FSI Member Spotlight Episode #17: Challenges and opportunities surrounding banking data architecture and the pressures of digital transformation initiatives
In a recent FSI Member Spotlight, Praxent had the opportunity to meet with Matt Beecher, CEO at Neocova, an AI-based and cloud-native data solution for community banks and credit unions that includes multi-source unification, advanced business intelligence, predictive analytics, fraud detection, and regulatory compliance.
In this episode host Tim Hamilton, CEO & Founder of Praxent, talks with Beecher about the challenges and opportunities surrounding banking data architecture and the pressures of digital transformation initiatives.
Tim Hamilton: Matt, tell us what is Neocova, and the suite of products that you all have developed?
Matt Beecher: We are a data platform for modern banking. Our real goal here is to transform a bank’s most valuable asset, which is data and allowing them to drive better outcomes with speed and scale. And the suggestion there is that isn’t happening today. Right. That’s where we really focus all our efforts around and our products represent that. Our product suite really all starts with our main product, which we call Fineuron.
This is the repository and the collection of data for a bank. One of the challenges that banks have is that they run through anywhere from 10 to 50 different sources of disparate data that tends to get siloed and unutilized. There isn’t a great mechanism to capture all of that data today. There’s a lot of folks that are still reliant on their core processing systems to be their source of data truth, which just doesn’t work.
It’s not scalable, it’s not efficient and it’s not modern. That’s where our platform comes in, really anchored with Fineuron. We go through a process of using machine learning and A.I. to ingest data from multiple different sources. We process that data, transform it into a unified data language that sits in Fineuron.
Then from there, we’re able to do a lot of great stuff. That process right there, that step, doesn’t exist. This is the Holy Grail from a data perspective to be able to bring all that data, not only unify it but normalize that data, too. That last piece is incredibly important because that puts everybody in the same language. You have good data than to move forward. Then from there, we employ really three microservice products off of Fineuron.
One is what we call Spotlight AI, and as the name suggests, that allows a bank to shine a light into areas that they wouldn’t normally be able to see or get to. And of course, the ‘AI’ suggests that we have a lot of A.I., artificial intelligence-driven mechanics behind that.
But at the end of the day, what we’re trying to help a bank with the Spotlight product is one, enhance business intelligence. Understand my business; what’s going on? What’s happening? And I need to do that quickly. Right. So it’s not a function of, “Hey, we’re going to wait 60 days”, but let’s get that right away, understanding, you know, profitability and product engagement, and these things that are really important but have been elusive for banks.
The next layer is what we call intelligent discoveries, totally configurable, but giving the bank the ability now to use that data to make real-time alerts that are important to them. Right. So a customer has stopped making a direct deposit.
I want to know that because that’s a good sign of somebody attriting from my bank. Right. So I should take action on that. Or, are we seeing a lot of outflows going to crypto platforms? Right. I want to know that. Or, are there a lot of account openings, Zelle account openings? That’s a good source of fraud in a bank if you see that happening. So we can customize these things based on what a bank wants to see.
And a lot of this is A.I. driven, and a lot of it comes off of that normalized data model because we can do that in real-time. And the last is predictive analytics. So the ability to really run models against that data to say, “Hey, who of my customers are at risk of attriting? What are the next best products I should be speaking to a customer about? Where are held away accounts, and what should I do about it?” to drive change management within that organization.
So an incredibly powerful platform or microservice, I should say, that runs off of that data, that allows a bank to do a lot of things that they couldn’t normally do. So that is the gold standard. I mean, that’s typically where we start with the bank.
The second microservice is, what we call Groundswell AI. And this is our BSA AML tool, so really focused on compliance and fraud detection. So here we’re driving a typical BSA AML functionality from transaction management to case management, but also layering in advanced techniques around outlier detection. So trying to catch more bad actors, if you will, using artificial intelligence to do that. All coming from the same source of data, though. And then the last piece of it, which is actively, or I should say, will be active, in development because the need is so strong, is Ambios, which is really our next generation core processing system.
At the core of the core is GL. And that’s really what Ambios is, a general ledger as a service platform with high modularity and integration ability with other fintechs. So this is really important. And we built the mesh into the platform to really conform with open banking standards as well, which is going to get more important in the United States over the coming years. So that’s the platform.
Tim Hamilton: So in the past, you and I have talked about the real gap that’s holding the industry back is; speed to insight. It seems like the future is this distributed ecosystem of fintech, at the edge, in order to enable financial institutions to meaningfully differentiate themselves, to pursue different market strategies.
I wonder if we could double click into the challenges that banks are facing today, whether that’s in the realm of efficiency in operations, or in the realm of customer journey personalization. Tell us a bit about the top priority challenges that you’re seeing on a day-to-day basis?
