AI-Powered Growth Strategies for Modern Banking

Jim Marous (00:12):

Welcome to Banking Transformed, the top podcast in retail banking. I'm your host, Jim Marous. Despite having established customer bases and trusted brands, many financial institutions struggle to keep pace with the rapid technological evolution driven by fintech firms and digital first challengers, the potential of AI to transform banking operations, enhance customer experiences and unlock new revenue streams is evident, yet implementation continues to be a significant struggle.

Jim Marous (00:45):

I'm joined in the Banking Transformed Podcast by Marc Corbett, Director of Solutions Engineering, Americas for Backbase to discuss one of the most pressing challenges facing retail banks today, effectively deploying AI solutions to drive growth. We'll explore how retail banks can effectively overcome implementation challenges, harness AI technologies and transition from reactive to proactive business model.

Jim Marous (01:14):

Marc brings valuable insights on navigating this complex landscape to unlock what Backbase calls the growth mode a state where banks can fully leverage AI to drive sustainable competitive advantage in today's hyper competitive marketplace.

Jim Marous (01:32):

Legacy systems, fragmented data architecture and organizational silos create substantial barriers for AI adoption and banking. Many banks invest heavily in AI initiatives but see limited returns as they grapple with integrating these new capabilities into an existing infrastructure. Today, we're going to discuss how to address these hurdles and to achieve growth. So, Marc, before diving into specifics, could you share a little bit about your role at Backbase?

Marc Corbett (02:00):

Yeah. Hi, Marc Corbett, I'm the Director of Solutions Engineering Americas. My team works with a lot of our future clients and existing customers on the digital transformation journey.

Marc Corbett (02:12):

So, understanding the landscape of emerging technologies, what they have in their roadmap, what they're trying to achieve with a platform like ours and then we do a lot of the technical stuff too, like earmarking legacy systems, deprecating and evolving the underlining architecture and some of the silos that they have. So, it's a very fun, it's a very evolving job, and it stays on the precipice of what's next in the technological world that we live in.

Jim Marous (02:41):

Well, we just finished up our Financial Brand Forum in Vegas last week and there's certain words or certain phrases that became everywhere. It's been a couple years now that AI is just on everybody's tip of the tongue. But I see as I'm talking to organizations on the podcast, I visited with them at our event last week, there's a little difference between the talk and the walk and we're obviously seeing a lot of disruption in banking.

Jim Marous (03:10):

We're seeing that traditional banks primary challenges are around competing with fintechs and digital first challengers, but it really isn't taking AI from my perspective, taking AI from the back office solutions, the things that create efficiencies to the front office solutions where it really improves effectiveness, improves the customer experience, improves the employee experience. So, what are the challenges that you’re seeing the most when financial institutions are trying to really deploy AI?

Marc Corbett (03:43):

I think it's the same song and dance that we've seen throughout the last decade while everybody's been focusing on let's get to the next modernization of our software stack, let's bring an application to our customers. We've seen the evolution of Web 2.0 and then into mobile and now AI's a new tool, but it doesn't mean that the landscape's changed.

Marc Corbett (04:05):

So, I still see the same problems I saw a decade ago when I was first starting at Backbase which is legacy systems, fragmented data, organizational silos, business and technology aren't speaking, aren't working as one, agility and speed is always going to be probably the largest cog in the wheel. I think when we get into some of these transformations and journeys and customer expectations, are we really synchronizing the data set that we have access to and meeting our customers where they are?

Marc Corbett (04:34):

Because Jim, you were talking about it, when you're on the Banking Reinvented Podcast over here in Amsterdam, it's a user who's jumping into an Uber. They're not just getting a ride experience anymore, they're being brought a lot of services along the way, same thing with Amazon and many of the other vendors in the space, so why can't that FI experience be any different?

Jim Marous (04:55):

Legacy technology is often cited as a major barrier to innovation. What we're seeing is organizations now are trying to find different ways to modernize their banking core, but also just modernizing banking as a whole. How are you seeing forward-thinking banks addressing the challenge to enable AI integration?

