Governing AI in Financial Services: Inside the FCA with Jessica Rusu, Chief Data, Information & Intelligence Officer

0:00As a data scientist, I could give you my opinion as to what I think responsible data science looks like, but it's more than just the data or the tech. It's an end-to-end application that will be integrated into the financial institution. It could be back office, it could be operational, it could be front office facing, it could be consumer facing. So there is no one-size-fits-all definition of what responsible AI looks like.

0:31Welcome back to Data & AI Mastery. Today, we're doing something a little different. As you know, I'm passionate about speaking with leaders who are shaping the future of data and AI and learning how they're navigating the harder questions that come with it. Today, we're going inside the Financial Conduct Authority, the FCA, to sit down with Jessica Russo, Chief Data Information and Intelligence Officer, one of the most influential figures shaping how AI is governed in financial services.

1:04There's a version of the AI story that gets told at conferences. Responsible adoption, governance frameworks, ethics by design. It sounds right, it looks polished, and some is generally true. But today I wanted to get underneath that, to speak with someone who doesn't just hear what firms say about the AI programs. She sees what they're actually doing in practice. The Financial Conduct Authority regulates the conduct of over 50 ,000 firms across UK financial services, from global banks to early-stage fintechs. Its mandate is to protect consumers, keep markets clean, and ensure the UK remains a competitive place to do business.

1:44In practice, that means the FCA sits at one of the most consequential intersections in the economy right now, the point where the pace of AI innovation meets the need for accountability. The FCS position on AI is deliberate, no new AI-specific rules, but a clear expectation that firms embed responsibility into their existing governance. A principles-based approach in a technology environment that changes every few months.

2:15Hey Jessica, how are you? I'm great, how are you? Amazing, it's so good to see you today. I'm very excited to have you on the podcast.

2:26Jessica, to kick us off. Yes. You had a fascinating career. You know, you worked at Ford, at GE Capital, eBay. Now you're the Chief Data Information Intelligence Officer at EFCA. You know, we all have a book with multiple chapters. So I'd love to hear from you, you know, a bit about your story. And is there any common thread throughout your career? Yeah, thank you. So I've had a really interesting and diverse career spanning, I guess, both sides of the pond and international as well. I started in the U.S., as you mentioned, at Ford Motor Company. I started in technology. I moved around through engineering and managing vehicle delivery programs.

3:07And then I got really excited about tech. From there, I pivoted into what used to be called analytics. analytics. Then it evolved into data science. And that was how I really got my passion for data and tech. And I think that that background, just moving around and then becoming an expat, having the opportunity to work in California, e-commerce, big tech, fintech, you know, this gives you a real diversity of experience and I suppose has influenced who I've become today. I love it. I love how you have such a technical background, also super diverse, working for companies in different places, in different industries.

3:47So how has that equipped you for the role that you are in today as Chief Data Information and Intelligence Officer at the FCA? I think, firstly, as an executive, your primary job is strategy. So especially now, as we think about the role that technology is playing in transforming not just financial services, but every industry, so much of that and how you react to it, you have to think both internally, how is my organization positioned to best leverage tech or best leverage data? And then how does this shape our reason? When I say our, I mean our company's reason for existence in this economy.

4:35And that is the job of every leader. And the experiences that I have had, whether it's from marketing, product, consumer facing, finance, you know, what drives growth, you know, what drives volumes, what drives velocity, thinking about all of these questions through a tech and data lens is really exciting. And particularly now, it's just kind of table stakes for how you go at pace, especially during AI and as we see everything moving just so quickly. Yeah, that's really interesting. And, you know, I guess, how did the role come about in 2021? And what was the mandate? So it's really interesting because one of the things that I found as my career was progressing at eBay and PayPal, and I spent a lot of time in California and in the Bay Area, a new role was taking shape at that point in time, the chief data officer.

5:42And so my career had progressed naturally through different data science and advanced analytics roles. I was intrigued by the opportunity to be a chief data officer. So I went and became a chief data officer at a fintech because that was really intriguing. It was the opportunity to use AI in consumer lending, actually. And that was right at the cutting edge of data science at that point in time. I think where a lot of roles potentially fail or where there can be challenges, especially if your mission is to digitally transform whatever organization you're responsible for, you always hit up against some silos.

6:26So there might be a part of the organization that is the, maybe the CIO or the chief tech officer, or you might have a CISO or a chief data officer. And all of those things need to come together strategically in order to transform an organization. So I give a lot of credit to Nikhil Rathi, my boss, the CEO of the FCA. He created this idea that for the FCA to be the most digitally forward regulator in the world, pro-innovation, pro-tech, we needed this role. And that is why it led to this situation where I have the chief data information and intelligence officer because of that vision of bringing everything together to transform the FCA.

