Michal Grela (MG): So hello and welcome to yet another episode of IT Leadership Insights by Future Processing. Today, together with Julien, we’re going to talk about data as an open banking enabler. So we’re going to touch base on two important points here on data and the importance of data on the fintech industry, especially open banking bit. Embracing open banking is already key to remaining competitive and accessible, especially when it comes to data connectivity and aggregation. Today, I feel safe to say that every company is a data company, especially when it comes to tech companies, but possessing data is one thing and making a reasonable use of it, it’s way, way, way another thing, way different thing. My guess today is Julien working with Flinks. So I’m really, really happy to have you here today, Julien.

Julien Cousineau (JC): Thanks, Michael.

MG: Could you introduce yourself and the company please?

JC: Sure. So Flinks is a data aggregator in Canada, one of the leading one since we started three years ago. Is shy number of employee of 60, all dedicated to gather data and empower fintechs in Canada. We are also currently looking at expanding also activities in North America this year. So mainly what we do is we do the bridge between the consumer and the fintech and also providing all the financial services in Canada from probably mainly 50 segments. So my role is directing the technology initiative from R&D to operation to security and all that comes with technology.

MG: Wonderful. Thanks for that intro. All right. So let’s dig into the topic. Just to make sure we’re on the same page, what do you actually mean when you say open banking? What would be your understanding?

JC: Yeah, that’s a great question. And sometime when I participate in some panel, it’s very different from the perspective of depending of who the person is working on. So to me, open banking is access to financial data. Period. It happens when the data move from the consumer consent perspective to a third party. And sometime where some people can, I feel it be confuses, they include in open banking the mean of the exchange of that data. So I think when we were moving from A to point B in the past, not the same way that we do today with cars and trains and stuff, the thing we wanted to do is to move from point A to point B. The means to do that is different as the technology grow. So open banking to me is really about really exchanging that data from point A to point B with the consent of the consumer.

MG: That’s a very interesting explanation. I don’t think I ever came across a similar one. I like how strict it is. And I understand that it does imply some challenges to the open banking trend, or how we would like to put it, especially on the side of incumbent banks, because I imagine that on one hand side, when you’re a fintech or a startup or a challenger bank, then open banking might be something that you have in your DNA. But on the other hand side, the big players, global tier one incumbents struggle. So what are the challenges for them? Why do they struggle?

JC: Yeah, I think it’s also very different from country to country. I know that in Canada, the economic recession in 2008 has not affect that much the consumer in Canada. So the consumer trust on the incumbent and the established bank are very strong today. So it doesn’t mean that in terms of experience, a lot of fintech has a great success. Just for Flinks, we are powering over 250 of those in Canada. And from what I learned on integrating most of those customers is that they really focus on niche experience. And that is directly proportionate to what the incumbent see.

So if they see that some of their customer segments are going through a specific experience that the fintech is succeed to do good with that specific customers, then they try to understand why that customers go in that specific experience, because they have a so massive, big user base, let’s say or customers or clients, then they try to apply some time the fit all model. Where the fintech have a very big leverage into that. One example I can give you is we have lenders that we do that, as a specific terms of the loans rates that enables from religions where it’s not able to loan money because it’s against the way they operate to have interest. And then basically they change a little bit the model and they can address that specific customer segment that the bank never had thought of addressing.

So if you multiply that by a bunch of fintech that are able to very, very address some specific segment of customers very well, then that’s where I think the ecosystem gets more spread, more distributed and the experience is very niche to a specific set of people. Yeah.

MG: That’s true. And I was wondering, preparing for this conversation, how much room there is for such innovation in corporate financial services. And I even came across this comparison that it’s like convincing… Preparing a bank to go that way, it’s like convincing a dinosaur to evolve. But I really like the video you touched on on the customer expectations, because what I see on the market is that banks, fintechs, everybody basically operating within that industry is struggling with managing that expectation. And definitely some of the core challenges of the industry today would be circulating around that, providing whether customer journey, transparency, personalization, et cetera. So how can a technology be a driver here to stay on top of these risks and challenges with customer expectations?

JC: Yeah. No, exactly. And I think asking the question is kind of answering it at the same time. Personalization is really key to the online experience. And it’s what I see that is the most exciting for banks, because that’s one of their main problem is they kind of build the same flow for everyone. If you onboard… Onboarding is one of the biggest challenge, let’s say, even in the portal for retention and stuff, but even just for onboarding for promotional offers or something like that.