Marc Corbett (05:15):

I think, you said this best when I was listening to you the other day, that the rope analogy. So, time has passed, institutions have not modernized that technology the way they have, they're holding onto the rope, it's getting further away, there's a lot of catching up that needs to be done.

Marc Corbett (05:31):

But at the same time Rome wasn't built in a day, we need to gradually replace outdated systems, and it just starts with laying everything out, sometimes on a spreadsheet, we do it with our consultants a lot and just saying, "What are you using? Where's the redundancies? How can we look at this from a modularity standpoint and actually use this point solution, this integration pattern more than once across your network and get away from that risky big bang." So, that progressive modernization, I think, is huge.

Marc Corbett (06:00):

API first architecture is another thing that we always focus on which is shockingly still very much of something that gets in the way of kicking off, an implementation kicking off a strategy because that API readiness has to be there.

Marc Corbett (06:15):

Cloud adoption's finally there. I feel like there was that reluctancy over the years, finally, we've gotten rid of the badge cloud vibes, and more importantly that feels like that was something behind us now. And while we see more cloud adoption that brings data like strategies in, that brings additional partnering with fintechs in because now you have a more agile, more lean architecture that is somewhat replaceable in some aspects and not all on-prem.

Jim Marous (06:46):

So, we talk about the growth mode, certainly Backbase has talked a lot about achieving that growth mentality, that growth mode. And we heard at the Financial Brand Forum last week that every organization is saying they need to acquire new customers, they need to grow organically, that's really central to banking transformation is it's actually the way you pay for it in many ways. Can you explain a little bit about what the growth mode is and why it's so critical today?

Marc Corbett (07:16):

So, AI is a big piece of this, we're using AI power platform now to push this but once again, AI is just a part of that larger strategy. Our banking leadership is kind of looked at it and called it growth mode because we need to get away from a slow, disconnected legacy tech staff that kind of handcuffs our ambitions, handcuffs our strategies to a more nimble all in one and this is where we come in platform play.

Marc Corbett (07:44):

We believe that a single architecture, single code base, getting away from all of this merger and acquisition of technology products while you're actually going through that from the financial institution space and unify that data experience is huge. That allows us to be more proactive, it allows us to find that data layer and we're putting an AI layer as well into a lot of our capabilities so that we can tailor experiences.

Marc Corbett (08:12):

I think you were saying it recently, Jim as well, tailoring that value proposition is huge. If you think about different users at different lifecycle events and different places in their life, I have a mortgage with another FI, why isn't my primary FI offering me a mortgage solution?

Marc Corbett (08:32):

I remember when I first renovated my home, I had to go to a kind of a nimble neobank to get that financial backing because my bank wouldn't give it to me, why is that? Why is that relationship so broken? So, tailoring those experiences only can come from disconnecting those legacy experiences and getting into that nimble all-in platform suite play.

Jim Marous (08:56):

It's interesting, we were talking before we went on air about the fact that organizations now more than ever really have to define their specific north star they're trying to solve for, that may not be at north star for the organization as a whole but it's certainly at north star with regard to what they're trying to solve for today.

Jim Marous (09:15):

You're traveling the U.S., you travel globally to find out how are organizations, what are they focusing on? What are they trying to deploy AI against? What are some of those strategies? What are some of those solutions that they're trying to deploy AI to make it better?

Marc Corbett (09:32):

I think what we're seeing is there's a lot of waste, there's a lot of redundancy, whether it's the back-office, redundancy of systems, data not getting where it needs to get. So, where we see maybe AI solutions in the marketplace right now are simple low hanging fruit, those large language models being trained, being able to synchronize, synthesize that data set but more so in the future.

Marc Corbett (09:59):

And how we're trying to curve this is don't solve tomorrow's problems with another point solution even if it's AI. Think about an AI data layer throughout your entire organization. Let's put aside how technically you can make that happen because that's a whole podcast in itself.