7:15Yeah, I really love this vision. Instead of isolating the different functions, you bring it together to kind of like supercharge the transformation, the one leadership. That's super cool. I also find it fascinating that you worked in a fintech before because you now see kind of the two sides of the coins. Has that kind of shaped the way you think and support organizations in the finance world? I think there are tremendous innovations and real cutting edge stuff happening in the fintech industry. And that's why it's so exciting that part of my job is to lead the innovation services as well. So most of my day job with my CIO hat on is maybe internal digital transformation, right?

8:00But externally, you have the whole market, you know, leveraging technology and coming up with new ways to offer financial services. So having had the opportunity to work in a fintech gives me that unique perspective into what are the challenges if you're a fintech? How do you grow your business? How do you become a unicorn, for example? How do you scale? And, you know, just solving the customer pain points, what you do with data, what you do with tech. So I think that's really added another string to my bow, and I'm really grateful for that time.

8:37So you mentioned the FCA Innovation Services. Yes. Could you tell us more about it? So the Innovation Services have been around for 10 years. What is most commonly understood about them is this idea of a sandbox. And where we have evolved that model over time is two kind of offerings. One is a regulatory sandbox and one is a digital sandbox. And I'll explain the two. So the idea of the regulatory sandbox is a place where we can work on policy initiatives before they harden. And stakeholders from the industry can come in and maybe give us example disclosures or work through what it would mean to work with that policy before we finalize the policy.

9:26So essentially testing it, testing it, being an agile regulator. So that is the regulatory sandbox. And that continues to be a really important tool for us, particularly, let's say, for example, with stablecoin policy or crypto policy or all of these emerging areas of financial policy that we need to work through. It's really important, right? So that is the non-technical space. The technical space, the digital sandbox has evolved over time. It's now always on. We built it in partnership with NIA1. We further extended it last year to include technical support from NVIDIA. We continue to have both NIA1 and NVIDIA supporting that technical environment.

10:16And what that does is it's a unique place for innovators, fintechs, to come in and leverage all of the synthetic data sets that we have. And those are a real competitive advantage for firms because we have fraud typologies. We have market data. We have transaction data. We have 300 to 400 different really important data sets. So the firms can come in and work with that, and they also get coaching and development through a sprint. So, for example, if we have a technical sprint focused on a specific subject area, for example, mortgages or SME finance, they can come in and participate in the sandbox for a three-month sprint.

11:03They actually develop their products and services whilst they're working with us. And the feedback that we get is that is a game changer for UK fintechs because you get not only the sort of technical leg up. What I hear is that it would have taken me a year to do this on my own, but you've sped up my development lifecycle to three months. So that's great. That's great to hear. They also get to work with a regulator and have that kind of oversight happen. They'll learn about us. We learn about them. And that might help them whether they need to go on their VC journey. Maybe they decide to get authorized.

11:45So all of those kind of fringe benefits come with the sandbox as well. Wow. It sounds like real cutting edge stuff, right? And it's so cool to see the impact into reducing the timeline to get your products out there and test it out. and so on. I guess it'd be great to understand, you know, what are the key ingredients to make, you know, this initiative, the sandbox kind of happen? Because I'm thinking, in principle, that could be useful to other organizations, not in fintech, you know, like to offer this sort of innovation sandbox available for other startups in the ecosystem. That sounds super cool.

12:21So I'm wondering, you know, if you look back, were there like key ingredients that made the initiative really successful? So I think collaboration is the point that's really important. We often hear chief execs of large firms talking about shared problems. For example, it could be that criminals are able to use 2D images to create fake accounts. Okay. So this is a problem not faced by one entity, but multiple entities. And they all have a slightly different set of data. They might be able to share that data with each other. But sometimes these bilateral sharing agreements can be hard to broker and sort of, you know, to arrange that situation.

13:11So when there's a center of gravity, like having a sandbox, you have that opportunity to say, yes, I'd really like to participate in that. And I'll be willing to share my data sets with you, perhaps anonymize, or we might turn it into a synthetic data set. So I think that the collaboration is key. I think information sharing is key. The synthetic data assets have been a real game changer for not just us, but for all of the firms that participate and utilize that information. And I think just on the policy side, it continues to be an interesting way to make legislative change at pace. So, for example, switching on and off rules or testing different policies live.

14:04Yeah. Yeah, super cool. Great to hear the ingredients that, you know, may be applicable to other industries, to be honest. And so nice to see the FCA, you know, acting as a real ecosystem builder and facilitating collaborations. I hope you're enjoying today's conversation. If you're finding the insights useful, please do take a moment to subscribe to the Data and AI Mastery podcast and leave us a review on Apple Podcasts, Spotify, or YouTube. Every new follow helps us reach more people and shed incredible work being done by today's Data and AI leader. All right, let's go back to the episode. I'd love to take your take now on, you know, we've got this sandbox environment, super cool to test, you know, new initiatives and so on.

