In this episode of The Connector Podcast recorded at Nordic FinTech Week, Martin, Microsoft’s head of AI for financial services across EMEA, discusses how generative AI (GenAI) is reshaping the banking and insurance sectors. He begins by sharing his personal background in banking and AI strategy, and how he uses Microsoft Copilot daily to summarise inboxes, meetings and reduce administrative overload while travelling. Martin then distinguishes two complementary benefits of GenAI: massive productivity gains (50‑70% time savings on tasks, up to 15% efficiency lift) and a "super‑power" effect that makes workers 40% more effective and enables them to perform at expert level on new tasks (e.g., writing code). He cites industry data showing financial services as the highest GenAI‑adopting sector after telecom/media, and highlights concrete transformations such as consolidating five legacy lending‑system steps into a single chat‑based workflow and deploying AI‑powered avatars for customer service at Commerzbank. Martin outlines three primary drivers of adoption speed and use‑case selection: (1) data and AI maturity (e.g., Morningstar’s rapid launch of a customer‑facing assistant), (2) digital‑process maturity (the need to replace manual or paper‑based processes before AI can be layered on), and (3) corporate strategy (illustrated by Klarna’s demand for near‑zero‑human interaction). He emphasizes Microsoft’s secure, compliant cloud infrastructure, referencing the long‑standing partnership with UBS and the EU‑data‑boundary architecture that satisfies the EU AI Act. The conversation shifts to challenges: a prevailing mindset that seeks "something brand‑new" rather than leveraging proven use cases, and change‑management hurdles around upskilling. Martin notes that 94% of employees want GenAI training but only 5% of employers provide it, leading to widespread "shadow AI" where staff use personal tools (78% of GenAI users) outside the corporate stack, raising security and compliance risks. He also links talent attraction to AI readiness, arguing that the flow of technology from personal to professional life now demands employers invest in AI‑enabled talent. Finally, Martin explains how Microsoft balances its global scale with deep local compliance, maintaining EU‑specific data centers and regulatory relationships to ensure that banks can adopt GenAI safely and legally. The episode closes with a call to stay connected via The Connector platform.
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Welcome to the Connector Podcast, an ongoing conversation connecting fintechs, banks, and regulators worldwide. Join CEO and founder Koen Vanderhoydonk as you learn more about the latest available trends and solutions in the markets.
So welcome to another podcast by the Connector at the Nordic Fintech Week, and today I've got with me a Another special guest live from the conference, Martin from Microsoft. And again, I don't think anyone wants an explanation about the company. Everyone knows.
I hope so.
I guess so. But could you tell us a little bit more about yourself, Martin? Who are you? What brought you here?
So what brought me here is I genuinely like to connect with interesting people, especially interesting people that disagree with me. That's the most fun to have. But the event is just fantastic for just meeting a lot of fintechs, a lot of banks. So that's what brought me here. What brought me into this industry, my background is in banking myself. I had different leadership roles in banking, in retail wealth management, but also digital and AI strategy. And then about 8 years ago, Microsoft asked me to join the company to help build up our Cloud for Financial Services business in Western Europe. And nowadays I'm heading up AI for financial services across the EMEA region.
That's quite a job, I would say.
It's exciting. It keeps me on my toes.
So I guess the first thing I would like to ask, how much are you helped with AI on your job?
A lot, especially when you travel like this. I'm traveling the whole week with different meetings in different places in Europe. The biggest use case I have for GenAI, our Copilot, is every evening I just ask it what's been the 10 most important topics that I need to catch up on that have been in my inbox, in my Teams, whether it's an email or a chat. And it just gives me the list of the 10 topics, what's been discussed. Or I'm missing so many meetings right now, oftentimes double and triple booked. And for me, basically just having the GenAI follow meetings on my behalf when I can't take them, give me a summary of what's been discussed, what are the actions, that just takes so much effort out of reading meeting protocols, let alone having somebody write them. So that's the biggest time saver I have. So I can actually put all of that mundane stuff away and focus on some more interesting and cerebral things.
That makes a lot of sense. And my next question would have been, how does it actually bring benefit to the banks? But you already answered that in a way.
No, no, no, no, no. That's the interesting thing. So yes, productivity is a key topic. No question. And the numbers are staggering. Interestingly, and I mentioned this earlier in the keynote, banks or financial services in general is the single industry that has the highest adoption of GenAI in the world. And at Microsoft, we're fortunate. We, together with OpenAI, have quite a large market share. So I can look at the numbers daily and see the different industries. There's one industry that's bigger, that's telco and media. And there's a few social media companies in that sample, so it's a bit of a biased sample. But apart from that one industry, financial services has the largest usage of GenAI. So that's good. The interesting thing though is for me that the best banks and the best insurers are looking beyond just productivity because it's not just that. The numbers are impressive when you see that between 50% and 70% of time savings for individual tasks across the board, you can raise efficiency by up to 15%. But productivity is one piece. We can do more things. We can do things faster. There are 2 dimensions that are oftentimes missing. It's kind of like squinting and making strategic decisions with 2 eyes almost closed. And one is, if you look deeper into how it's fundamentally reshaping work, it's not just the productivity piece. It's also, in a way, superpowering us. And there have been fantastic studies done. Both have been actually published in the Harvard Business Review. One is looking at giving GenAI assistance to people. and giving them tasks they already do normally. But with the assistant, qualifiably and quantifiably, they become better at what they already know, up to 40% improvement in performance. And that's just amazing. We become better, superpowered at what we do. The other one is—
Interesting.
