The Final Mile of Data: From Insights to Engagement

Jim Marous (00:00):

Hello, and welcome to Banking Transformed, the top podcast in retail banking. I'm your host, Jim Marous, Owner and CEO of the Digital Banking Report and co-publisher of The Financial Brand.

Jim Marous (00:22):

Customers want their financial institutions to know them, understand them, and reward them based on their unique financial situation. Displaying this level of empathy is difficult though, if data remains in silos. The aggregation of data to generate insights is only half of the battle for financial institutions though.

Jim Marous (00:43):

Banks and credit unions must go beyond using data to create great reports to supporting exceptional experiences and engagement, what I call the final mile in the customer journey.

Jim Marous (00:56):

I'm excited to have Ed Maslaveckas, CEO of Bud Financial on the Banking Transformed Podcast. We'll be discussing the firm's expansion to the U.S. And how financial institutions of all sizes are leveraging data, insights, personalization, open banking, and banking as a service as strategic differentiators.

Jim Marous (01:17):

Starting out from the UK, Bud Financial has expanded into geographies like Australia and New Zealand, and has recently announced an expansion to the U.S. Bud's data intelligence models have supported BASS open banking efforts, lending deposit generation, wealth management, as well as onboarding processes and buy now, pay later initiatives.

Jim Marous (01:40):

In short, Bud Financials works with financial institutions worldwide to help them improve customer experiences and engagement.

Jim Marous (01:49):

So, Ed, before we get started, can you share a little bit about yourself as well as how Bud works with financial institutions to be positioned to be future-ready?

Ed Maslaveckas (01:58):

Thanks Jim, I don't think I could have said that first part better myself, probably that's what my marketing team will be asking me to be able to articulate for a while, so that was a great intro.

Ed Maslaveckas (02:06):

I guess the question around why do we exist? Why are we different to what's out there? We come from an open banking background and prior to being sort of "open banking player," we were a consumer app first for the first two years.

Ed Maslaveckas (02:30):

So, the focus of that app was around taking the transactional data that was in the bank account (this is before open banking, so 2015), taking that data and turning it into customer insights so customers could make better financial decisions. And those decisions were sometimes around budgeting and financial management, they sometimes were about financial products — what to take and what not to take.

Ed Maslaveckas (02:54):

And so, in building that early on, what we developed was early capability to take that data and really understand who the customer is. And if you fast-forward to today in how we work with financial institutions, our specialty is taking that same data and we can now accept formats from different aggregators, from core, from different cards, from OCR readers in lots of different countries, but the core is still the same.

Ed Maslaveckas (03:29):

That we take that bank data or the transaction data that someone spends and their income, and we understand who the customer is. So, we've taught machines how to understand transaction data. That's what we do, that's what we're best at.

Ed Maslaveckas (03:42):

And as you mentioned, there are ways that wealth managers, that credit bureaus, that lenders, that FinTechs can all use that in different ways. But I think that core capability is there was sort of a missing part of the puzzle. We talk around the industry and before, I was at Salesforce, we talked about 360 view of customer.

Ed Maslaveckas (04:07):

Well, in financial services, if you don't understand what's in the bank data, I don't think you even got 200 degree view or maybe even 100 degree view. That for us, was always the core problem and the core thing to unlock. So, really, in a nutshell, that's what we do. We understand that customer based off of that data. And believe it or not, that's a really hard problem and that's why not many people have solved for it.

Jim Marous (04:31):

Well, it's interesting too because I talk about the fact that the final mile, which is, it's one thing to get from data to insights, it's another to get from insights to implementation, actually using it.

Jim Marous (04:43):

I get frustrated because my consumer bank, my personal bank account, I know they know so much about me and I know if I went into their headquarters and said, "Show me everything you know," they'd show me just amazing data and insights about who I am, what I do, how I do it, where else I bank, all these things.

Jim Marous (05:03):

The problem is that final mile, that actually showing me what you know about me so that I value that relationship greater. How do you help organizations with that final mile? Not just going from data insights but from insights to let's say engagement and experiences?

