The Connector Podcast - Datavillage

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.

Welcome to this new podcast from the Connector, and it's the first one in 2023. And I have a special guest with me, one of our new customers, Frédéric. Data Village, what is it about?

Yeah, nice to meet you, Koen.

Hello.

So first, thank you very much for the opportunity. So I'm Frédéric, co-founder of Data Village, and my main experience is in the financial industry. I've worked in security, architecture, technology innovation, you know, open banking, all these funny things. We created Data Village with Quentin, my co-founder, with the initial idea that if a financial institution or any organization could access more sensitive data or personal data without having to manage it, copy it, and giving complete transparency to consumers, it would really open new opportunities.

And because how do financial institutions currently share the data with each other or not?

They do, of course. And today financial organizations share the data either through API, so typically open banking, you know, with fintechs, but also with even more basic means such as transferring files with other financial institution regulations. And the main limitation in that way to share data lies in the fact that the data are transferred and copied.

Mm-hmm.

So this leads to limitation in opening more data because, you know, the fear of losing competitive advantage when sharing these sensitive data outside of the financial institution. But also, it's also linked to the risk of non-compliance with data regulations. So typically GDPR. So both leads to complex ad hoc mechanisms from financial institutions, such as trying to anonymize data, adding also too much friction.

Very cumbersome.

Exactly. In the user experience, you know, with easy consent management, that's one of the main drawbacks of open banking.

So banks today, they won't put their crown jewels up for grabs. How can you then improve data collaboration and how can you make it effective and efficient for financial institutions? Because there's a lot to gain from sharing.

Yeah, exactly. So you talk about data collaboration and that's important. That's exactly what can improve efficiency. So unlike data sharing, which transfers data from one entity to another, data collaboration is different. It's about allowing several entities, could be organizations but also consumers, to combine their data and execute algorithms for a common purpose in a central place. So it's not about transferring this data or copying this data. That's the main difference.

I'm just a simple person. So what you're saying, Frédéric, is that you have 2 complete different datasets, you bring them together, you don't jeopardize any of the privacy, but you can still run analytics on both of them.

That's exactly the point. That's what I mean by an algorithm. Could be any type of algorithm. And the most important is that these data and algorithm remain fully confidential in that environment. Wow.

And is this something that is new? Is it something that already exists for many years, or it's something that you literally invented?

So we don't invent it. So data collaboration is now clearly a trend in the market. So there are a lot of— you know, organization, but also data platform that is talking, that are talking more and more about data collaboration. And on the other hand, technology such as confidential computing, these ones are new. Clearly they are new. I mean, depends what do you mean by new, but it's not that it exists 4 or 5 years ago.

So that's the tech part.

That's the tech part. And this is exactly thanks to that technology that we are able to achieve that goal, meaning keeping things fully confidential and trusted and secure.

And I guess everyone in the audience is very keen maybe to hear some examples if you can. Can you share some of the live examples where data is shared and you got magical results?

Yeah, sure. There's so many examples. I would say there are endless opportunities. But basically, you have 2 types of— 2 categories of data collaboration. There is one related to what we mean aggregated data, meaning allowing particular the search for insights such as collaborative fraud detection, ESG risk assessment, or benchmarking. This is typically about aggregating data from multiple sources and getting insights.

So trends is your friend.

Exactly. Exactly. It's a good, good, good word. Yeah, exactly. Another category involves the consumers. It's a bit more complex, but the opportunities are much bigger when you involve consumers in that journey and in that collaboration journey. And it allows new hyper-personalized products Such as automated product-level cashback based on receipt data from the retailers. So you cross this receipt data with the financial transaction, making sure that both data, meaning the receipt data and the financial transaction, remain fully confidential for everyone in that collaboration. There are other examples such as—

Maybe before you continue, because it sparked something on me. So what you're actually saying is that it's going in that direction where the user also owns their own right to share the data. Is that what you're saying? Because you said 2 places where you shared the data with consent.

Exactly. That's very important. And that's why there are these 2 types of use cases. So the one where the consumer is involved is always related to consent, of course, because consent is mandatory. And if you use data for a new purpose that has not been agreed by the consumer, the consumer has to agree. So on this specific use case, effectively, the consumer has to give consent to process this data for a specific purpose. But always with the idea and the objective in mind to keep this data private for consumers, but also for organizations. That's what we mean by privacy-by-design solution.