14:45So I guess the bigger picture is scaling AI responsibly. A lot of fintechs are then thinking about, I want to use AI to maybe reimagine the customer experience and operations and so on. But obviously, you want to do that in a responsible way. So I feel like scaling AI responsibly is being thrown around a lot at the moment in the industry. I'd love to get your take. How do you think about it? And maybe the counter would be, what does it mean to do it badly? I think that's a great question. What does it mean to do it badly? So as a practitioner, I would say maybe as a technologist, as a data scientist, I and many others might have an opinion about what is the best way to approach a product delivery.

15:28And there's lots of frameworks out there about how to do it effectively. And I can talk a little bit about how we're doing it internally in a minute. But externally, what we see is that firm needs when it comes to AI delivery come in different shapes and sizes. So we have tried to be as flexible as possible to meet whatever those needs are. So it's not all fintechs. I described already the AI lab services that we have. So we've built on top of our digital sandbox this AI lab that gives AI spotlight opportunities for firms to showcase types of AI tech that are working well and what they're solving for.

16:15We have specific sprints focused on different types of AI, for example, agentic use cases or chatbots or vulnerable customers. So we have developed all of these offerings where firms can come and essentially use our tech or use our services. But not all firms necessarily need to engage with the FCA, and they don't necessarily have to come into the sandbox. But what we decided to offer was this concept called AI live testing. What that means is our teams are going along as an innovator and from a supervisory standpoint to work side by side with firms as they are doing live product testing in the market.

17:03So these could be large banks that maybe they want to deploy a chatbot. Maybe they want to give consumer advice. Maybe they want to use some tool internally or externally. And they don't need to come in the sandbox, but they do want to showcase how they are thinking about AI governance. And so the typical questions that you might expect, you know, how have you selected data for this model? Why did you choose the model that you chose? What sort of testing have you done to make sure that, you know, as consumers are going to interact with this, you know, what guardrails have you put around that service or that product?

17:53So we sort of ask all of the questions that you would expect to see. But again, from a guidance and a best practice sharing standpoint, not as a here's a tick box approach to deploying AI, because we know that every application is unique. And we know that technology is moving so quickly that what might have been a good model six weeks ago might not be a good model six weeks from now. So it's really about how robust has the firm considered this product delivery? What guardrails do they have around it? And how able are they to respond to that? So that's the AI live testing initiative. And we have just announced the second cohort of firms that will be kind of going through that experience.

18:44Yeah, amazing. so it sounds like you know spending a good amount of time thinking about the assumption the lineage the guardrails around the output how's that used and be able to evidence it i do wonder is there like a uh a moment where all right we've done a good job here you know it's responsible or is that a an ongoing journey you know as as model gets better and better that you know companies you are going through? Like when can we answer the question, we're using air responsibly and we feel good about it? So from a policy perspective, we continue to leverage the consumer duty. The consumer duty essentially says you have to offer fair value to consumers.

19:30You have to make sure that the products and services they receive meet their needs, that they have flexibility and choice. We look at things like complaints volumes and vulnerable customers as well. How are they being treated and do consumers have access? We call it sometimes consumer financial inclusion. So are there certain groups of customers that are somehow being excluded from financial services? So all of those things mean that by saying that the rules haven't changed, we feel that that is actually a stronger starting point rather than a weaker starting point. So in some places, we've had some feedback, oh, you should make some rules for what responsible AI looks like.

20:17And as I said, as a data scientist, I could give you my opinion as to what I think responsible data science looks like. But it's more than just the data or the tech. It's an end-to-end application that will be integrated into the financial institution. It could be back office. It could be operational. It could be front office facing. It could be consumer facing. So there is no one-size-fits-all definition of what responsible AI looks like. But if you think about some extreme examples, they're already covered by legislation and policy. So let's say, for example, a firm decides that they just want to cut out an entire department and replace it with some agentic workforce.

21:03They could do that. But have they thought through the operational resiliency, you know, responsibilities? What would happen under the senior manager's regime? Who's accountable if all of that agentic workforce were not able to show up for work one day because there was an outage or a systems issue? So as they think about deploying technology wherever they deploy it, the same rules of the game apply, meaning there's no get out of jail free card just because you use AI. You cannot say, oh, it was a black box and it was too complicated. If it's your firm, you have to understand how it works and you have to understand the impact on customers all of the time.

21:50And so we actually feel that that's a more responsible AI place to be versus having some sort of, okay, if you do these 10 things, then you've ticked the box. Got it, got it. That's fascinating because it sounds like there's definitely a balance in offering the flexibility so that firms can think through how they want to implement internally, but also take an outcomes-focused approach. So I really like that. But also what I'm hearing from you, Jessica, is there seems to be a clearly focus on what's the first order or second order of the decisions that you make. Like, you know, for example, replacing with an agentic workforce, what's the consequence of that for operational resilience?