Ed Maslaveckas (05:24):

Yeah, that's a good question, this is such a good point. So, I guess it really begins taking the core problem. So, the core problem is, if I was to go through your bank data Jim, I'm sure it'd be very enlightening. And as would it be if you went through mine, you would understand what's in there and I would understand and I could take a piece of paper or create an Excel sheet and create some insights on who you are.

Ed Maslaveckas (05:45):

And so, when we began this journey, we believed that the great and the good of the banks in the world had that capability at a machine level, that they had systems which could tell me really who you are and what you did. But the reality is a human can do it but there are deep challenges of allowing that solution to be scalable. And scalable beyond a one-on-one personal banker-type thing.

Ed Maslaveckas (06:17):

I have this old letter that was written to my mother a long time ago when she was just moving out of her parent's farm in the UK. And this letter talks about the nuance of her life, the fact that she's thinking of moving abroad and that she knows her mom and she spoke to her about that, she'd just finished college and she'd met this guy and all these details about her.

Ed Maslaveckas (06:46):

And that was a really personal experience and that was kind of the banking of old was this kind of awareness on who you are. And so, our job is maybe not to understand who your parents are, and that kind of thing. Our job is to teach machines what that transaction data means.

Ed Maslaveckas (07:04):

And to get to that — so like you talk about the insight to action, first of all, the question is how do you get to insight? I think a lot of people have talked about the fact that they have insight, but what's happened is people have not started from first principles and they've said, "Okay, how do we understand this person? How can we get insight on this person?"

Ed Maslaveckas (07:25):

And they've gone, "Okay, well we need to categorize, enrich the data. We make some sort of categorization capability when we make the insights." And so, there's a lot of vendors and people out there talking about, "Oh, we've got all these insights."

Ed Maslaveckas (07:37):

The reality is the decor enrichment, the categorization, the merchant identification, the classification, the geolocation, all the insights on that transaction data, typically, what we see is that that's done really poorly and it's just skipped over.

Ed Maslaveckas (07:55):

So, at best, people in the market have ... Let's say they have like 80% accuracy at understanding what that transaction is. That's kind of typically a good solution you see in the market, people might claim to have something else but when you do a benchmark, it's probably at best, 80%.

Ed Maslaveckas (08:12):

Well, that sounds good but the problem is as soon as you are trying to make an insight, create that insight on someone, it's not on one transaction, it's on 100, it's 200, 400 transactions. So, your error rate compounds so that 20% over two transactions become 40%, become 60%, and that's your error rate.

Ed Maslaveckas (08:33):

And so, this is why there's been so much talk about self-driving bank account, all these type of things, but the core problem hasn't been solved that actually machines don't understand really accurately enough where that transaction is.

Ed Maslaveckas (08:46):

So, we spent really the last five or six years — and Bud's been around for eight years, and really now, we've been able to grow because we spent five years and a lot of investment dollars fixing that one problem. So, now our models are at that core categorization level, are best in market. The latest models in the UK have got about 98%, which means the error rate's much much smaller so that the insights can be better.

Ed Maslaveckas (09:13):

So, when the insights are better, actually, the bit from insight to action is actually an easier problem once your insights are accurate. Because if you start to go to action and you've got a bad insight, then people have a bad experience.

Jim Marous (09:28):

Exactly.

Ed Maslaveckas (09:28):

Bad action, and that's the worst piece. So, that's why now we've been able to do a lot more on lending and actually, make people feel more comfortable with that. So, that's a very long answer to your question.

Jim Marous (09:40):

It's interesting because you've had a great deal of success in the UK and have recently expanded in Australia and New Zealand, but why did you decide to come to the U.S. Where the competitive marketplace as well as the regulatory environment are not necessarily very friendly?

Ed Maslaveckas (09:56):

Yeah, it's an important point. So, as a tech founder, the U.S. Has a huge draw but also it's a very scary place. There's a graveyard of European companies that have tried to come over here and had to retract. I think for us over the years, we have been asked by a number of large corporates and FinTech's, "Would they consider bringing your services over to the us?" And we've looked at a number of times.

