In this episode of The Connector Podcast recorded at Swiss WealthTech Live, host Koen Vanderhoydonk interviews Roy Kirby, Head of Core Product at SIX Financial Information. Kirby outlines SIX Group’s four business lines—exchange, security services, banking services, and financial information—highlighting the global data services they provide, including price, reference, corporate actions, and ESG data. He discusses the biggest challenge in wealth tech: catering to younger, tech‑savvy investors who demand faster, high‑quality information. Kirby describes SIX’s sustainability initiatives, both in launching climate data products for clients and embedding ESG practices internally. The conversation then shifts to AI, where SIX has built a private‑cloud large language model interface (akin to ChatGPT) that speeds up market‑data queries, saving up to 40 seconds per request for non‑expert users. Building on this, SIX is commercialising an AI‑driven regulatory‑risk tool, RegRisk, that scans regulatory documents and flags relevant items, moving from internal use to a market product within weeks. Kirby offers advice to fintech startups: focus on core strengths and clear use‑cases. He closes by inviting listeners to connect via LinkedIn. The episode showcases SIX’s blend of data infrastructure, sustainability, AI innovation, and market‑focused product development.
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Welcome to the Connector Podcast, an ongoing conversation connecting fintechs, banks, and regulators worldwide. Join CEO and founder Coen van der Hooijdonk as you learn more about the latest available trends and solutions in the markets.
Welcome to another podcast episode today streaming from Swiss Wealth Tech Live, and I've got with me Roy Kirby. Head of Core Product Six Financial Information. Welcome. Who are you and what do you do?
Hi there, and thanks for the invite to talk to you today. So my name, as you said, is Roy Kirby. I work for Six Financial Information, and I'm a product manager. I head our core products, and I look after things like price data for financial instruments. I look after reference data for financial instruments and corporate actions for financial instruments. So we run a global business here at SIX. I'm here at the conference today to talk about what we've done with AI, what we've learned so far. Interesting.
And can you tell us a little bit more about the SIX Group as well?
I can, yes. So the SIX Group is— it's got 4 business lines. It's probably most well known for running its exchange business. So So we run here in Switzerland, Swiss Stock Exchange. In Spain, we run the BME Exchange, and we run a digital exchange as well. As well as the exchange group, we also have another group which does security services. So things like custodian services. We also have a banking services team who do a lot in the payment space. So we're well known in the payment space here in Switzerland and across Europe, and I work in the financial information part of the business where it does what it says on the tin. We provide financial information globally, be that what I look after or things like ESG data as well.
Interesting. If I may, what, what do you see as being the biggest challenges today within your space?
So I think the biggest challenge is, and we were talking about it a bit earlier at the conference today, is particularly in wealth tech here. You look at the way that the wealth is shifting from older generations to younger generations. Those younger generations use very modern tools, very modern software tools, very modern processes, and they want their investments and their wealth profile looked after by the banks that we serve. to change and to be updated so that they're really targeted at that newer, younger, more tech-savvy end user. So some of those things that used to be on paper, used to be quite hard to do, should be on a new technology on your device with quicker interaction. People want information More quickly, more readily available, same quality as we've always had, but more readily available to answer more questions because they have a lot more questions than the previous generation had.
And oil to the engine is data.
Exactly. That's it. Yeah. Yeah.
And in the data space itself, you mentioned sustainability. I mean, a lot of challenges today, not only in Europe, but the rest of the world. So how are you coping with these challenges? I was talking to one of your colleagues earlier. And I mentioned analogies like baking a bread. You're responsible to bake a bread, and you know that people that eat it can have allergies, but you don't necessarily know what is in the ingredients. So it's a very tough job.
It is. And I think if you look at that, so sustainability, what are we doing? We're obviously creating data products, so data products around sustainability. So for example, we've just launched some new climate data. that can be distributed to our end customers. So there's the data element of it, which is obviously important. But look, we also look internally at sustainability as well. So things like making sure we're a sustainable company in the way that we deal with employees, but also in the way that we do things like traveling for events. So for example, this event here today, nobody has flown to this event from Six, you know, so we, we have the data, we're producing the data, but we're also trying to live this model internally as well. Because I think culturally there's a bit of a cultural shift that organizations need to have as well. So we're trying to embed that in a lot of our, um, our staff.
That's nice. You earlier also said Sixt has different elements, different departments. Uh, one of them is very well known as Sixt Digital. And then the obvious word comes, it's blockchain. So is it literally using emerging technology or is there any other implication or applications that are being used within the Sixt Group or within your own domain even?