22:30Thinking to that level, I think, is super interesting. Yeah. Which I guess leads me to the next question, right? From your experience, what are you seeing maybe firms underestimating when it comes to, you know, adoption of new technology? And obviously, AI is like a big one. So I think some firms, not all of them, so some firms are understanding that AI is an incredible opportunity for them to reshape their corporate strategy. And they are starting from the top down and thinking about that very strategically. For example, some firms are actually saying our competitive advantage is that we're going to have humans answering the phone instead of all of my competitors shifting to having a agentic workforce or an AI operation interacting with consumers.

23:22So those firms that are thinking strategically, I think that's good. And as a technologist, anyone who says, okay, I've got a tech solution that's shopping for a problem, we see that a lot in tech. So we see a lot of, oh, here's a really great software or here's a really great tool or here's a really great product. And then you try to shoehorn it into the organization without really thinking end to end, how do I want to transform this? Sometimes we call it consumer-centered design. um internally for us we're thinking very much through an ai product delivery lens so we've stood up an ai factory or an ai product delivery team within our our product officer function and we want that to be very much not focused on delivering faster horses so you can take a process and you can just automate it and make it faster, or you can reimagine the whole process.

24:25So we're thinking about that for our authorizations processes, our supervision, for our enforcement and intelligence operations. So everywhere we have data-intensive, neural networks, workflows, actions that need to be taken, we're looking at those opportunities. But we're trying to avoid falling in the trap of, oh, just faster horses or just automate that. Because we can automate things, but we can also reimagine them. Yeah, no, I really love that. It sounds like, you know, on one end, you can use AI as a tool for incremental improvements. On the other end, back to corporate strategy, rethink your value proposition and double down on what you think makes you special as an organization and just reimagine the way things are done.

25:14So that's super cool. Now, if I take you to the fintech world, right, you know there's a lot of surveys around adoption of ai both at the at the firm level the consumer level i'd love to hear from you you know what do you think is accelerating ai adoption and maybe in some places what's maybe hindering it within the fintech landscape so i think the opportunity to automate aspects of your workforce is an area of extreme interest for fintech founders in particular because as they try to scale, you often hear the founder will say, you know, not only am I the CEO, but I'm also the CIO and the CPO and the CTO and the CMO, you know.

26:00And so they have all of these hats that they wear. And so we have seen agentic workforces coming into the supercharged sandbox like Anitech, for example. We've also seen Kestrel and then MySerene and different examples of firms looking at how can I make the speed of either identification of risks or the handling of complex tasks more reliable and more resilient. So we see that happening at pace for fintech. And it might not be that visible to consumers because they just see the innovation on the end. They don't necessarily see the means to the end. That's great. And say I'm a CEO of a mid-market type of organization.

26:52Any advice you'd give that CEO when they think about adopting AI and making things happen? Because I hear a lot of leaders, they're all excited about AI, but they're kind of struggling to see meaningful improvement or meaningful ROI. So is that like one advice you'd give them? So maybe taking my regulator hat off and just putting my CIO hat on. When you are responsible for running a large infrastructure, you have systems, you have a cloud infrastructure, you've got networks, you've maybe got some SaaS software running across your organization. You have all sorts of things. And most likely what you are experiencing is every single supplier that you have is adding some sort of AI feature.

27:49So you have this AI creep happening, which is essentially all of this new tech and services and products coming into your infrastructure. And not only that, but you're also paying for it. So you might see your bills for digital services and products going up, and there's not much you can do about it because you might already have vendor lock-in, you might already have things that you're responsible for. That's why I think it's so important to think strategically about where do I want my firm to be in five years' time? And what do I need to do about my infrastructure to get from here to there? It's kind of a classic digital transformation question.

28:34Those of us that have been running really, really fast to digitally transform organizations, we sort of have to start over. So I'm sorry, kind of bad news for all CIOs out there. so so I've been in my post for five years I'm coming up to my five-year anniversary it would be great to stand on top of a mountain and shout you know this is wonderful we have digitally transformed the FCA in in five years we now have you know insights at our fingertips we're cloud native you know we've got this you know agile architecture you know we've got you know hooray hey, we've succeeded. Well, actually, the goalposts just shifted.

29:20So everything that we have achieved in the last five years and that organizations have done with their infrastructure and architecture needs to be entirely reblueprinted, right? And so I think what that means is it's an exciting time because you get to rethink it. But for firms that are saying, well, I'm not seeing any ROI yet. Well, there is ROI. You know, you've got basic things like co-pilot. You've got, you know, probably your staff is already using automatic meeting transcription or maybe are able to search and find documents and utilize them faster. So I think those things are happening.