Marc Corbett (10:15):

But once you get there, how do you take that approach from a strategic technology assessment and say, "Okay, let's prioritize this data set. Let's make sure that our customer service representatives are speaking the same languages that our advisors and they can help migrate users from a consumer plus business experience into a graduation process of commercial and lending and we can keep them in that ecosystem."

Marc Corbett (10:38):

If we're not talking the same language from a data set, we're going to lose out on a lot of those opportunities for our customers and I think that's where AI's really helping us strategize and move quickly in the marketplace.

Jim Marous (10:51):

So, is most of what you're seeing still really back-office modernization, trying to take costs out of the equation, traditional banking mentality or are they starting to open the doors towards what we talk about a lot, but don't deploy very much, which are customer facing opportunities, be it in the consumer small business or commercial banking areas? Are we starting to see at least for the most progressive organizations some strategies that are being deployed to make banking better for those who they serve?

Marc Corbett (11:22):

I think so. So, there's something we've been doing and focusing on, it's called Customer Lifecycle Orchestration and it's really trying to increase the product holdings per individuals so increasing that ratio. You hear about it a lot, but how do you do it better?

Marc Corbett (11:37):

And I think that AI, while it's going to help us clean up the back office massively and repurpose a lot of the workflows so that you can get into those digital insights and those high digital touch points that you want your member representatives to be facilitating versus just the minutia of finding and searching, the same goes for the front end in the front office where I'm a client, I log in as the customer and there's kind of different algorithms that are running based on my life events that are giving me the right campaigns that are guiding me through first financial management flows and wellness flows.

Marc Corbett (12:14):

There's a system that needs to be in place that's actually dynamically changing my dashboard and prioritizing family centric banking versus my old life when I was a single person who was just managing a small portfolio. Maybe I want to bring an IRA into my banking experience as well, which could be the gateway into some of the wealth and advisory services that that FI offers.

Marc Corbett (12:38):

These are things that we're thinking about now instead of domain specific solutions from a vendor assessment where we see in the market those RFPs go out, we've got one thing we're trying to solve, onboarding.

Marc Corbett (12:49):

Well, have you thought about the life cycle of that onboarding? Have you thought about the product acquisition phase after you start servicing that customer or that member? This is where AI gets into the mix in our opinion, and this is where we're taking it in the next few years, certainly.

Jim Marous (13:04):

Data unification and bringing the silos together is actually foundational to AI success otherwise you're just building silos of insight which doesn't help you nearly as much. What steps are banks taking today to break down these data silos without a massive infrastructure overhaul?

Marc Corbett (13:25):

The steps that they're taking, I think is first of all, making sure everybody's at the table. I think you said this the best, making sure that usually when you go through an assessment like this, there's a lot of risk involved, we're working with risk mitigators, which are banks, essentially member credit unions.

Marc Corbett (13:43):

So, bringing everybody to the table, from the compliance, the architecture, the technology, understanding and getting them to kind of identify the pain so that you can start to organize that internally. Because likewise, we have a solution for everything, but it's hard sometimes to organize that on our end as well was what is priority for the institution?

Marc Corbett (14:04):

I think Ford did this really well back in the early 2000s. They found that after the subprime mortgage crisis, all of a sudden, they had a different company culture of admitting when something was wrong on the assembly line and they would raise their hand.

Marc Corbett (14:20):

We need banks to do that too because that attention needs to be given to those systems, it's not just good enough. So, it's very much a business-driven initiative as much as it is technology, even though that's the framework that we're taking it from.

Jim Marous (14:34):

Whenever I'm talking to somebody about AI, we're talking a lot about improving the information we have in the back office, making it so that we can understand our customers better, understand our clients better and that helps a lot even in the democratization of data across all the touch points, that really helps out a lot.

Jim Marous (14:56):

How do we move that so that the customer knows that they're being known? Yeah, I talk about it often that Amazon shows me every day that they know me better than anybody else in the retail sector. Uber knows me really well and knows what I do beyond the car sharing. It goes into what I like to visit when I'm out of town, where I like to go, what I like to eat, all these elements.