Ed Maslaveckas (10:30):

And really, with our core capability being around the intelligence on transaction data we started to think, "Okay, could we build these models? Would they work?" We built the models last year over and we managed to get to quite a good degree of accuracy pretty quickly. And we've obviously invested in systems to be able to do that for years, and that's how we've done that in New Zealand, Australia. That's how we've done it in test markets in Europe with other languages.

Ed Maslaveckas (11:02):

So, we found, first of all, we could get our models to a pretty good degree of accuracy. So we started speaking to a number of different potential customers. What we found over here was that first of all, the U.S. Is a very competitive market, very dynamic.

Ed Maslaveckas (11:19):

And so, what that means is that a lot of organizations are very willing, if they have an edge, which with our services, you can create an edge by having greater accuracy. If you can offer someone an edge in lending, customer experience, personalization, then really, the buying market out here is much more dynamic and there's an opportunity there. So, that was one piece.

Ed Maslaveckas (11:42):

Second of all, because of the dynamic nature of the market, U.S. Organizations are very used to and adapt at using "alternative data." So, it's amazing if you look at the lending models over here, how much other data is used other than the bank data, way more than that's what's used in the UK with open banking, way more that's used with the open data regulation in Australia.

Ed Maslaveckas (12:04):

Those are sorts of crazy, maybe good crazy maybe weird models over here that are being used, but people weren't using the transaction data. And the reason why, my belief is first of all, it's that hard problem to solve. Second of all, a lot of aggregators over here had done some hard work and a lot of hard work at exposing the data and the core transformations have been done so you can get ahold of data now.

Ed Maslaveckas (12:29):

But that next step hasn't been taken because in this market, the screen scraping problem is so big, it's such an engineering challenge to get ahold of the data that that next level investment hasn't been taken.

Ed Maslaveckas (12:44):

Now, don't get me wrong, there are a number of startups now in the U.S. That are starting to do what we do. So, it's kind of a fun time to come across, the competition's emerging at the same time we're here. So, there was just a clear need here and a clear demand from customers.

Ed Maslaveckas (12:59):

So, it was a bit of a no-brainer for us and we're six months in from a sales mission. We've got our first few customers, we're engaged with a couple of different banks now, so the demand here is real. It's just making sure that we are present-minded enough to capitalize on that and not have our European or our UK heads on, we want to have a full American mindset and treat it from zero to one. We have no advantages based on our existing business, it's like starting a new business all over again.

Jim Marous (13:35):

So, that said, you talked about a lot of solution sets, anything from lending to wealth management, to buy now, pay later, things of this nature. We talked a little bit about this before we started the podcast, but organizations today have a really difficult time jumping into the water for economic environment situations for the scope of mindset and just having so many things going on at once.

Jim Marous (14:00):

Does your solution, is it compartmentalized where a person says, "I need to solve this part of my problem, not the whole thing," that you can come in and work on a specific solution set?

Ed Maslaveckas (14:13):

Yeah, so it's interesting, that's kind of where we've got to in the last few years including this localization within the U.S., so I'll just briefly talk on the different component pieces.

Ed Maslaveckas (14:23):

First of all, we had to solve that first challenge first, that was to get really accurate the data. Then, like you say, build the insights, the core insights to understand the customer, the income, their activities, their preferences, that kind of thing.

Ed Maslaveckas (14:40):

That then allowed us to build discreet systems and services. So, we can now do credit worthiness assessment. We have an automated process that completely does the end-to-end. And we even have our own dashboard that if it's flagged for a manual review, a bank can pick that up and they can have a manual review of that dashboard straightaway, so that's an end-to-end solution.

Ed Maslaveckas (15:05):

On the other side of where we can set up custom, what we call signals. So, if you want to push a notification insight to a customer, we can set those up, we can allow you to build your own. And that's kind of the steps we see is the transaction intelligence, the customer insight piece, which is those types of services individual assessment.