Matt Beecher: Every bank’s a little bit different. And there is this high-level problem statement, which is, and this is a general statement, but both community and regional banks tend to be buckling under their own weight.
That weight is really driven by paralyzing old kludgy technology and trap data. And that drives speed. Right. The siloed data, your heavy, heavy reliance on legacy core processing systems, you know, is a big, big concern. You can’t talk to a bank anywhere and say, “oh, yeah, I love my core processor and I’m going to be with them forever.” They’re all looking for off-ramps, right? Modern off-ramps.
So I think that’s the starting point with everything. And I think what’s the big thing that’s really kind of driving adoption for us and what problems people are trying to solve and solve them quickly, are this notion of getting a 360-degree view of my customers because, at the end of the day, they just want to do a couple of things. Right. Bank math is pretty simple, right. Pay less interest on your deposits than you collect on your loans.
That’s it. That’s banking. I think these banks are under a lot of pressure today; from fintechs, challenger banks, and larger banking institutions. So they’re losing deposits. They’re making less money on the products that they have.
And even inside of the bank, they’re losing a lot of wallet share. Right. So that form of wallet share can be a mortgage goes somewhere else, a personal car loan goes somewhere else. If you’re more of a commercial bank, merchant fees could be going elsewhere. And fundamentally, most banks don’t have an understanding of even if that’s happening or ‘how do I address it?’ Right. And so that’s really where we’re focused, is how do we help banks solve their banking problems via better data.
So, where that manifests is driving to a 360-degree view of their customers. Business agility is huge for banks being able to move quickly and nimbly driving operational efficiency. So, it doesn’t mean laying off people, but it does mean, “how do I scale human capital through my organization as this bank grows or as we move in a different direction? How do I drive better customer engagement?” And that’s a revenue exercise. Right. So that’s keeping your customer, but it’s also potentially cross-selling and up-selling with new products and its technology acceleration. Right. And I think we sit and talk to a lot of banks and there is such motivation to do something.
A lot of banks just don’t know what to do. And sometimes the answer, Tim, is, well, I’m just going to layer on all these fintechs: account opening fintech, digital banking, front end loan origination, and all of a sudden they’ve compounded a problem. Right, because they’re now adding more siloed data sources. All with good intent. But you know what? It’s built on top of kludgy, kludgy, busted rails. And so they’re quite never going to get to the outcome that they want until they address the data problem.
And I think what’s kind of cool is that we are seeing a secular shift happening in banking pretty quickly. And that secular shift is folks actively moving away from those legacy systems and trying to move somewhere else. So the ability to get an off-ramp to a legacy system is massive here as well.
But at the end of the day, it’s a data problem. Right. Is the data problem around ‘how do I operate my bank efficiently?’ And ‘how do I drive better customer engagement?’
Tim Hamilton: I love the way that you started that. You said my data is tracked. So there’s this concept of encumbered data and it’s holding the bank hostage. That’s really it at the very, very top of the list of challenges that they’re facing.
There is a book called Uncommon Service by an author, Frances Frei, and she talks about how in many, many organizations the prevailing strategy is to do more, to do more across every single value discipline. From, I think you described it perfectly, from direct deposit to savings, to credit cards, to lending and beyond; to just do more. And her thesis is that approach to strategy leads to exhausted mediocrity that ultimately doesn’t endear the brand to the chosen customer. I want to drill down just a little bit. You’ve talked about creating an off-ramp from those legacy data entrapments.
What does that off-ramp look like? Give us an idea of what that process looks like so that we can understand how actually approachable it may be for a community bank looking for a better way.
Matt Beecher: It’s interesting for us, and I’m probably going to say this 10 times over, but, I learned early on, and I’m not a career banker,
You get into community banking and every industry is a little bit different. And somebody made a comment early on in my tenure. ‘If you’ve met one community bank, you’ve met one community bank’, and they are very, very much like snowflakes, which they should be. They serve very different communities around the country and are incredibly important. But the starting point for us is this core recognition of a bank that they got to do things differently. Right. That’s always kind of the starting point.
And we have these great quotes that we collect, that does sort of drive that adoption model, right, from one bank. We need to know what we don’t know now, so we can make better banking decisions, faster; speed, data, insight. These are really important things. The other interesting quote and this was from a 30 billion dollar bank, it’s not like a little tiny bank, this was a big bank, but the direct quote was, ‘we can’t wait around for our core provider to run out of gas’.
Right. They’re literally sitting there and saying, “gosh, we have so much risk in a big name, core provider.” Right. And there are only three that are in that group. And another bank, which is data gathering for us, is slow and expensive. ‘We literally have a zero-degree view of our business and our customers. So once you get over that hump, that you need to do something different and you’re not going to get it from your core provider, then we can start really mapping out how we work with the customer.