Jim Marous (15:24):

But the fact is, in my banking environment, it seems like risk tolerance gets in the way that my primary personal bank, my primary small business bank want to know me really well but really get into a discomfort zone when it comes to making sure that I know they know me because they're afraid that well, maybe they'll be wrong.

Jim Marous (15:48):

Does that get in the way with most of your clients where they get to know their customers extraordinarily well, but when it goes against deploying that, let's call it, and it's using a current cliche, Agentic AI mentality that says, "I will help you down that path." Is it more than just risk that gets in the way? Are they afraid of the data that they're getting, the answers they're getting, even though they're using them internally?

Marc Corbett (16:12):

That's a really good question. It's funny, there's nothing scarier than talking about Starbucks or something, and that it's on your Instagram feed like five seconds later and you're saying like, "Is my phone listening to me?" The reality is, while there's a debate about this, what it's really doing is it's cross analyzing the IP that I'm on, it's looking at who is in my proximity, it's analyzing the friendships that I have because they're also seeing ads that are specific to that infrastructure, that ad environment and so that's where that hyper-personalization comes into play, it's almost too good.

Marc Corbett (16:45):

So, I think there is that aspect but ultimately, we think that when we look at the industry, these banks have foundationally built their relationships, especially in the customer financial institution space, even into the sub-regional space, based entirely on relationships themselves, intimately knowing our customers. Hyper-personalization is not a new concept, it just used to occur in branch, it used to occur in person with that white glove feel.

Marc Corbett (17:12):

Obviously, that's not scalable, but that hyper-personalization via AI to analyze vast amounts of data, real time to understand that customer's needs, and then preferences leading into a highly tailored product recommendation, I don't see that as a risk as much as a helpful hint into the future. And like you said, they're going to receive it anyways from external applications, so what's the difference?

Marc Corbett (17:36):

That proactive engagement for me personally is where I see us predicting customer needs, triggering their perspectives, enhancing their services, increased customer lifecycle value over time so that it's even harder to leave things that we're doing with a completely built in family application where I'm ramping up my child now within the same FI giving them access to a virtual credit card.

Marc Corbett (18:00):

It's not a rapidly new concept, but it's real time and it communicates now both with downstream, with my system of record and that engagement layer, and therefore I trust it. And I think that's really building that trust model long term is the only way to retain these customers, otherwise they're going to go elsewhere to Venmo to Square Cash.

Jim Marous (18:19):

You say it so well because I think that instead of breaking down the trust, I think the customer is becoming much more tolerant of what I'll call mistakes. So, my son and his new bride are living with us for a few months and I see that my Hulu is all screwed up now because it's all got the things he likes watching as opposed to things that I like watching and so I got to search for the shows that I was used to watching.

Jim Marous (18:45):

But you know what, I think any human today is going to understand why that happened. We just went through a wedding and so everything that was purchased at Amazon, had to do with the celebration and different elements of the wedding and most recently, not with the same people, but a baby shower, my wife just put on for a relative of ours, we realize that certain things are being offered to us that may not be any longer applicable.

Jim Marous (19:12):

And I think financial institutions really have to understand that, as you mentioned, the way to build trust is to show that we're looking out for them, that we're trying to solve their problems and that every once in a while, we'll have a mismatch, but that's not the end of the world.

Jim Marous (19:28):

And in fact, with AI, you could actually tag those households or those people that may not feel comfortable in this zone. So, you just say, “You know what, we're not going to hyper-personalized on this person, they just don't like it,” and it may be different within a household.

Jim Marous (19:43):

I know my wife would feel a lot less comfortable than I will because I understand where it's coming from. I kind of get jazzed by the attempts at times on Instagram to post something that's going to apply to what I was looking at somewhere else during the same day. I think it's important because we're not going to be able to achieve the massive opportunities in AI unless we embrace some of the opportunities it gives us.

Jim Marous (20:10):

And we can't just ignore them because as you mentioned, the customer now knows you can do this. So, if you don't do it, if you don't start combining outside information with inside information, they're going to feel like, “You know what, I got to look for those alternatives that understand me better.”