Yeah. So technology-wise, I was talking at the conference earlier and I was talking about what we've done with AI, so artificial intelligence, and particularly around what we've done with large language models. Interesting. And what we see there is large language models, and probably the most famous one that people will know is something called ChatGPT. Is how do you take that and how do you get the efficiencies, um, in your organization that a tool like that brings, but without making your data exposed globally to everybody? Because if you use ChatGPT on your Google search engine, you'll notice that actually where the data's come from is hidden from you. And that's because a lot of people want to hide where their data comes from. We don't want to hide where our data comes from because we're a quality data source. But how can we internally in our own private cloud with our own private networks bolt on a tool like ChatGPT to expose our data in a nice, easy to use, friendly to use way gives people efficiencies. So if I can, I, I showed a, a small demo earlier, which very simple— somebody looking for the 5 biggest gainers of a stock exchange in the last 5 years. Now you can do that on what's called a market data terminal. You can do that, somebody can do that. We timed our expert user of that terminal. He took about a minute to be able to do that, to find the top 5.
Which is not bad, by the way.
Which is not bad, by the way, which is quite good. We put ChatGPT onto the front of that interface. It took 10 seconds to do exactly the same thing.
So for a non-expert user?
For a non-expert user, I saved 40 seconds. You, you multiply that 40 seconds up by how many times you do that sort of query, and particularly here for the wealth industry, Instead of focusing on those routine tasks that you need to do and taking 40 seconds or a minute, they take 10 seconds and then you can focus on what you should be doing. And what you should be doing is having that relationship with your end customer because that's where your value is. So it's how do you use those tools to make that sort of thing happen is what we're looking at at the moment.
It is a market trend at the moment that a lot of us do RPA. But it seems that now indeed with ChatGPT, the next level of RPAs are coming where AI is used to also do those little things as well. So it makes sense.
Yeah. And what we're not saying— what we're saying is still there is a human involved. The human makes the decision. The machine does not make the decision. The human makes the decision. What the machine can do is expose to you all of the information quickly and efficiently so that you can make that decision. And we all know that in routine tasks, we sometimes skip a step. A machine doesn't skip a step. It always gives you all of the information that it can find. You skip the step because you don't think it's important, but sometimes it is important. So it's always nice to have that on the screen in front of you.
Yeah, I guess it's that whole discussion, like, who is most right? Because also humans make errors. But I think if you have a second pair of eyes, if I may say, it also sort of allows the hallucination not to occur to the end client. Because at the end of the day, today the models still hallucinate, which can be tricky anyway. Well, since we are in a financial environment and SIGS being at the core of that in many, many different ways, We cannot avoid to talk about regulation. So how does your team, how does the group actually navigate within this complex regulatory environment?
Well, very interesting question. Actually links into the one that you've just asked me. Internally here at Sixt in financial information, we have a team that looks at regulatory documents. So they are subject matter experts. They've had years and years in that world, but they were getting overwhelmed with the amount of regulatory documents that are out there. So what they did was they built themselves a tool using AI to actually search through these documents for them.
Sweet.
Flag things that they should look at. And they've used that internally now for about a year. And what we're doing in just 18 days' time is releasing that as a product to sell to our customers. So we're going to call it our RegRisk tool. So regulatory risk tool. But it's basically an AI-based document engine that allows you to, again, scan more regulation, focus your subject matter experts on the regulation that impacts you, take away some of the grunt work, some of the boring work. But yeah, we've used it internally as a tool and we're going to launch it externally now.
Well, congratulations. It's really exciting.
Yeah, it is. Yeah, it is. Thank you.
Especially coming from a market infrastructure, saying that you are so quick with go-to-market. It's, it's brilliant. And maybe that brings me to my last question. Um, I mean, you are a market— you are a market infrastructure and FMI. What would you give as advice to a new entrance to the financial industry, a startup if you want?
Um, yeah, good question. What, what advice would I give? I, I would— I'd probably give the same to any, any company. I would say know your strengths. What is it that you're really good at? What is it that you bring to, um, the financial markets if you're a fintech? But what is it you're really good at? And not is it just what you're really good at, but what is the end use case that you're solving for? Again, there's a, there's a room full of about 100 people there at the WealthTech conference. They're interested about solving problems and use cases. So how do you help with that? That's The most important piece of advice I could give.
Thank you so much. Roy, last question. I'm sure people want to continue this conversation. How do they come to you? How do they come to your team?
So probably the easiest way is I think most people use LinkedIn. So contact us over LinkedIn. My name's Roy Kirby. So search for Roy Kirby and Six or Six Group. Ping us on LinkedIn. We're always interested to meet new interesting people.
Thank you so much. And thank you also to the audience for staying tuned. And we're going to get more live from this event. So stay tuned. Thank you very much. Bye-bye.
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