The personalization is either absent or very limited. And I think with open banking and the use of the data, that will change very fast so that we can create a hyper personalization flow on their end. And I think more we’re going to a real-time access to those data and data can be… We are talking about financial data at the moment, but you can be, I saw a lot of mix of data together that make that personalization even better, which is social media or even the device you’re using. If you’re Apple or a Windows user, normally we can think about some behavior that you do or the way you behave in the website. For sure I think financial data is one of the biggest assets for hyper-personalization, because thinking about it for a second, how much intel you can gather from someone’s historical transaction, the life events, did you buy a house recently? What do you own? Do you have animals? Are you a smoker?

MG: That’s getting creepy.

JC: Yeah. And that’s where I think it goes when I explain that, because I think it goes… Just seeing that knowledge come with a great responsibility for sure. And it needs come to play here. And that’s why that aggregation and Flings in part of it, all of this aggregation, the main pillar, the center of the transaction is consent management. And it’s explicit consent. So it needs to be a win-win transaction. So let’s say, a good example, if you have a car loan of 6% interest at some banks, and when you go into that hyper personalization flow, we can offer you a 4% transaction loan terms… Sorry, 4% interest terms loan, so it’s a win-win. When the competition is part of the interest into knowing more about you, because for sure if you go physically in a branch, let’s say, they will ask the same question. So it always come about trust. Because if you go in a branch, they will ask, “Okay, what do you have? Do you have this?” And you’re going to start talking and telling your situation and telling what you do. So it’s kind of disclosing-

MG: Disclosing data. Yeah.

JC: It’s the same thing, but it’s real-time, online experience and stuff. So it all come to trust at certain point. And I think the digital economy will all go through that same pillar, which is trust, how we can enable trust when you’re not having a previous relationship with the people. So let’s say, you know either the brand, the bank, or the person that work at the bank that you know, that your mother or father knows since 10 years and you can trust them. So how we can recreate that thing in real-time and with the same amount of trust. So you are inclined to share those data, that anyway you will probably share if you are having that trust.

MG: Yeah, definitely. I agree what you said, especially when you’ve mentioned that we all leave data traces everywhere and you can pick up this crumbles and really leverage both customer and transactional data in products and processes, basically any industry, the fintech industry as well. But were we to focus more on extracting data out of it, how would you approach that? How would you shape your data strategy fintech?

JC: Yeah. So it’s really like… Come with that. I think what we saw from the past bad experience that we have in the industry with the data privacy and stuff, we’ve learned a lot. I think the industry learned a lot about that, but we can do much better. I think consent and explicit consent is the center of that new era of data. And I think what we need to understand is, all that come to trust and when you talk with someone, you expose data, but not in a digital way. So, how we can create that consent network to share that data with trust and to control, the consumer gained back control over the data, they can revoke access at any moment, they can do whatever they do? And when this happening, when that layer is very well made, then the second layer is-

MG: Trust and consent layer?

JC: Yeah, exactly. Then the second layer is exactly what you said. So what we can do with all this data and the value of that data? So a well-organized data structure and that have flow that come to empower the online digital experience. So the first layer is complete. The second layer is that. So now that we organize all the data to understand better you, and you don’t need to repeat yourself over the phone again and again, and repeat all your situation again and again. We can know even better you based on cross matching data that you even not having the power to do or the time to do. And then that’s all the power of hyper personalization and bots and even human analysts can offer you a much tailor-made experience online and even physically if needed. Yeah.

MG: Okay. So trust is something I can hear you’re coming to a lot and you definitely put stress on first creating this trust and consent and only then building something on top of it. And I see that’s not only super ethical, especially bearing in mind how crazy it can go when it comes to how crazy things can go and how strange things can go when it comes to the amount of data you can collect on people. So it also creates this on the second hand, it creates this buy-in on the side of customer, because he explicitly gave consent in order to have some benefits. And that’s really a great approach. If you actually have this amount of data, what would be the practical use cases that would be especially relevant when it comes to open banking? Do any come to your mind? I don’t know, maybe… Yeah.

JC: Yeah. Yeah, no, I actually mentioned one good one, but I have many of them that I like much. So, since we opened the open banking and exchanging financial data become more common for people, just in Canada four million of the 38 million Canadians has used open banking in the previous year. So it’s-

MG: That’s quite good.