Jim Marous (20:28):

I did that with my business bank where I all of a sudden realized that PayPal knows me a whole lot better than they do as far as how I run my business. It wasn't like it was not available to my traditional financial institution, they just didn't do it.

Jim Marous (20:44)

How do you see in the future this really impacting because we're talking about the growth mode, not just the acquisition of new customers, but really improving the customer lifetime value over the lifetime of the relationship.

Marc Corbett (20:57):

Yeah, no, so I want to touch on something you said right there really quick as well which is the culture of experimentation. I think that there's this culture of experimentation that's occurring within our institution. I think a lot of vendors are doing it, which is how can AI transform our landscape? Because I think our CEO, Jouk, said it the best, it's like the light bulb, you can't get away from it, it's going to change the whole world, whether you like it or not.

Marc Corbett (21:22):

It's going to change the way that we operate and interact with one another Agentic or not. And putting that AI facility and data layer into your application set is going to enable you for future opportunities. So, it's just a new factor, it's a new tool set, but it dramatically changes the landscape.

Marc Corbett (21:40):

So, back to the future, from that context, you can look at a lot of white papers out there that talk about AI 2027, the experimentation of Agentic AI in the last year you were saying on our podcast 2022 is when this stuff started occurring and now here we are only two and a half years later and look at the transformation it's had, not just on your LinkedIn feed, but in the entire industry.

Marc Corbett (22:06):

I think hyper-personalization customer experiences is something that we will see on a level that is unbeknownst to us at this moment from a banking landscape.

Marc Corbett (22:16):

Even the Chases, the USAs of the world are going to struggle to keep up with the agility of that because even them of their massive size will have to build in-house teams to change the way they architect in order to deliver that instantaneous kind of agile environment where you can give a different demographic, a different audience, a completely different interface based on that hyper-personalization and that's where you win if you have an open architecture.

Marc Corbett (22:43):

AI-Powered financial wellness is going to be huge, we already see it with the rocket monies, you've talked about using Acorn, now it's how do we combine those well-known tools into the tooling landscape of knowing our customers real time, delivering that data in the same breath and then there's that intelligent automation of customer services as well.

Marc Corbett (23:03):

So, I think you still get to the point where you need help, you have a dispute, you have a transaction issue, and it's how can we get ahead of that? Because that's massively expensive for the institution managing those disputes.

Marc Corbett (23:16):

And there's vendors out there today I think who watch those disputes try to get ahead of them so that they can say, “Actually this is a subscription, I can help you with the management, but AI's going to deliver more of those customer services automatically. You're going to be less on the phone and more with prompts to those kind of socializing engines within the application itself.”

Marc Corbett (23:38):

We've built an AI assistant already within our mobile apps, huge amount of use cases that you can apply that to and then ultimately innovation. I think the innovation we saw for the last decade will seem like a blip relative to what we're going to see in the future.

Jim Marous (23:54):

And innovation that's not just an enhancement to a current product, but maybe the elimination of the way we look at the product set. I think that overall, the checking count is in many cases no longer the primary financial relationship.

Jim Marous (24:10):

It is in some cases, but there's many people. My son for one who I'd say it's a Venmo plus his credit card and his debit card because he uses his cards more than anything and it's a credit relationship right now for him as opposed to the … he's never written a check in his life and never had a check in his life so that is not the relationship or the engagement function that it was before.

Jim Marous (24:37):

In addition, I think the actual collapsing of channels so that they all work with a common, as you mentioned earlier, a common basis of information where they're acting the same, they're not re-asking questions.

Jim Marous (24:52):

In addition, when you look at the lifetime value, the ability to say with AI, with the technology we have today, with the integration of services, the ability to add a service simply with a one click, because you have all the information.

Jim Marous (25:07):

Yes, we need it in different silos within the product set, but the reality is if we hold it within the organization, we've got to make it so they can work with each other seamlessly in a flawless way, otherwise we're going to lose that customer. Backbase has really done a lot with regard to expanding the realm of how AI and how the growth model can really be achieved.