When it enables us to do things we couldn't do before. It just adds new capabilities. I could suddenly write a computer program and I have no idea how to write a single line of code. But together with a GenAI, I can actually, whether it's in C#, in Python, you name it. So, giving us superpowers is another dimension. And these are oftentimes overlooked because we so zero in on the productivity piece.
I would almost say that this is part of productivity. It's part of it because, yeah, in a way, yeah, I see what you mean. It's 2 different things because you're sort of augmented and you can do new things that you could not do before.
Productivity for me is you do things faster and you do more things. The other one, when we get superpowered, is you're better at what you're already good at. You're going from kind of good to great.
Yeah.
And superpowers is that suddenly with a snap of a finger, and the study is really fantastic, I can share later on, you from the get-go are performing at about 83% of an expert level from a cold start. And those are for me fundamentally different things, also from a talent, from an HR strategy, but also from operations strategy point of view. And when we don't have them in our mind, that's a big, big gap I see. The other one is when you look broader, this is looking deeper in the future of work beyond productivity. When you look broader beyond productivity, it's also that a lot of banks that started their journey, like many do, with let's get the productivity in because there's a huge cost-to-income ratio challenge for every company, they need to become more productive. The second thing they realize is that GenAI helps them to fundamentally reshape their operations, both internally facing and externally towards customers facing. So, I mentioned it earlier that we're working with a bank that's now reshaping the entire lending process.
Mm-hmm.
With GenAI. And the mantra that they have is today it takes about 5 different systems that the advisor needs to go through and then manually bring together. So, that's a process with lots of clicks and 5 different systems. And then they wanna go from 5 systems to one quick chat. And that's a very different way of operating. So, that's the second one. Also, when you think about GenAI-powered avatars now becoming the customer interface for Commerzbank, that's also externally a very different way of operating. And the third one in terms of the broader transformation of the enterprise is that also a number of companies are using GenAI to fundamentally rethink their value proposition, their offering, and their business model. And for me, those two dimensions, the broader transformation of the enterprise, the deeper transformation of the future of work, that's what when you look at an industry like financial services that is embracing it at such a speed and such a scale, that's interesting to see how companies are playing with both of these dimensions.
And would you see there is a lot of difference between the speed that some companies adopt this new change?
Absolutely. And not just the speed, but also what different kind of use cases they adopt. And there's mainly 3 main drivers behind this. The first one is speed and kind of where you jump in, in the shallow end or the deep end of the GenAI pool, if you will. Number one is what is your data and AI maturity. One of the first financial services companies that brought a GenAI solution actually for customers at the customer interface live, so the most challenging thing you can actually do, was Morningstar. Morningstar, biggest platform for financial market insights for investors. They went live with a GenAI assistant for customers called Mo, Morningstar Mo, kind of makes sense.
Yeah.
They started development in mid-March. They went live mid-May. 2 months, but they live and breathe data. Moody's, hot on their heels, but they also live and breathe data. So existing data and AI maturity determines a lot of your speed. If you've done the groundwork over the last years, you might be faster. Second thing is really your maturity in terms of digital processes, because guess what? You can't plug an AI or GenAI into a manual, let alone a paper-based process, and these are still around.
Mm-hmm.
And the third one is your own company strategy. We can't forget that because if you talk to a company like Klarna, for them, it's almost an operational failure if anybody needs to talk to a human. Their technology just needs to work 99.999% of the time.
Mm-hmm.
That's how a startup works. So they're embracing GenAI to do a lot of these things as seamlessly, as real-time as possible. And you heard the CEO go really broad on GenAI and really big on GenAI. If you talk to a private bank or wealth manager, very different. So they want to talk as much as possible to the customer and see— you see more mid-office GenAI applications. So speed and type of GenAI employed really depend on these 3 different dimensions.
I guess one thing they all have in common, and that's around data security and privacy. How does Microsoft deal with that topic?
It's all running in our cloud, and as much as this is a standard answer, our cloud is the most secure and compliant one you can find for financial services because we have done such massive investments in this. And as a point of case, financial services for Microsoft is the single biggest industry in our business. It was one of the industries, actually the industry that started the big cloud journey eight years ago. UBS. big bank in Switzerland and international investment bank, went all in on cloud. They started putting all of their risk management into the cloud, double the performance, 40% less cost. And that really kickstarted so many banks and insurers coming on. And because it's our biggest business, we have so many engineers dedicated to building a platform that's compliant. So the good thing is for us, we don't— it's nothing new in that sense because it runs on a platform that's built for financial services. So security, privacy, it's all built in.