JC: That’s quite good. So we’re still in the adoption curve. We’re not critical mass for sure, but we see that this is getting up and up. And mostly because people start to understand that either it’s getting seamless to interact with the trust system actually, because one of the things we do with the financial data is to establish trust between parties to go to the next layer of the transaction. So it’s seamless for that. And then you also have all those people that are not well served with the actual system, because the actual system trying to optimize for the mass, the general. So with data, as I said, you can do personalization and then you feel much more served by your institution and so on.

So one example I have in mind is people that come to Canada that do not have any credit score, the credit score has been there for years to determine if you are a good payer and again, it come to trust, if we can trust you to loan you money, to start a business, to even have an apartment, or even have a cell phone and so on and so on and so on. So every people that come to Canada, let’s say they don’t have the credit score yet, but they all have a bank account. You start with a bank account, you come to the country, then we can with data build new model that will even distinguish or being even more precise than a credit score that is focus on delinquencies. Then with financial transaction, you can go more the transactional behavior.

So are you paying your rent in time? What’s your income variation ratio? What’s your… And so on and so on and so on. It’s not just about delinquencies, and delinquencies to predict that you will not repay a loan, let’s say, it’s much more long to predict, let’s say it needs… You could not pay your credit card, let’s say in six months from them and now this is the first event that you have. So you need a much more longer history recall data to predict delinquencies than predicting behavior. So, for people that just come to Canada, we were able to do a lot of good stuff for those people that are normally under bank at a certain point. And that’s one of example, I like.

What come in minds also? We have also very weird case, let’s say validating… Like I said, if you have the money you need to do that transaction, the right balance for doing a payment between two persons. Before you were not able to do that. So by doing that, you also diminished the risk and then the percentage of interest on the transaction could go lower, because normally people try to have a proportionate number percentage of the transaction for the loss that they’re going to have. Let’s say, if you have 10% of your transaction and not have the right balance, then you need to do absorb that loss. So what you’re going to do is you’re going to increase the percentage of transaction so you can absorb that loss. But if we were able to, in real-time, understand if the transaction will succeed or not, then everyone is winning because risk is going lower, trust is going higher, and then people are available to be competitive on the margin.

MG: Yeah, I really like your two cases, especially the one with the credit score, because what also crossed my mind, just top of mind as an example of data in open banking was credit risk analysis, which is often being brought up as a common use case as well. I don’t know, fraud prevention or detection or income detection, et cetera, et cetera. So actually something that’s hands on and useful. So what would be your predictions when it comes to the future of this niche? How do you see it evolving in the upcoming years?

JC: I see that a lot of a consolidation of banking services will happen in the future. And you see already the big tech that trying to have grips on it. And because the border were getting, less as a friction for the banking system. Right now we are in a highly regulated space, more seen in North America. Not North America, in America mainly and also Iraq. But more than the open banking will be stronger and stronger, we’re going to have some global player that’s going to start being more present and build brand toward that. So I see that a lot of consolidation in the market will happen and the competition will be much in favor for the consumer in the near time, yeah, in close time.

MG: Yeah, the consolidation is also a trend that you can definitely observe in real-time and also big players buying or investing in fintech startups and challenge banks. And I’m just wondering, what’s the bottom line of our conversation? What will be the key takeaway? And what I noted is that, of course open banking is all about this connectivity and data aggregation and it’s all about data being available. But what’s also very, very important in the second leg would be that is all about creating this buy-in and trust on the customer’s side, so that they are willing to actually give this data and they should expect something in return. Would that be something you would agree to?

JC: Mm-hmm (affirmative). Yeah. I think we went a bit too fast in… I said too fast, but it really depends of who is seeing it, but we use your [inaudible 00:23:16] of the data that was available without really thinking that much into the data privacy and the data’s infrastructure that needs to be done as the consumer as the center of it. So, now we need to go a little bit backward and fix that layer in order to really enable the next stage of innovation around… I think open banking, it’s a branch of the more broad open data discussion.

MG: So it’s going to be more about data as well in the future?

JC: Yeah.

MG: Not just about banking. I like that one. I’m looking forward to it. All right, cool Julien, thank you. Thank you for that conversation. It was very informative and very interesting. And also thank you our listeners for listening to this, another episode of IT Leadership Insights by Future Processing. If you liked it and if you found it useful, don’t hesitate to share it and please do drop us a line if you’d like to have another topic covered in one of the future episodes. Thank you. That was IT Leadership Insights by Future Processing.