Ed Maslaveckas (15:30):

And then we go to the third step now, which we've only really just begun talking about but we've been building ever since we came here. And really what happened in this market very quickly was what we heard from lenders was, "Look, a lot of your services are helping me lend more but really right now, I want to understand two things. I want to understand how can I increase deposits, and how can I understand in this new market, this new environment if my customer's going to default before early?"

Ed Maslaveckas (15:59):

So, we've built something that we're calling portfolio level insight. So, we've gone from transaction level insight to customer insight, now to cross portfolio insight. So, what we can do is we integrate into the core of the bank. So, most of our solutions really are on first party data, not third party data. So, the first problem is sort my data out, then we can bring the world in.

Ed Maslaveckas (16:19):

So, what we can do now on that, is in real time, we can be monitoring the bank transactions of the customers and we can understand stack rank from 1 to 10,000 or 1 to 100,000. Show me the customers based off of these attributes that are most likely to default on an unsecured loan that I have. Show me the customers that are taken out more than two buy now, pay laters in the last three weeks.

Ed Maslaveckas (16:47):

All these custom segments we can now build and you can view them through our dashboard or your own dashboard, if you can use it by our set of APIs. You can segment those customers for risk which helps stopping default and you can understand the opportunities in real time.

Ed Maslaveckas (17:04):

Has a customer just had a big cash deposit land in their account? Have they sold their house? Have they changed their job? Or do we need to talk to them about their 401(k)? Are there opportunities to understand where my customers are banking and what products they might have so we can get a view on what types of other products they have by understanding the cash in and out?

Ed Maslaveckas (17:30):

And that gives me an opportunity as a bank to actually have that conversation with a customer and say, "Hey I noticed you do X, Y and Z. We have this product here that does that, does it better or it's a similar product but we're a local bank and we invest in the community. Is that something that you're interested in depositing that money with us?"

Ed Maslaveckas (17:51):

So, we've changed very quickly over here to act locally on those. And also, that's really helped us globally and thinking about the UK and Australia, we now have these portfolio views we can start bringing into banks over there and all that thinking was developed from conversations here.

Jim Marous (18:10):

So, this is a personal question, I guess on your journey, because when you started off with this product, it was really a great platform that I would say helped consumers bank better. You found all these opportunities, you helped them move the scenario and actually help them with their financial wellness journey.

Jim Marous (18:30):

That hasn't changed very much, but as we just discussed, you're now allowing organizations to compartmentalize your solutions and deploy them as needed. How hard was it for you to let go of the global view to allow the solution sets to be sold separately?

Ed Maslaveckas (18:53):

I think it came down to being pragmatic. We have this long-term vision of, we think that every customer interaction in the bank should be data-driven. And to do that, there are these different touchpoints.

Ed Maslaveckas (19:08):

One is around the lending, the credit worthiness piece. One is around the personalization, the cross-sell, the upsell. And one is around financial wellbeing and understanding of finances and making decisions.

Ed Maslaveckas (19:20):

And we used to try and sell the whole thing. And by selling the whole thing, it was too big of a problem for anyone to solve. It was too big of a problem for us to do the whole thing well. And equally, it's too hard of a problem for someone to buy the whole thing. You you're talking about a big old sales cycle, a big amount of belief in us as a company.

Jim Marous (19:46):

What's difficult about this, you sell just enough to give yourself pats in the back and say, "We did well," while you may not have optimized the sales potential?

Ed Maslaveckas (19:54):

Yes, that's it, and so, you really need to go deep to get to that full outcome, and so that's allowed us to scale by having individual solutions that are end-to-end point solutions, and then invest in the future, and invest in some of those other solutions more deeply.

Ed Maslaveckas (20:15):

So, yeah, we're picking off piece by piece. We're not going to do everything in the bank, but certainly from a data ... Like getting the data into a usable format so that different models and systems can use that data, that's really our game.

