Your Bank's Data Strategy Could Make or Break AI Success

Jim Marous (00:11):

In banking, every second matters. Fraud happens in milliseconds, consumers demand instant answers, and AI can only deliver value if it's powered by live, real-time data. Yet many financial institutions are still relying on batch reports and outdated systems making decisions based on yesterday's insights. The shift can't wait.

Jim Marous (00:36):

Forrester predicts that by 2025, half of all businesses will use AI-powered self-service as their primary customer touchpoint. The future won't be positive or possible without real-time data at the core. Banks that leverage streaming data will transform customer experiences, manage risks more efficiently, and unlock the full potential of AI. Those who don't will certainly risk being left behind.

Jim Marous (01:06):

Today, I'm joined by Gillaume Aymé, CEO of Lenses.io, and a leading voice on data innovation. Together, we'll explore why real-time data is becoming a lifeblood of modern banking, the hurdles institutions must overcome, and how to build the foundation for AI-driven success.

Jim Marous (01:25):

So, welcome to the show, Gillaume. Before we begin, could you please introduce yourself and your company to our audience?

Gillaume Aymé (01:33):

Hi Jim, it's an absolute honor, big fan of the show, great to be here. So, I'm Gillaume, I'm CEO of Lenses.io as you mentioned. We are one of the leading players in what we call a data-operating fabric. We make it easy for businesses to harness the power of real-time data and transform their business so that their customers can have services that provide real-time insights and actions.

Jim Marous (01:57):

So, let's start with the most foundational level. Why do bank executives care about whether or not processing happens in real time?

Gillaume Aymé (02:06):

Well, maybe we should clarify for your listeners what we mean by real time. Data is generated by every single organization, especially within financial services constantly. And the moment or time at which you can act on that data is a competitive advantage for any business. And we have to say that financial services has actually done a pretty good job over the last 20, 30 years.

Gillaume Aymé (02:32):

Imagine going up to an ATM machine and pushing in your card and typing in your pin number and having to wait five minutes or for you to be authenticated to withdraw your cash. That just wouldn't be acceptable.

Gillaume Aymé (02:45):

And I think because of the intricate nature of financial services within a world economy, there's been a lot of pressure on banks to ensure that some services run in real time. But now, we're really crossing a chasm. It's a new era now where real-time data is both a huge opportunity, but also a huge threat. Either if you don't do it and you don't harness real-time data, or if you get it wrong.

Gillaume Aymé (03:12):

So, if we think over the last 20 years, high-frequency trading systems, settlement systems, real-time payment, authentication, these are all kinds of services that banks and financial services organizations have harnessed.

Gillaume Aymé (03:28):

You could say that after the financial crash, there was increased pressure on cyber resilience. Regulators were demanding for real-time insights on the position of banks. So, that triggered a new wave of systems being modernized to real time.

Gillaume Aymé (03:53):

Open banking really was a great driver of real-time as well, forcing some of the legacy players to open up their platforms, share data to third parties. And that also caused that information to be shared in real time; real-time insurance settlement, real-time quotes.

Gillaume Aymé (04:13):

Imagine if you are on an insurance aggregator site, and your company took five minutes to respond to a quote, you just wouldn't be in the game, and you'd lose market share. We're really turning a page because now, these are some of the services that you might expect.

Gillaume Aymé (04:34):

But now, it's an absolute requirement by customers to have real-time services. A bank can have the best possible branches. Those branches could be palatial, but actually, if the mobile application doesn't show your bank account in real time, that just wouldn't be acceptable, especially to the new generation.

Jim Marous (04:56):

It's just interesting because to your point, consumers know what they get anyway. If they don't understand the back office of a financial institution, more and more, they don't at all understand if something's not real time. They kind of in the back of their mind thought, “Eh, I understand I may not get credit for my paycheck right away, but the rest of everything else is done in real time.” And it's not.

Jim Marous (05:23):

It's interesting because you've written in Forbes (because you’re a regular contributor) about companies struggling with poor or inaccessible data when implementing AI. Could you give any more explanations or illustrations as to what this looks like in a typical retail bank, and why it impacts the customer experience so dramatically?

Gillaume Aymé (05:44):

Yeah, you're totally right. As a consumer, I just expect it to be real time. And the fact that it's difficult behind the scenes is not my problem as a consumer. We just have that level of expectation. Now, I think we'll probably get into why some of the changes, the banks, all these systems not being in real time. But when it comes to AI, again, this is where expectations are very high from consumers.