Jim Marous (25:31):

We talked on one of the other podcasts within this series around commercial banking that is the key focus area and it's actually commercial and small business I'm going to use interchangeably because they both leverage services beyond what the consumer usually uses. What specific AI applications are showing the most promise in the commercial and the small business set?

Marc Corbett (25:54):

I think first and foremost with the commercial landscape, you're so right. So, you could talk to a small medium business segment in a larger institution that's 50 billion in assets and above, and that's actually commercial banking to a CFI. So, it's a broad scope and it really depends on the functionality that we're offering up to the end user.

Marc Corbett (26:16):

What fascinates me about commercial and commercial lending is talk about obsolete, archaic legacy ways of working with such a new business model every day as well. It's such an interesting juxtaposition of two factories where you have revolutionary growth and at the same time, we haven't changed a lot of how we service those users.

Marc Corbett (26:38):

And I think a lot of the automation, a lot of the ways that we offer up products is so credit risk assessments for lines of credit personalization of relationship management, your relationship managers and how they communicate with you, how they recognize your entities and sub entities automation of some of the entitlements within your system, fraud detection is massive.

Marc Corbett (27:01):

So, we have a lot of the hooks already in our software where there's a step up, there's a real time payment and it exceeds a certain amount. You need to use biometrics, you get a push or a nudge to a mobile device, someone's on the go.

Marc Corbett (27:15):

But then embedding fraud and AI into that to see how many clicks are we copying and pasting that password, what's the behavioral data of that user and is it unlike what we've seen before from that same user? And automating that process is huge.

Marc Corbett (27:31):

Then there's document processing, cashflow forecasting, all these things that have traditionally been very manual, done on spreadsheets or aggregating external systems, now we can kind of interwind those into a single experience and then give the customer once again what they need when they need it versus throwing a million functional areas at them and expecting them to be a subject matter expert on my commercial platform.

Jim Marous (27:56):

It's going to be interesting too because with AI and with the ability to track behaviors within a commercial relationship, there's a lot of owners of that relationship. The payroll manager is not at all like the financial expert within the organization, is not like the owner of the organization.

Jim Marous (28:13):

So, to understand the way they want to bank how they want to interact will be just a tremendous opening of opportunity in addition to be able to look at not just fraud and risk but let's take the opposite side of that with regards to credit availability.

Jim Marous (28:30):

Where, because of behaviors, because of things going on at the financial institution level that you're able to say, "I can offer this organization or this person X lines of credit because of the way they're behaving outside of what I'll call credit bureau related activities."

Jim Marous (28:48):

We're seeing this, Chime just released a notification that they're going to be offering a number of their households of 500 line of credit that was not based on credit bureau adjudication, which means they're probably going deeper into the relationship base to say, “You know what, these people may not have qualified in the traditional ways, but because of the way they react to us, because of other data we have availability, they're going to be available.”

Jim Marous (29:18):

I think this is really the opportunity that if we take it, it'll be tremendous. And I think we have to get out of our own way at times we have to look and say, "Why is it that our personal lines of credit at X, Y, Z organization only go to $1,000, why not down to 100?"

Jim Marous (29:35):

Because if the mass amount of my base would benefit from the security of knowing that they have a line of credit that can be accessed if they miss a payment or if rent comes due and they don't want an SF fee to hit their account that we have access to credit that can get them over that small hump.

Jim Marous (29:55):

I think it gets to a whole new way of looking at relationship banking in a way that's really forward looking as opposed to simply looking what's happened already?

Marc Corbett (30:07):

Neos are doing that, back to your point, we see these disruptors doing that and the institutions that we work with are looking and saying, should we offer that? How radical would it be for us to either set up a sidecar and rebrand it with the existing infrastructure so there has to be reusability there or can we embed that within our application set as an added service like we've done now internally with roundups that Acorn started doing years ago.

Marc Corbett (30:33):

And I think you're right; you have to catch those trends, you have to reassess your risk assessment and more importantly, the behavioral data's huge. We really structure everything on data itself, but the behavior, people used to walk into branches and their appearance, the way they carry themselves might actually earn them more trust in terms of a business loan or a line of credit, like you're saying.