Ed Maslaveckas (20:31):

So, it's interesting, this next AI wave that's coming — we have certain AI tools that do different parts but there's a huge wave of AI products and services coming and the banks' not going to be able to take advantage of any of those, whether the AI services are built by Bud or by someone else, if they don't actually have a view and an ability to get hold of that data in a clean way right now. So, that's a big challenge that we're talking to people about.

Jim Marous (20:56):

You know, it's interesting, as I'm in the marketplace, every financial institution knows what they need to do, there's no mystery here. They also know how important personalization, data-driven insights, insights driving actual activity and engagement, they all know this, but the talk is so much greater than the walk. It's very hard for organizations to think outside the box and do things differently no matter how much common sense it makes because they're used to the old processes.

Jim Marous (21:27):

So, when implementing new business models, how can finance institutions from your perspective, unlock the greatest value? How is this different from what's been done in the past, and what do you see going forward? That's a big question.

Ed Maslaveckas (21:44):

Yeah, I mean, it's a big question. So, look, I think it's nice now, and as you mentioned that we can talk about end solutions because I think first of all, getting your data into a usable format was the first problem and we've solved for that. The next question is how do you put that into a broader strategy across the bank?

Ed Maslaveckas (22:10):

So, I don't think I've seen any institution be able to solve their data challenges outside of the product and the kind of product and business line silos they exist in. You've got secured, unsecured, even sometimes you've got your credit card, home loan, all those different silos.

Ed Maslaveckas (22:32):

I think I would love to be able to start to ... Like years ago we had these innovations functions that existed in banks and they were kind of off to the side. And what happened over time was they got brought into the bank and there's like a thin line across the different silos.

Ed Maslaveckas (22:47):

I think there should be a specific job now of data orchestration that sits across, and I think there are, but I don't know that that strategy has been pushed hard enough because that's the future of this. So, I think first of all, having a unified data strategy would be number one, and understanding that the same core data that you're using in any of those products or propositions can be leveraged elsewhere.

Ed Maslaveckas (23:13):

And that's what we get to with we've got these end solutions now, but ultimately, the core technology we use to understand the customer is the same. It's just we've built personalization tools, we've built lending tools. A bank should almost be a large version of what we are, is have that core customer capability or understanding and then have offshoots off that.

Ed Maslaveckas (23:40):

Now, we get into the realms of what the CRM implementation is of the bank, and I think that's going to change hugely in the next few years. And that's based on language models. So, what language models can do is they can pass through large data sets very quickly and get an understanding.

Ed Maslaveckas (24:01):

So, it's going to be interesting when you start to see what some of these new models are able to do rather than replacing manual CRM processes. The CRM of the future is going to be one which understands all that insight and can create that outcome and that's that action piece you're talking about.

Ed Maslaveckas (24:18):

So, they need to get ahold and understand their data first, that's it. Then they can speak to the great and the good of the actions and outcome providers.

Jim Marous (24:30):

So, we're going to take a short break to recognize our sponsors of this podcast, but then we're going to get back and talk a little bit about ChatGPT, Bud's transactional AI, a recent announcement you just made around Google, but also what do financial institutions do now. So, let's take a short break here and we'll get right back.

[Music Playing]

Jim Marous (24:51):

Welcome back to Banking Transformed. Today, I'm joined by Ed Maslaveckas, CEO of Bud Financial. We have been discussing the recent announcements of Bud's expansion to the U.S. And the opportunities and challenges being faced by financial institutions around developing and delivering personalized solutions.

Jim Marous (25:09):

So, right before the break, we talked a little bit about conversational AI both from the standpoint of ChatGPT but also, with regard to Bud's transactional AI. How do you see all these elements of the new wave of AI impacting what you are delivering to the marketplace but more importantly, what your data insights do to make this delivery even better than would be on its own?

Ed Maslaveckas (25:37):

I mean, in lots of ways, as any sort of technology founder, we couldn't have predicted how — as much as we've been in this world, we couldn't have predicted how quickly this GPT world and these language models are moving, it's really phenomenal. There's obviously a competition and innovator's dilemma that's held this stuff back, and now the floodgates have opened.