Gillaume Aymé (06:07):

We're now becoming very familiar with the likes of ChatGPT and the AI agents and so on. Let me give you an example. What we are starting to see more and more from our customers is AI-assisted call centers. You call your bank through the call center, and you might be on hold for 30 minutes if it's human. There's a huge opportunity for that frontline support to actually be AI.

Gillaume Aymé (06:31):

But for us to replace that call center operator with an AI operator, then that AI system is really going to need a lot of context. It's going to need context and the context in real-time, but that's a lot of feeds there. That system has to be built, and it has to be trusted.

Gillaume Aymé (06:51):

And some of the complexities and the problems for banks is have they put in place the right infrastructure and the right practices to build these systems that can be trusted and put in front of customers?

Gillaume Aymé (07:05):

Imagine if you are engaging that call center operator and it just stopped for five minutes to think whilst it fetched some data in a batch. Well, maybe if I was talking to a human, I would accept that. But if I'm talking to a machine, I'm not going to accept that it freezes for five minutes whilst it goes and fetches a patch of data. Or worse, still, it doesn't have the latest information about me.

Jim Marous (07:26):

Yeah, it's interesting because if you think about this, a lot of this … where banks are really focusing a lot of their AI implementation right now, it's on the back office to try to get more efficiency, more automation, more effectiveness. But even that can't be done if you're not running in real time.

Jim Marous (07:44):

You just brought up a great example in a call center where we're trying to lower the cost of a call center for the basic requests. But basic requests are things such as my current balance, what transactions have happened in the last day or in the last minute, if they're looking at fraud.

Jim Marous (08:00):

In addition, a lot of organizations are spending a lot of money and a lot of time on improving the security side, and the fraud side, and the privacy side of what's going on within their financial institutions. The current legacy systems just can't support what we can do and what we must do, not only for our organization, but for our customers as well. Correct?

Gillaume Aymé (08:25):

Yeah, yeah, absolutely. I think for a bank or any business to really successfully position itself for this AI revolution and AI era, there's a lot of work that needs to happen behind the scenes. Fundamentally, a lot of the core infrastructure needs to be modernized, these are IT systems.

Gillaume Aymé (08:46):

The banks live with this legacy of 50, 60 years of archaic infrastructure. Some of it, probably maybe about 5 or 10% has been modernized throughout time. Now, as what you said, real-time is an expectation across a business. Back office, front office. So, now this other 90, 95% of infrastructure has to be modernized.

Gillaume Aymé (09:10):

Some of it is going to go into the Cloud, some of it without wishing to get … or some of it will stay in place, but it needs to be modernized. That's one challenge. The second challenge is upskilling your team. For these systems to be built to process real-time data, it's a whole new paradigm, actually, from how systems were operated and built in the past.

Gillaume Aymé (09:38):

So, you've got a workforce that somehow you need to upskill to this new form of data. And this is really where Lenses comes in, by the way, is simplifying the experience for those teams so that actually, you don't have to upskill. You can keep the skills that you have already in the house and just make the work simpler through great tooling.

Gillaume Aymé (10:01):

And I was going to say that the other thing is, time is not so much of the essence in some regards. I think you touched on it, but there are a few use cases now that are just a simple threat not only to the business, but in some cases, to the flow of money in the economy. Cybersecurity, you now have seconds to respond and protect against a threat or defend against a threat.

Gillaume Aymé (10:28):

Fraud could be millions of lost funds if you're not responding quickly to fraud. Also, can lead to money laundering and all sorts of other challenges. So, I think some businesses really (in some cases) need to wake up and realize the threat of not protecting their business, let alone having the competitive advantage by not modernizing these systems and applications to real time.

Jim Marous (10:59):

It's interesting, Gillaume, you have to work with organizations all the time that need their core updated. Most organizations (it's bigger than a bread box), it is such a huge endeavor. So, they're trying to find ways to compartmentalize what's going on.

Jim Marous (11:16):

How does Lenses work with a firm that may have a partial legacy, a partial modern system? How do you work alongside a core to make it so that they can get the benefits of what Lenses provides without having to wait until the whole thing's being redone?