Marc Corbett (30:55):

And there's this aspect of we look everything via binary and in a traditional model we need to expand that data outreach and understand where else is there financial assessments going on. Additionally, security-based lending and wealth advisory and all these things, they're all coming together, even though we're talking about consumer all the way up to a higher net worth individual, they have very similar experiences when it comes to seeking out those high digital touchpoints.

Jim Marous (31:22):

It's interesting because there's certain things that hit me and then I ask it everywhere to find out is my thesis correct? And one of the challenges I'm having right now is the limited number of organizations that look at flow of funds, that look at where do funds go once they get into an account of one of my households and not realizing, for instance, that in my instance I'm transferring money twice a month between financial institutions, well, why is that?

Jim Marous (31:48):

Why doesn't anybody try to peel back that layer or the fact that I'm paying a mortgage on a monthly base that may be overpriced based on the very easy dynamics. You can look at, when was my mortgage opened? What's my payment amount, what's my outstanding balance? You can pretty much figure out what rate I'm paying.

Jim Marous (32:06):

Well, if it's higher than what is market today, why are you not reaching out to me and offering me something better or if you actually hold the mortgage, why are you still doing that? To actually show people that you're looking out for them on an ongoing basis.

Jim Marous (32:22):

As you know, I talk a lot about my Acorns account, but I go I'm having money taken out all the time by Acorns and yet not one financial institution going, "By the way, we can build something like this for you." I'd probably move my account but nobody ever does it. So, lost opportunities by not looking at what’s going on overall.

Jim Marous (32:41):

Even the simple dynamics of when a customer's shopping for a new car, they have a credit bureau run against them when they try to do a test drive, that's their way of saying, "I can feel secure and you taking a car off the lot without me in the car."

Jim Marous (32:55):

Well, when they do that, you have a strike and you can find out who's shopping for cars. Well, that's pretty valuable data. The dealers and the manufacturers use it all the time to go after you before you actually buy a vehicle but my financial institutions ignore that even though they could finance that vehicle.

Jim Marous (33:13):

When we look at AI capabilities today and we look at the use of data to build better insights, what balance do you believe financial institutions should strike between developing the AI capabilities in-house versus partnering with technology providers and is this defined only by size of the organization or more than that?

Marc Corbett (33:39):

So, this is going to be contentious because I'm going to be biased as a provider of a platform so take it with a grain of salt. But we've looked at dozens of institutions who've built in-house capabilities and institutions who've acquired in-house capabilities from vendors in like a single source of truth kind of way in terms of, I have one problem, I need a point solution, let's put it in place.

Marc Corbett (34:04):

And time and time again, what we see is when you take that approach, you may end up with unwanted tech debt. There's the innovation and the knowledge that you need internally about AI, but I think you should use that in your assessment.

Marc Corbett (34:18):

And where we come from the perspective is let us be the kind of software development efficiency that you're looking for, let us bring the platform in and then you make those holistic choices now that you have someone who's basically funding an AI initiative within your organization from a larger group and team globally.

Marc Corbett (34:37):

So, we use Backbase’s infrastructure AI capabilities now for upgrade agents, making sure that we get synthetic test data, pull request classifiers and things like that on just the SDLC and that cost really drives down from a software development lifecycle standpoint so you get productivity gains. But ultimately what we're really driving towards is you come to us and you say, "Hey Backbase, what's in your roadmap?"

Marc Corbett (35:02):

And we show you what's in there and say, "Well actually I'd like to develop a feature faster than that." Okay, well let's give you the keys to the castle and open it up. It's powered by the Agentic AI, there's that AI and data layer below it so now you can use your own sources in order to build and expand on that roadmap and potentially even partner with it if it's an initiative that we see fit.

Marc Corbett (35:24):

This is a good, I think, harmonious relationship we have with a lot of our even smaller CFIs in the space who want to take that risk but don't want to necessarily hire 15 AI developers or people in the market who don't really want to take on that risk in general about the management and cost of that tool, cost ownership over the next five years.