And then from our perspective, how we do that, we have an incredible deployment and implementation team, led by Aldo Pietropaolo. His brother, Dino Pietropaulo, is our Chief Technology Officer. So as they like to say, ‘Dino builds stuff, and Aldo implements and then deploys it. And they’re a great tag team. And, yes, they’re brothers. But we work through an agile pod execution model. So once a customer is on board, we work really closely with them too, one, automate a process around sort of onboarding into their implementation cycles and data.
So we really approach this as ‘turbo tax’, right, a guided process of how we access and connect your data. But it’s a work-in-progress with that customer to make sure that we’re working through these things together, the right way. The customer participates with us, and that to make sure that we have the right scoping. We bring in solution engineering to take care of some of the technical things and go through those user stories. What are we trying to do? What are we trying to accomplish? Really spell those out, iterate through our planning cycles, and then really kind of push to go live.
And that’s still a work-in-progress and refinement. But the process of preparing user stories, being incredibly transparent, following the customer in, is massively valuable here, because you just avoid mistakes along the way and you get to a great result. And from that inception to actually start driving results for us is a two to four week exercise so we can move really quickly with these banks to start driving that first layer of value. And that’s really important, because speed to value in today’s day and age, especially when you’re talking about technology, is super important versus the alternative for a lot of banks, and we face this as well, is like, “oh, we’re building our own data link”, or data warehouse, or, data puddle, I don’t know whatever people want to call it. Right.
And one, they’re not technology organizations, they’re banks. Right. They might have good technologies. But we always see those cycles taking multiple, multiple years, hiring of multiple, multiple people and, you know, being incredibly expensive. They can get there. It just takes a long time. So speed to value is really, really important. We’re cutting cycles down from five years into weeks, months, which is pretty powerful.
Tim Hamilton: Wow, that is extraordinary. So talking through the implementation, it’s actually, it’s not a multi-month experience; doesn’t take six to 12 months. We’re talking about a matter of weeks, as you say. The other thing that I heard is that it’s pretty personalized to the client’s circumstance. There are collaborative sessions to really focus the implementation on the use case and on the primary objectives for that particular financial institution.
Matt Beecher: Yeah, and that’s important for us as well. I mean, this is not Microsoft Excel. We know that. And it’s not an off-the-shelf product. But where we stop short, too, and this is an important distinction, is not being a customizable solution. Right. It’s configurable. And I think that’s really, really important because that allows a bank to work with us, work with us very, very quickly, and getting the answers that they need. But it also doesn’t put us in the position of a lot of other technology providers, banking technology providers, that actually make a good chunk of their revenue off of services. So we want to make sure that we stay pure to the technology and stay focused on that. So having configuration aids in speed and cost quite a bit.
Tim Hamilton: Are there other missed opportunities that banks ought to be paying attention to or trends within financial institutions? The data and the use cases that this data could unlock under, for example, the heading of personalization and greater engagement or operational efficiency. Tell us a bit more about that.
Matt Beecher: Yeah. And those are the two big pieces. Right. And I think from a customer engagement perspective, this is huge. Right. And I think the great thing is, once you have good output from data, and I’ll get two examples from there, it actually starts driving change management function, inside of an organization. So we can talk about predictive analytics and A.I. and all this great stuff. The reality of it is some banks literally have no capacity to consume that. They just don’t have enough resources and they don’t have enough resources to run change management against that. Right. Today. They will over time. And other banks have the ability to do it instantaneously because they may have marketing, a well-seasoned marketing team, or even marketing automation as part of their platform. But we think about the personalization as really starting to understand that customer. Right. And we get to a 360-degree view of that customer so we can start measuring now spending patterns. We can start measuring different sorts of activity, especially sort of anomaly activity, and getting a sense of where held away accounts are, which is really important because this is stuff that you typically don’t know. Right.
And so if we start understanding, we’ve held away accounts were, we can do that because we have transactional data and we see a large outflow going to Citibank. OK, that’s probably pretty good – we use ‘fuzzy logic’ – it’s probably a pretty good guess that that’s a mortgage. OK. Why isn’t that mortgage here? Did they even know that we have a mortgage? That’s a retargeting exercise, right, for an individual. We see a paycheck go up. We see a bonus get deposited. Should we be approaching that customer about wealth management services or other products that may make a lot of sense?
And so it’s things like that, that I think to get really interesting. The other side of it, too, is if a bank is launching a new product. Who should I be approaching with that new product? Or, who should be in that mix? Because their characteristics match really well with this product and now we can start recommending new products to certain customers. That’s just not individual customers. That’s commercial customers as well. Right. And you think about simple things that you would just take for granted that banks can do today, like, “Hey, I want to know all of my customers that are paying over five percent on their mortgages. Because I should retarget them for refinancing opportunities, or if I even backtest that on held way accounts, maybe I can reapprove.” Nobody has the ability to do that. Right. And then you funnel that into change management. There’s no ability to actually take action on that.