Gillaume Aymé (11:35):

Yeah, that's the key. I think we have to accept that these businesses are not going to be able to update or modernize all their systems or even be able to move their data. So, the great thing about Lenses is it sits on top of an organization or a bank's existing investment. It's not asking to change anything. It's going to discover what real-time data already exists in the business. That's already a great help.

Gillaume Aymé (12:01):

Jim, if I asked you to make a quiche, you wouldn't be able to make a quiche without knowing what ingredients you have. And I don’t know about your taste, but you’d probably want to taste ingredients before you make the quiche.

Gillaume Aymé (12:16):

So, in the case of building a new system that responds to real-time, those teams that are building those systems need to know what data already exists, what ingredients already exist. And if they need to do that, they need to be able to taste it.

Gillaume Aymé (12:30):

And by taste it, I mean to see the data quality. Do I trust it? Is this data good enough to build my service for application? Now, you can do that without modernizing anything, without moving the data. That's the thing that Lenses started. It just sits on top, and it discovers, and it powers teams to be able to touch and feel and taste data if I continue with the analogy.

Gillaume Aymé (12:54):

Then the next step is, “Okay, I've got the data to build my system. I like it, it's clean, or maybe I need to process it in some way. Now, I need to build my application.” And again, this is the work done by engineering teams.

Gillaume Aymé (13:09):

But when you're building these applications with real-time data, it's very, very complex. Because some of these systems are like super jets and F1 cars, really complex. You need some of your best engineers. And unfortunately, you don't have an army of best engineers.

Gillaume Aymé (13:25):

When you've got 5,000 systems to modernize, you're going to have to use your entire workforce. So, either you upskill them or you make their job easier. And this, again, is where Lenses comes in. It makes the work of building these systems so much easier, so that practically, anyone, almost anyone without an engineering pedigree can do it.

Jim Marous (13:48):

That is so important and we're lucky in the banking industry because over the last five, six years, we've gotten more and better solution providers that can help and partner and make it so that … so many financial institutions would say, “I'd like to do a core conversion but I need my data to be fixed first.” Well, now we don't even need data fixed to fix data. It's my non-technical mind thinking there.

Jim Marous (14:17):

But on the other hand, what's important is by you orchestrating what can be done with the data and with real time, you are putting them in a position that they're much better and more ready to make the additional core conversions that they were stalling on because of this. And from what you're saying to me, multiple times, they don't need a core of engineers from front to back all the way across the organization.

Jim Marous (14:46):

You bring tools to the table that makes it so you need less people, but you also bring a partnership and a collaborative spirit that says, “We already work with organizations like yours. We've gone down the wrong routes at times. We've gone down the right routes. We can bring case studies to the table that can show you the return on investment.”

Jim Marous (15:06):

This is a big deal in the industry because we need the conversions to be fast, we need it to be seamless, we need it to be thorough, we can't do half jobs. But we also have to make it so it's the building block upon which something bigger is done.

Jim Marous (15:21):

When somebody engages with you, how long (and I'm not going to put you in a corner here because it really depends more on the client than on you) does it take with a partner that says, “I want to go about it the way you want to go about it. We're on the same page.” How long does it usually take to actually convert their systems and their processes to what Lenses can provide?

Gillaume Aymé (15:46):

Okay, maybe we're an exception here, but generally, we're talking about days of effort to get some value because I think what we find with customers is they've been trying to modernize these systems without tools for some time. And now that backlog of these systems that need to be modernized, it's just getting bigger.

Gillaume Aymé (16:05):

In some cases, I was speaking with a large financial service, US based bank, and they said even without AI, they had 400 different projects in the backlog that they just couldn't process quickly enough.

Gillaume Aymé (16:19):

And again, that's the challenges that we've talked about, the lack of skills and lack of access that these teams have to data. So, already, by just giving them something that accelerates, it takes the pain away. And I'm really talking about days in general of value to accelerate the development of these services and to get value from Lenses.

Gillaume Aymé (16:42):

But what I find then is (and especially now in the world of AI and agents) that our customers and the market in general is so hungry to partner with vendors. We’re entering really uncharted territory. And what will make customers successful and businesses successful is having close partnerships with businesses and navigating this uncharted world.

Gillaume Aymé (17:10):

And more and more, I have customers coming to us and saying (especially around the AI), “Hey, could you help us here? We have this strategy, or we have these initiatives, we'd like to get validation of how you could help us and get insights of also how you've helped other customers.” And I've been in the industry around 20 years, I've never sensed more of that than I have in the last six months in particular.