Ed Maslaveckas (26:03):

So, on the face of it, it looks like everyone's just figured something out and everything's just exploded at once. But you look at what Google's now released and they realize that now, the world is here and they've released a bunch of new AI tools and now you realize, "Hey, these guys, wow, I mean they're miles ahead." They're implementations are already phenomenal, and I guess we could talk about what we're doing over there in a second.

Ed Maslaveckas (26:35):

But for us, we're actually quite fortunate in the sense that actually all of our models, the way we enrich data is all language based. And so, what that means for us is, hey, that's how we've been able to create really accurate systems quite quickly.

Ed Maslaveckas (26:52):

So, we trained over here in a number of weeks and we got to a high degree of accuracy and the U.S. Is the hardest market. Language models, obviously, there's more English language than any on the internet. And so, that does help from a U.S. Perspective, but also there's more merchants here than anywhere else. So, that's the detractors you need to get a long tail of merchants to be accurate.

Ed Maslaveckas (27:19):

So, for us, language models are really important because when you get that string through of the messy data there's language elements in there. You and I could read something and say, okay, WAL, PT 26-5, like we could say, okay, that's the 26 ... We could see that was from Walmart and maybe PT, maybe that's petrol or gas or GA would be gas over here or something like that.

Ed Maslaveckas (27:51):

But again, I said at the top of the show, understanding that on a machine basis, you got to teach the machines to do that. So, for us, our systems are ready to be used by language models, that's what's great.

Ed Maslaveckas (28:08):

And also, a lot of what we do underneath our model is we do a lot of data labeling and training. And so, the opportunity for us is actually (maybe give some of a game away) — but we use a lot of human intervention, human labelers to train our models. Well, now some of these GPTs can actually train our models. So, not only are we the most accurate but today we think this is the next unlock for us. So, we're really excited about where that takes us next.

Ed Maslaveckas (28:37):

And then on the top, we've made all this data accessible, we've enriched it with our models. You couldn't create a real-time GPT model for transaction data because it's just not scalable to the billions of transactions, it's too heavy to call that every time.

Ed Maslaveckas (28:56):

So, you need a model in between which is what we've built, and then on top those actions then, GPTs and those types of models can be used to make the assessments on what's happening on the customer level. So, we're in a really nice sweet spot. But there are different threats that exist from these new pieces but we just have to leverage the tools better than anyone. That's kind of the way it's always been, I guess.

Jim Marous (29:23):

Does the future then maybe move a little bit away from product sales to actually engagement around financial wellness which will lead to product sales, but where there's a big conversational element that really can build engagement from the data.

Jim Marous (29:39):

So, going from data to an engagement and conversation maybe around, "Geez, I'm having problems with my balancing of my checkbook. I'm having challenges with getting credit right now because my credit rating has gone down," or anything of this nature, to then going into the product as opposed to being product-specific. This becomes much more customer-specific capabilities when you're looking at the power of ChatGPT, correct?

Ed Maslaveckas (30:04):

Yeah, you need to have that core understanding and it goes to what we were talking about before; the accuracy of the underlying data first and then the queries on top, yes. The queries on top can be resolved much quicker and easier. And so, you need some general purpose way to query that data.

Ed Maslaveckas (30:28):

I always find it though funny in the sense that when chatbots came around v1 — remember all these chatbots a couple years ago, it was the bane of my life actually, because we would go to VCs and they would say, "You should be building a chatbot, not what you're building." And we were like, "Okay, that's not our game. You don't understand the product, you don't understand what we're building, fine, but anyway, move on to the next investor."

Ed Maslaveckas (30:52):

So, with financial services, with numbers generally, I don't think ... Because that's what this is, you should do X because we see Y, that's what the GPT would say. But often sometimes, you need to show an image as well. So, I don't think that chatbots are necessarily going to replace all interfaces because graphical interfaces as humans, they tell us a lot.