It's by design.
Correct.
Well, looking ahead, what do you see are the biggest challenges for banks when it comes up to leveraging AI?
There are many because it's never easy.
Good answer.
Yes, I would be lying if it's as easy as snapping your finger. There's a whole playbook you need to run through, especially if you want to have it embedded at scale, not just a POC here, POC there. But if I pick 2 ones, It's mindset and adoption change management. Mindset is the biggest one simply because we sometimes think about it completely wrong. As an example, when I look at the use cases everybody's doing and I present them to the different C-levels and boards around the world, I always say, guys, in case you didn't notice, I'm not showing you anything new.
Yeah.
There's nothing new under the sun because as an industry, we've been talking about the same innovation cases for about 10, 15 years. We have no shortage of ideas. We had a shortage of execution. And the reason I believe why we're now seeing financial services is leading and adopting GenAI is like the floodgates opened. We're finally executing all of these. But I still have CEOs come to me and say, tell me something that I can do with GenAI that I never heard of and never thought of before. That's just the wrong mindset. Just do the things you know will generate value for your customers and for your people and that you've known for 10, 15 years. So, and there are many other different Challenges, biases that we have that kind of hindering us in mindset. But that's one category. The other one is said adoption change management. If people don't understand technology, they will use it wrong, and that means a risk for the company. But that also means frustration for them because they don't get the value out that they were expecting. And across industries now, not just financial services, because financial services a bit better, but still, if we look across industries. About 94% of employees say they want to be trained and skilled on using GenAI because they're just really eager to do it. But only 5% of employers are offering skilling at scale for their people. And that's a big problem. And at Microsoft, we always do a Work Trend Index every year looking deeper into how are people working. And this year, the really, to me, shocking finding was that 75% of people are already using AI and GenAI at work across industries. That's a huge number.
That's a very huge number.
A slightly bigger number, but that's the worrying one, is 78%. 78% of those 75 are using their own tools that they bring to work because they're not willing to wait for their employer to bring them. It's basically shadow AI. So for me, that piece in adoption change management, people are using it, but they're not getting trained in it. They're using technology they bring from home. That's one of the big challenges we need to overcome as an industry, and employers need to follow their employees.
Yeah, which brings us back to data privacy. and security. Because if they use tools that are not part of the stack of the bank, there is a risk.
Absolutely so. And so as a bank, you really want to get on top of that because everything else is just going to be a very, very negative experience from a risk and compliance point of view. But also, we were just on a panel talking about the future of talent in the era of AI. You want talented people. And we used to have a pattern where the technology comes from the workplace to the home. That's how we all ended up with a PC in our homes. Now it's going the other way around. Technology comes from the private life into the workplace. Everybody's using GenAI in their private life, and they're not willing to put up with a discrepancy of experience of how to do things when they come into the workplace. Also from a talent perspective, and financial services has not been high in the rankings for attracting top talent anyway. So this is an area where also as an employer, you really want to make sure you invest.
That's an interesting way of looking at it. And I agree with you because in my opinion, the fact that everyone can use ChatGPT or any other tool, it doesn't even matter. It's, it makes it real.
Oh yes.
And because it's real, imagination and fantasy is actually running high. And I think that's the reason why people actually wanna bring it to work because, oh yes, they are incentivized by what they saw, how they can actually do their work better. So no, I see the gap. I see the gap. It's interesting. We're almost at the end of this conversation. And what was on my mind is, well, Microsoft obviously being a global company, but also still a US company. How does Microsoft deal with things like the EU AI Act? How does that work within a large organization?
We're not a global company as we operate globally, but Like here in Denmark, we've got very strong roots in every single country that we operate in. And from a European perspective, for almost a decade now that I'm at Microsoft, and 8 years or so, we had dedicated technology setups for the European Union because it is an area that has its own laws, its own expectations. We have a complete EU data boundary so that all the operations, all the data resides and is being operated on within the European Union boundary.
Mm-hmm.
And we do this in different other areas. And if you even look just at our data center footprint, Making sure that the data can also reside in country. We are building one data center a week, literally. These are massive operations because we want to make sure that it's close to the people and close to the businesses. So yes, we're a globally operating company, but we're also with deep, deep local roots. And we make sure that whichever country, whichever region we're in, we're operating based on those principles. And for the European Union, that's deeply established. We've got very good ties with all the European regulators. Because for us, if people cannot use our technology and can't do so compliantly, there's no value generated. And ultimately, we strive to empower every person, every organization on the planet to achieve more. So that's really what we're driven by.
I think that's a very nice ending. It's very inspirational as well. So, Martin, thank you very much for joining us here. And thank you also very much to the audience. Please stay tuned. Have a nice day.
Thank you.
Bye-bye.
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