Jim Marous (35:46):

Well, it's interesting because it's not just the risk, it's the reality that solution providers out there have had to invest in AI developers, coders, everything that has to do with AI right now in order to be competitive. And we can take the smallest solutions set out there and the providers that are really focusing on their realm of influence are doubling and tripling down to make sure that they're going to get it right because they need to stay in business.

Jim Marous (36:15):

Why not partner with them who've already gone through all the trials and tribulations, they've already gone through the detours and the shortcuts that are necessary with other organizations like mine. Why do I need to get all these people with these specialties when they're out there already?

Jim Marous (36:34):

What's exciting is, I've said it before, is that the most innovation, the most progressive organization right now are the biggest and the smallest because the biggest have the funds, the smallest actually have the ability to partner with some of the most progressive organizations like Backbase that are out there that can support their efforts in either a complete overall positioning or in very segmented ways to hit their north star, which is pretty exciting.

Jim Marous (37:02):

So, you've been in the business for a while, you've been at Backbase for a while, what excites you about the future with regard to AI growth strategies?

Marc Corbett (37:12):

I mean, there's a lot, I think this has created a completely new landscape that I thought that maybe wasn't going to be available to us in the next five years. Whether that's internally, like I was mentioning earlier, that culture shift and the organization where we're saying, "Okay, Jouk's done an incredible job at this, let's embrace AI fully.”

Marc Corbett (37:31):

Let's become an AI powered platform versus a platform that tax on AI as a feature, let's use it to synthesize and clean up data sets. Let's use it to create dynamic documentation and reports. Let's do all of that at a factor of two to three X through which we were normally going to do it.

Marc Corbett (37:52):

I think it's a great, exciting time to be in tech in general, that's leveraging some of these technologies because it's opening up possibilities to us that we've never seen before. And more so I've always been proud of the way we've orchestrated the platform, completely open AI or open APIs, completely in-house built, we haven't grown through acquisitions or mergers of smaller fintechs that means we've always had a very clean set of code.

Marc Corbett (38:20):

It's a platform that if you're a nerd and you're a developer like me and you take a look at the backend you get really excited to work with, now that that new technology is coming in, we're even more likely to adopt it at a speed faster than other vendors in the marketplace because they don't have to sift through hardcoded acquired libraries that they've had over the last 5 to 10 years. Now that excites me because I think we can really become a market leader in that space.

Jim Marous (38:44):

Marc, thank you so much for being on the show today. I'm excited also, I was talking this to the people in the audience over the last week when we were in Vegas at Financial Brand Forum that I've been in banking my whole career, which is a long time. I don't think I've ever had a time when the opportunity wasn't greater, when the excitement wasn't higher, when the customers weren't more excited about what we can do.

[Music Playing]

Jim Marous (39:09):

And really, it's up to us as individual financial institutions decide, "Okay, you're going to grab that, are you going to grab that next rope on the rope course or are you going to sit there with a still rope going, “Now how the heck do I get over that?" We've seen all those TV shows where they have all the obstacles. I'm not going to get to the next obstacle until I get through this one.

Jim Marous (39:31):

And what's good is there's a lot of organizations that are willing to help financial institutions get there. There's no lack of people wanting to serve and make banking better and I think the opportunity is endless.

Jim Marous (39:45):

But it's, as you said, also an exciting time at technology, it gets down to culture. Is your organization going to be willing to do banking differently to make banking better. So, Marc, thank you so much for being on the show. I really appreciate it.

Marc Corbett (40:01):

My pleasure. Thanks for having me, Jim.

Jim Marous (40:04):

Thanks for listening to Banking Transformed, the winner of three international awards for podcast excellence. If you enjoy our work, please give us a positive review. This has been a production of Evergreen Podcasts, so a special thank you to our producer, Collins Blakely, audio engineer, Chris Fafalios and video producer Will Pritts.

Jim Marous (40:22):

If you've not already done so, please remember to subscribe to Banking Transformed on both your favorite podcast app and on YouTube for more thought-provoking discussions on the intersection of finance, technology and leadership.