It almost sounds like GDPR, privacy by design. But are we not talking about hyper-personalization here?

It is, but—

But then next generation.

It's exactly next generation. So it does not mean— I mean, hyper-personalization does not mean zero privacy. Today, yes. I mean—

There is also a want. I think there was a sort of investigation done or a questionnaire in the market, and it was in Asia. And I believe 60% of people would actually sign in to get personalized information or services from their banks. So that also shows that, I guess, people are willing to share their privacy if they get something in return without losing, again, crown jewels. Second time we used the word today. But if you can share bits and pieces, then maybe it's different. Am I making sense?

Yeah, fully. And good to hear that there are already some numbers Proving that there is the wishes for consumers to bring, you know, to get more from the data. But on the other hand, banks, financial institutions, or any other organizations need to bring, you know, the right means to make it happen. So that's why the solutions, typically confidential computing, but also we are also using personal data vault, brings We talked a couple of times about GDPR, and GDPR is the regulator.

So how does the regulator come into play? What's their view on this?

This is not only about the financial industry. So today, the European data strategy is really driving this evolution, you know, with a set of new regulations that are still under definition or already approved. Typically, you have the Data Governance Act. So this one is approved and it will be—

What is it exactly? If you can maybe explain it to the audience in a few words.

Yeah, sure. So this Data Governance Act brings a framework for data sharing and data control. So for consumers, but also for organizations. So it brings the notion of trusted data intermediary. So this These data intermediaries will rely on new technology, but will bring trust into collaboration, enabling data sharing and trusted data collaboration between different entities, but also ensuring control and privacy for consumers.

Mm-hmm.

And this is a framework. The main difference compared to GDPR is that it's more an enabler. It's really built as an enabler to make it happen and to enable that data sharing, but it's— clearly beyond finance. So it's about data sharing across different industries. And this one will be in application as of September 2023. So it's very soon, very close. Very close. Yeah, very close.

It's funny the way you say it because it almost sounds like open banking, open finance, collaborative finance, and almost the European Union going in this direction. And it's a fact. If you look at the recent discussions they started with the markets, there was a big discussion, or at least the beginning of a discussion, about MiFID profiles, so investment profiles for clients. And also there, they insinuated that there was a possibility to start sharing a profile amongst different banks. But then I guess banks obstructed this because They didn't want to bring their crown jewels, third time good time, onto the table. But with this type of technology, this is all possible, I can imagine.

It's a very relevant use case again. So yes, clearly. And typically it brings new opportunities for banks to bring this data together, putting back control to the consumers, giving much more or much better user experience for consumers in regards to their MiFID profile, but also making sure that the banks keep control over the data. So that's really, I would say, the beauty of the solution. It's not that this MiFID profile and all the data that are required to calculate this MiFID profile will be shared between all the financial institutions. It will be used but not shared.

So this will be a difficult question. And we're almost to the end of this conversation. So maybe I can ask you a difficult question because Because it all sounds so good, win-win-win from every side, but where's the catch?

No, I don't see any catch. I think there's real opportunities. The only thing is that the banks should not try to keep the data on their own, for sure. That's the main reason.

So there you bring back the philosophy of open banking, actually.

Exactly. But with my one main difference is that One of the main reasons why open banking is not successful is about this fear of sharing that data. And with data collaboration and privacy-preserving technology, you can overcome that, you know, that problem. Which means that now there's no reason with such type of technology and opportunity, no reason for banks to keep the data on their own. But also on the other way around, bringing new opportunity for banks to access data from other industry. And that's also important. It's not one direction, but really bidirectional opportunity for financial institution. So that's why it's so, so nice, I mean, to look into the future with also these new technology that are really enabling new things.

So I trust you that there's no catch. I think the only thing left for the banks listening to this podcast is to contact you and to get some more insights. But how do they do that?

Yeah, of course. So from our website, datavillage.me, or through my LinkedIn account, or of course through email, so contact@datavillage.me, or my personal email, frederik@datavillage.me.

Excellent.

There's a lot of input we can share, much more about opportunities and technology, of course.

I think you really inspired the audience of this podcast. Thank you so much, Friedrich, for joining me today. And thank you also to the listeners. And stay tuned, more news from FinTech World will come soon. Thank you.

Thank you, Koen.

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