Ed Maslaveckas (31:28):

You get a lot of information more quickly than you do through language. So, it's not going to replace all interfaces but it will augment some of the understanding and some of the solutionizing. They can start to get better at logic, basically, they can understand more parameters for logical outcomes. And that's, I think those optimization questions which used to take a huge amount of compute.

Ed Maslaveckas (31:54):

It's like the kayak or the whatever you're — when you were searching for your flights, that was always the challenge with those products, was that there was so much compute that went into telling you what flight to get. And these can do it in a much cheaper way.

Jim Marous (32:09):

So, I'm going to do a little bit of a pivot but not as much as I originally thought it would be. But you recently made a very important announcement that Bud was going to actually become the first UK FinTech to join the Google Cloud marketplace. What is the impact on your firm but more importantly, why should bankers care?

Ed Maslaveckas (32:27):

Well, I think bankers should care for lots of reasons, but I think if you want to come down to the brass tacks, us being on the Google marketplace, it really allows you to purchase Bud through your Google credits and your compute, so it makes that onboarding much easier. It sometimes makes it much more affordable and cheaper to acquire the services, so that's the first part.

Ed Maslaveckas (32:53):

For us, it's making sure now that with Google's go-to market and being a partner with them, we need to — I guess it's twofold. One, we needed to be a big enough outfit and have the scale and the capability to deal with the inflow from Google. They'll start to sell our product and we'll upscale their sales teams of how to talk about some of our services, and that will really bring us a lot of scale.

Ed Maslaveckas (33:22):

But again, for us, it was making sure that we were ready to handle that scale. Of course, any startup, you could take on any challenge, but you don't want to get it wrong because if you get it wrong, that opportunity's gone. So, we've been working really hard to get that right.

Ed Maslaveckas (33:38):

Part of that is being able to scale into any country quite quickly and be able to deal with the data from different countries and be able to deal with different organization sizes with different teams as well. So, that's kind of really the impact's more being on some technical but mainly on a sales, customer-delivery, customer-support type side.

Jim Marous (34:02):

So, speaking of sales and getting very tactical in this conversation, if an organization wants to work with you to build a better personalized solution for whatever their challenges are, what size of organizations are your sweet spot? I mean, do you work with small organizations? Do you work with mid-size organizations? Where have you found your sweet spot to be in? What do you think it's going to be in the U.S.?

Ed Maslaveckas (34:26):

Yeah, that's a good question and that's changed in the U.S. So, if you look at Bud, our first ever customer was HSBC, and then we signed a couple of other big banks after that as well. And so, I mean, that's never the typical way to do things, I wouldn't recommend that as a way to start your FinTech.

Ed Maslaveckas (34:47):

The consensus is start small and get bigger over time and that is the right way to do it. But we just happened to sign HSBC as our first customer and you're not going to say no, especially back then. So, what that meant was really from signing them, okay, we had to really figure out how to work with them, we had to scale. That took us a few years to get over and to make that work from our side and also, it was much earlier in this sort of FinTech- banker relationship.

Ed Maslaveckas (35:18):

Certainly, in the UK, we were one of the first to do it. It was more progressed over here than it was in the UK, the kind of FinTech banker thing. So, that meant we can deal with and work with companies of any size. We just also announced a partnership with TransUnion and they're now an investor, and we're going to market with them. So, we're able to deal with those size.

Ed Maslaveckas (35:42):

But equally, we work with very small startups. We work with seed round, series A startups. But really, our implementations fall into two groups. One, big bank implementation, you need a team behind that. You need to be solutionizing, you have a structure and a communication structure internally and externally of how you communicate with one another as to not waste too much of each other's time.

Ed Maslaveckas (36:11):

You have like information sharing exercises, but on the FinTechs, it's typically, "Here's our APIs, here's the contract, here's what we want to build, solution alignment and go." So, they're slightly two different motions. Now, how big the FinTech is and how big the bank is, it almost doesn't matter, they just act differently. You have customers like we have Credit Karma, they don't act like a big bank.