Jim Marous (17:39):

What's interesting too (and we see it with almost every type of implementation tool, every type of collaboration) is while there's great intention, sometimes we can't get out of our own ways. So, what barriers, what hurdles get in the way upon trying to get good implementation success?

Jim Marous (18:04):

Because what you bring to the table is pretty powerful, but that doesn't mean that every engagement plays the way you want it to, or that there's challenges financial institutions have within their own organizations actually being a good partner on their side, not intentional, but it happens.

Jim Marous (18:22):

What do you see as being the biggest challenge financial institutions have in actually moving forward what you can provide?

Gillaume Aymé (18:29):

I think regulation is definitely one of them. Because there's this instant fear of, “Okay, if you're going to give teams access to data, especially real-time data, it's a very, very difficult form of data to master. And when I say master, it means harness, but it also means govern.

Gillaume Aymé (18:50):

And I can probably use the analogy of data in a lake that's settling idly, you can steer it and control it. But then real-time data is like a stream, sometimes we literally call it a data stream. It's a torrent of information. How do you control a torrent of information from a governance and regulatory perspective? Not only that, that data lives in multiple geos.

Gillaume Aymé (19:14):

Most of the listeners here working within financial services generally have global operations, whether that's your customers, your employees, your partners and suppliers. So, your data is going to live in different geos and jurisdictions. Not only that, this real-time data is almost literally flowing across different borders.

Gillaume Aymé (19:38):

That becomes a huge concern and it's frightening for businesses. Do I want to give my teams access to data that they shouldn't be seeing? And again, this is where we partner with our customers and our solution allows you to have not only this global visibility, it doesn't force anyone to change the data or move the data out of a geo, but it provides all the security and the governance and the data masking, if necessary, of anything that's sensitive.

Gillaume Aymé (20:06):

So, I would definitely say compliance to security is a big barrier and frightening for organizations. Again, this is where we-

Jim Marous (20:12):

And not because it's illegal, but the perception of, “Are we going to be … again, it's legacy thinking being applied towards modern technology and capabilities. And we as an industry had to go through that when we looked at Cloud governance, that,” Chief, do I really want my data someplace else?”

Jim Marous (20:32):

And the reality is once people realize, “Oh, geez, the Cloud is more secure than my current system,” then it's okay. But to your point, it's not that what you're doing is illegal or breaks any regulations, it's the perception as to, “How do I cover my tracks?”

Jim Marous (20:49):

But again, the good news is you have a wealth of knowledge within your organization, but your clients also have had to have that same hurdle jumped over. And you can use them as an example as to how an organization can rethink what's possible.

Gillaume Aymé (21:05):

Yeah, absolutely. And I think with AI, this is where it goes to another level, is that, now there's pressure on these businesses to give AI access to data. So, if you don't trust your humans with this data, are you going to trust your AI?

Gillaume Aymé (21:22):

And this is where putting – first thing is trust. What I would say to every customer that really wants to jump on the AI and Agentic train is you've got to solve your fundamental problems. You've got to trust your humans with data first.

Gillaume Aymé (21:41):

And that is possible, as you've just said. The same thing with moving to the Cloud, the fear around that, with good tooling and partnering with vendors, that is possible. And then once you trust your humans, then you can start trusting your agents. What you don't want to do is trust your agents to access data when the humans can't, or you don't trust your humans. And you don't have the guardrails around the human access.

Jim Marous (22:07):

It’s interesting, you've written in Forbes about a concern that many bank leaders have around vendor lock-in, essentially betting that their future is going to be left with one technology company. Why is this particularly dangerous in financial services and how should banks rethink about protecting themselves?

Gillaume Aymé (22:30):

Yeah, I think this is now one of the biggest concerns I'm hearing from leaders. I think it's something that's been passed down from investors, and the board, the C-suite, and now to the technology leaders that I speak to.

Gillaume Aymé (22:48):

In the past 20 years, I would say that there was convenience with working with one vendor. It gave you economies of scale, speed, comfort, you built great relationships with them. So, the benefit was speed, especially when it came to adopting Cloud, for example. So, we'd often hear us all in with a particular Cloud vendor.

