with AI. You can contact us via our website at OpenAI.com. There's a talk to sales form of course embedded within our sales team or all of our technical experts and engineers as well. So we'll be here to give you advice and if you want to learn more about kind of how to up level the AI literacy within your own organization. You can get to academy.openai.com. We've got loads of free resources there. There's a whole section just about using AI at work to help you and your team members kind of really up skill and make sure you're getting the most out of what's possible with this technology. Awesome. Yeah, go and check that out everyone and you can find me on LinkedIn. Thanks for listening. If you like what you've heard, follow our podcast and don't forget to leave us through a view. It helps us to make it better and helps others find the show. As always, if you want to join the conversation, find us on social media. Just search for 11FS with Fintech inside our email podcast at 11FS.com. Thanks very much and goodbye. Through 2025, we saw brands from every corner of financial services take their user experiences to the next level from personalization and investments to AI chat bots and crypto end users are more empowered than ever when it comes to managing their money. And we expect that trend to continue through 2026. 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