So those are big simple exercises that are pretty good use cases on that front that tend to drive a lot of value. And on the efficiency side, it’s the same thing. You know, there’s one bank that we work with, that we sort of did some back math with them and determined that they utilize about the equivalent of four FTEs to prepare board and management reports per year. Incredibly manual process. The output is tough to read. It’s just, it’s really hard. So the ability to transform that is huge because that allows them to, one, get to answers much faster. From a management and board perspective. But it also allows them to reposition those people in the organization that was just literally doing reporting. Right. And that’s really, really, really powerful.
Tim Hamilton: And it’s extraordinary under the heading of engagement and personalization, it really seemed, and the change management remarks that you’re making, it really, it seems to me that this is a way to begin a transition to more customer-centricity, and to equip that with all the data and the insights that such a transition requires.
I wonder if we zoom out and talk a bit about, we’ve covered this just a little bit, but we’ll maybe see if we can double click into it just a little bit; some of the challenges that are really holding organizations or have held the industry back from achieving that unified 360-degree view of the customer. And if you look at the current day trends, all the bank closures in 2020, for example, Covid forcing 60 percent of traditional banks to close branches or shorten operating hours. There really is this impetus and this necessary trend towards looking for innovative ways to stay relevant and to add value in more efficient ways. So I wonder how can organizations break beyond the hurdle that has held them back from that unified 360-degree view?
Matt Beecher: Yeah, again, it comes from all directions, right, and I think if one thing for any – hate to use this – as the betterment of the industry, but it’s been a forcing function has been Covid. Right, because it’s dramatically changed customer behavior because it was forced to. Right. You couldn’t go into a branch and the necessities of a branch fell away, a little bit, right? Not fully, because every community’s a little bit different and everyone interacts a little bit differently. But that’s a huge push. Right. So that’s pushed everyone into a digital banking realm, which, again, has highlighted some deficiencies that exist in banking. Right. One of which is the data layer. It’s like, great, I can build all these things on top of the kludgyness because my customers want it. But I still need to fix this fundamental thing. I think the other good thing that’s happened here, too, is that you have a little bit of liquidity in the market via the PPP lending program. So banks all of a sudden have a lot more liquidity and they’re putting it to work on sort of new technology.
So they’re addressing these things because they know they have to because they know it isn’t going to go back to the way it was. And you have to adapt and you have to sort of change, you know, modularity and everyone sort of approaches it a little bit differently. Like I said, but I think the digital realm and modern banking is what it’s going to be. It’s going to be your phone. It’s going to be your computer. And it’s been that way for a while. But I think this has really solidified that so we talk to banks all the time, and they’re actively questioning their branch strategies and how they engage with customers.
Tim Hamilton: Speaking of things to look forward to, I wonder if you can tell us a bit about what the roadmap looks like from Neocova. Give us a sense of where you guys are heading and what kinds of use cases we can look forward to.
Matt Beecher: I think what’s exciting for us is, one, having such great adoption early on in our existence. And having the ability to work with numerous banks and start seeing that RLI and the value that we deliver pretty quickly to banking institutions.
I think from a roadmap perspective for us, it’s continuing to refine the product, especially around some of the A.I. functionality that exists in our current stack. That piece of it allows us to move upmarket pretty quickly and work with larger banks. So, we’re still incredibly focused on the community banking industry. But I think what we’re finding is some of the larger banks and, as an example, one of the largest banks in the world, it takes them 30 days to produce data when requested. Right. So somebody from a business unit will ask for a request for data. It takes 30 days to turn that around. This is one of the largest banks in the world. Right. And there’s opportunity there, everywhere in between to reduce those cycles and drive access to data, that normalized data, throughout the organization; to business unit owners, to people that work in those business units, in real-time and speed. So that’s where we get really excited about the roadmap, is being able to drive that through. I think it also we’re seeing those use cases, again, move up-scale into larger banks that already might have a pretty great data program and a data infrastructure.
What they don’t have is the ability to utilize advanced technologies to drive more speed and resilience, especially around predictive analytics and A.I. So I think that’s the thing that gets really, really exciting for us because we’re seeing that market move because at the end of the day, technology’s got to do something and it’s going to do something that somebody else can’t. And that’s really where we see ourselves.
Tim Hamilton: Could you tell us what is the number one thing people should know about Neocova?
Matt Beecher: Yeah, it’s simple, right? Our vision is pretty straightforward and simple, and that is to transform, organize and make accessible the world’s banking data. So if you’re going to walk away with one point, that’s it.
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