Gillaume Aymé (23:11):

Regulation is slightly intervened here and said, “Actually, you know what? Maybe you shouldn't have all your eggs in one basket. You need to hedge a little bit your risk.” I think now that's at another level where there's commercial factors and market factors, but also geopolitical factors where there is a concern that being dependent on a very small number of vendors is now a risk.

Gillaume Aymé (23:38):

Again, because we're talking about banks, it's not just the organization itself, it's actually the flow of money in the economy or even in some cases, the world economy. So, what's good is (as I said), the convenience of working with just one vendor just meant that you’d have to train your team on one technology and so on.

Gillaume Aymé (24:02):

Now, what we're seeing is, there's a risk with that, but also, there's a competitive advantage by working with multiple vendors. And I'll give a few examples. One of the big things that we're seeing in the industry is the need of data sharing.

Gillaume Aymé (24:19):

Now, data sharing (we've touched on it, some of these services that we talked about) means moving data as a real-time data stream from one part of your business to another. From your payments team to your finance team, or to your marketing team.

Gillaume Aymé (24:35):

Now, we're seeing the need of sharing data externally to your business, either to your suppliers or in some cases, your competitors, or third parties in general. I'll give an example; a very large US banking customer shares data with a big Cloud provider, ServiceNow. The reason for that is cybersecurity.

Gillaume Aymé (25:00):

They use this particular ServiceNow ticketing system. When a ticket is raised, the internal security team at the bank is in-house. So, there needs to be sharing of real-time data between the supplier and the bank itself. And we're talking about seconds to respond in the case of responding to a security requirement. But that's not the case of a partner.

Gillaume Aymé (25:25):

When it comes to fraud, there's a need of sharing data across the different banks. Everyone has to gain by sharing fraud and attack vectors even to their competition, everyone has to gain. And again, sometimes you just have seconds to share that information for another organization to respond.

Gillaume Aymé (25:43):

But perhaps, the biggest opportunity is around commercialization of data. So, data that's in a batch or available in a lake (so it's kind of aged a little bit) has finite value. If you can convert and have that data as a stream in real time, it has exponentially more value. And that value can be sold.

Gillaume Aymé (26:08):

And I think there's an opportunity for every business to become effectively a data business. Data has to be treated as a product. And just like any product, you need a supply chain. You need the ability to distribute that data across your business and across your other businesses.

Gillaume Aymé (26:24):

If you've got real-time data, but you can't share that and send it out of your organization as a real-time data stream, you're losing an advantage to commercialize that. And that buyer of the data might say, “Well, hold on, you add this data and it's a real-time data stream, and then you're sending it to me by batch. That's not the product I want. I want this data as a real-time data stream.”

Gillaume Aymé (26:48):

If we think about how L&Ms are trained, I think something like 10% of the world's data is publicly available, what these L&MS are trained on, Wikipedia, literature and so on. 90% of the data is behind a firewall, it's commercial, it's private data.

Gillaume Aymé (27:07):

And if we want to really leverage the AI revolution, these L&M providers are going to have to train their data on private data, medical research, raw patterns, and so on. And that's going to come from private enterprise.

Gillaume Aymé (27:25):

So, the question is, how do these businesses share this data and sell it commercially as a real-time data stream? And this is also where Lenses helps. We've just released a replicator that allows you to move and share your data both internally to your business and externally to your business in real time.

Jim Marous (27:45):

We've touched around AI and when you look at the future of AI, for me, it's extraordinarily exciting when you think about Agentic AI and what the potential can be with AI agents. These systems and this autonomous thought pattern can make decisions and take actions on behalf of a consumer, not knowing what the consumer's actually going to accept or what's going to be possible from a banking world where there's so much emphasis on risk avoidance as opposed to managing risk.

Jim Marous (28:17):

But how does real-time data change what's possible with AI in banking as best as we look towards what the future can really become?

Gillaume Aymé (28:27):

Real-time data opens up whole new possibilities. I think it's a common theme, but in our discussion about cybersecurity, it’s the sort of thing where the types of attacks are beyond the capability of a human to respond in time. So, these security agents need access to real-time data in order to detect and respond to threats.

Gillaume Aymé (28:58):

That's an example of it's absolute table stakes that these security agents need access to a real-time data stream. Broad would be another example. I think I touched also on call centers. It comes up increasingly in the conversations I've had with customers, the opportunity to reduce cost of call centers.