Ed Maslaveckas (36:42):

But my God, are they stringent on the technology? They are a great technology partner, but they act super quickly. They get things deployed quickly, and they just have a different motion. So, it's really those two motions. So, we can work with anyone. In the U.S. Right now, our focus is on banks over 10 billion in asset size, and FinTechs of any size.

Ed Maslaveckas (37:14):

Now, the banks over 10 billion right now, as you mentioned, I think a lot of banks herethey want to have these discrete problems and understand they have data access, we or them can access their data and we can solve these problems for them.

Ed Maslaveckas (37:32):

Whereas banks below 10 billion, some may, some may not. So, for us, it's the focus. There's a team of six of us here, and then the FinTechs for us here, the FinTechs here, we're signing them quite quickly. That for us is just getting our whole U.S. Motion going, getting the data flowing, getting the propositions going. That's what that's all about. And the big bank sales thing, those are sort of one to three-year projects as you will.

Jim Marous (38:02):

Okay, and for our final question, one I use quite often when we're talking to companies like yours; when you engage with a financial institution, be it any size, and they say, "We want to go forward," and it could be in the UK, it could be anywhere, but in the U.S. Right now, or in the UK, when somebody says, "Let's go forward," what gets in the way of things working the way you envisioned it when you signed the contract?

Ed Maslaveckas (38:29):

So, first of all, getting to sign the contract is half the battle, so that was what we talked about, is we used to go really big, sell this dream. We had this dream that we were selling and we realized actually that just took too long to get to the contract. And so, once you're in and you've got the MSA in place and you've got a way of working together, then you can do other things.

Ed Maslaveckas (38:52):

So, what blocks you from post-contract? I guess it's not having a pre-alignment in terms of what's the business case, what's the clear ROI that we're both driving towards? Because if we're driving towards two different ROIs, then there's a mishmash, things get lost, and also, the solutions can get deprioritized.

Ed Maslaveckas (39:19):

There's not a clear budget or there was, "Yeah, we need to do this transformation thing and we want to make our app look better," great. As soon as anything happens, like what's happened recently in the financial market, in the bank space over here, well, that project's cut because it's not aligned to an ROI or a key objective in the P&L. So, I think that's the most important thing.

Ed Maslaveckas (39:41):

And that's where we often took some missteps in sort of 2019, 2020 was, hey, we were in this wave of the challenger banks are coming, the challenger banks are coming, look at their customer experiences. We need to improve your customer experiences.

Ed Maslaveckas (40:00):

And that was great, we could do that, but there wasn't a clear ROI at that point. And so, someone decides that they need to fix the mortgage process because that has a clear ROI, then you get deprioritized. So, it's making sure we have those.

[Music Playing]

Jim Marous (40:18):

Ed, thank you so much for being on the show today, I really appreciate talking to you. Congratulations on all the things you're doing at once, it seems; going to the U.S., working with Google, and then everything in the marketplace changes daily. So, I know that's a challenge in of itself.

Jim Marous (40:34):

I think we wake up every morning going, "Okay, we'll peak to see what the top news stories are, and hopefully, it's not banking this time." So, thank you again.

Ed Maslaveckas (40:42):

Absolutely.

Jim Marous (40:42):

I look forward to seeing you in Amsterdam.

Ed Maslaveckas (40:46):

Yeah, you too, take care. Thanks for having me.

Jim Marous (40:50):

Thanks for listening to Banking Transformed, winner of three international awards for podcast excellence. If you enjoy what we're doing, please take 30 to 45 seconds to show some love in the form of a review. Finally, be sure to catch the recent articles I've done for The Financial Brand, and check out the research we're doing for the Digital Bank Report.

Jim Marous (41:08):

This has been a production of Evergreen Podcasts. A special thank you to our senior producer, Leah Haslage; audio engineer, Sean Rule-Hoffman, and video producer Will Pritts. I'm your host, Jim Marous.

Jim Marous (41:19):

Until next time, remember: the real power of data intelligence is to make the life better for your customers.

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