Gillaume Aymé (29:20):

Again, these call center operators, and the AI assistant need huge amounts of real-time data. I think you mentioned something interesting that was, “Do we put these agents in front of customers directly?”

Gillaume Aymé (29:36):

I think the industry is recalibrating a little bit on what they can expect from agents, especially within highly regulated industries. They are difficult to master. We shouldn't think that these agents are easy to build. But they may be easy to build, but building is one thing, but that actually putting them in front of your customers is another where you can trust them.

Gillaume Aymé (30:02):

The safest thing (what I'm seeing with customers) is where there's AI in the loop, it's a human process where AI is assisting the user on that journey. And maybe if I lean on call center as an example, we have a particular customer doing this where it's still a human call center operator, but there's AI systems analyzing the voice of the customer in real time.

Gillaume Aymé (30:37):

And that voice analysis is doing sentiment analysis. Is the customer frustrated or not? Now, if you've offshored or outsourced your call center, it could be that the team is in a different culture or adopted a different culture and they wouldn't understand the intonations based on the voice, whether the customer is frustrated or not.

Gillaume Aymé (30:59):

And here's an example where, on the call center screen, it can measure and state whether it feels that the customer is frustrated. And if they're frustrated, you might route them to a different operator or offer them a discount or an apology or of some form. That sort of system has to happen in real time. There's no point telling the call center operator five minutes later that they were frustrated after they've already hung up.

Jim Marous (31:22):

Well, it’s interesting because when you look at Agentic AI, the way I see it actually being implemented (at least initially) is you can give me recommendations, but just like it’s being done on text right now with a lot of other industries, you can always ask me to authenticate that you want to go forward with what I've recommended.

Jim Marous (31:40):

If you're saying, “I found this and you may be interested in it, but I give you the final say,” then it very intentionally says, “You are controlling your data and your information, I'm simply here as a really powerful assistant that says, “I found something you may be interested in.”

Jim Marous (31:59):

That also opens the door that if I'm wrong, you can say, “No, I'm good.” And that makes it much more comfortable. Now, I'm not too sure, the financial institution industry is just built on no risk. And so, they're afraid of that wrong answer out there, when really, we're giving everybody the authority, every other industry, every other connection, the authority to make a mistake if you do more right than wrong. The value is built that way.

Jim Marous (32:27):

And I think that's really where the power is. And again, to the very foundation of our discussion, you can't do that without real-time data. It doesn't do me any good if you're telling me what has already happened to me, I get that already, I want to know what you think might happen to me ahead of time.

Jim Marous (32:44):

I want the GPS that says, “By the way, there's a Starbucks or a retail store you like visiting 15 miles up the road.” And tell me, “Since you're continuing down this path, you may want to stop by.” I may be completely wrong, I may not want to stop there, but I'm not going to accuse you of being wrong. You're giving me good information.

Jim Marous (33:05):

It's interesting because you've mentioned a couple times the fact that most financial institutions just don't have the engineers and developers necessary to really use real-time data and AI in the current state of affairs.

Jim Marous (33:23):

For banks that are not traditionally seen as technology companies, how will they restructure their systems or their back offices to be able to really implement what you can provide at scale and speed?

Gillaume Aymé (33:40):

Yeah, I was going to say that just going back to what you were saying there a minute ago, is one of the best ways of losing confidence with the AI agents is if it's providing something and it's just not in real time. I'm just going to say, “Well, that's worthless to me.” And that's how you can lose faith and trust in these Agentic projects, I might as well do it myself. So, hence the importance of real time.

Gillaume Aymé (34:07):

Well, the thing in terms of the back office (and this is probably one of the biggest disruptors of the industry right now) is actually AI within engineering itself. This is where by equipping engineers themselves with AI, that makes their work easier, then they're better positioned to modernize more systems.

Gillaume Aymé (34:31):

And again, this is also where Lenses comes in with introducing AI into the product itself that's embedded in the workflow of some of these teams and what they're having to do to accelerate them.

Gillaume Aymé (34:43):

This in general, is almost a low risk because there's always (as you said) a human element as it stands. If it's right, it's great; if it's not, we move on. So, what we've seen with many of our customers is investing fundamentally in AI, equipping AI to engineering teams for them to accelerate the building of the AI systems.

Jim Marous (35:11):

It's interesting, Gillaume, what you provide is something that every institution needs to have available to them through one provider or another. The challenge many times is not knowing what you don't know and not knowing the risk of doing nothing. How does an organization connect with you to at least have dialogue around what's the potential?

Jim Marous (35:38):

We've only scratched the surface, I'm not a programmer, I'm not a technical person, but the reality is, I know what you provide is something that's needed. But I know that if I was a retail banker at any level, I'd probably say, “I not only have to kick the tires, but I got to understand more about the risk and reward around this.”

Jim Marous (35:59):

How do they get ahold of you or your company to get a better understanding of what the potential is to at least open their eyes? I tell companies all the time, “We have so many great solution providers, you've got to pick up the phone, get on email, hit a text, whatever it is, to engage with these companies to understand better about what's out there and what's needed.”

Jim Marous (36:20):

And then you still have to make the decision who to partner with. I can almost guarantee that it's probably not your normal core provider, because most core providers, this is not what they're the best at, even though they're data companies by trade. So, how does an organization get ahold of you or your company, to understand more about the risks and rewards with this?

Gillaume Aymé (36:44):

The good news is that the majority of our customers are within the financial services industry. We have a lot of experience. We've got more than 10 years in this real-time space, and we’ve worked with some of the largest Fortune 500 companies.

Gillaume Aymé (36:59):

And we know that this is entering a new paradigm. So, my team and the team are very well-trained and versed on both educating our customers and our prospects as they reach out to us. And we've got experts that are not just on a single project, but they help our customers on a whole transformation.

Gillaume Aymé (37:24):

And that includes all the things around the side, cultural changes that might need to be implemented within your business. And of course, some of the different use cases that we can share with, that we do with other customers. But yeah, the easiest way is to go on our website, drop us a message, and then a team will respond back.

Jim Marous (37:47):

It’s interesting also, Gillaume, as we look to what happens in this industry. I've been in this industry my whole career, and I think it's a challenge because to get us unstuck, to get us to actually do something, often is hard. But it takes that first step just with any kind of transformation, personal or professional.

Jim Marous (38:08):

What would be your recommendation to a financial services executive to that first step? What should they do today to become more resilient? We usually talk about resilience in the form of fraud, risk and insecurity, but the reality is resilience now means being agile enough, being flexible enough, making sure your back office can actually support the future of AI.

Jim Marous (38:31):

What does a financial institution executive have to do today? Number one, job one, to really get moving towards a better future?

Gillaume Aymé (38:40):

I think there's so much value in teams on the ground. If you can equip those teams with tools that makes their job easier and that they can embrace … rather than seeing real-time data as something daunting or scary.

Gillaume Aymé (38:57):

We touched on it also around regulation. Actually, there are easy wins. You can win hearts and minds by giving your teams on the ground great tooling that allows them to embrace that. That instantly triggers a cultural change in your business.

Gillaume Aymé (39:14):

By equipping those teams, you're protecting yourself for the future. You're equipping them to be able to respond quicker as new threats and opportunities arise.

Gillaume Aymé (39:24):

And I'm a big believer, I think it's a culture that we try and instill within our company. We're maybe a very engineering-heavy company, but wow, there's some great talent with those engineers. And if you give them the right tools, they can deliver magic.

[Music playing]

Jim Marous (39:40):

Great recommendations, great thoughts, great insight, and almost a lens to the future. I'll use your company's name as a little bit of a connection there. But for those of you who are listening to the podcast that want to understand more about the power of data, the power of AI, the power of analytics, and the importance of real-time data, be sure to catch some of the other episodes shown here on the screen.

Jim Marous (40:07):

And as always, thank you for joining me on Banking Transformed. Gillaume, thank you for being on Banking Transformed today.

Gillaume Aymé (40:14):

[Foreign language]. Thank you, Jim.

Jim Marous (40:17):

Thanks for listening to Banking Transformed, the winner of three international awards for podcast excellence. If you enjoy what we're doing, we would really enjoy a positive review.

Jim Marous (40:27):

Also, check out my recent articles in The Financial Brand and the research we're doing for the Digital Banking report. This has been a production of Evergreen Podcasts. A special thank you to our senior producer, Leah Haslage; audio engineer, Chris Fafalios, and video producer, Will Pritts.

Jim Marous

If you want to hear more about the Debbie platform and how you can boost engagement by rewarding positive credit behavior, check out our previous discussions with the Debbie founders on the Banking Transformed Podcast.