How Banks Are Redefining Speed, Risk, and Customer Experience

In this sponsored episode of the ProSight Banking Strategies Podcast, Moody’s banking industry practice lead Chris Stanley discusses why the future of banking depends on delivering faster, more accurate financing and risk decisions without sacrificing safety. He explores how AI, data modernization, and integrated risk signals are helping banks break down silos, improve customer experiences, and compete more effectively with FinTechs. The conversation highlights the importance of rethinking banking processes around customer needs, fostering cultural change, and using AI as a catalyst for transformation rather than simply automating existing workflows.

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Don’t miss this opportunity to discover more about how AI, data modernization, and changing customer expectations are reshaping the competitive landscape with Moody’s Chris Stanley in the ProSight Quick Q&A on The Future of Banking: Better Decisions, Delivered Faster.

TRANSCRIPT:

Frank Devlin: This is the ProSight Banking Strategies Podcast. We’re here to inform you on the top trends, challenges, and opportunities in banking today. ProSight is a leading non-lobbying connector of people and information, with deep expertise in risk, fraud, compliance, and retail and commercial banking. Our purpose is to empower financial services leaders to strengthen and advance our industry through training and insights, as well as tools and resources like this podcast.

Hello and welcome to the ProSight Banking Strategies Podcast sponsored by Moody’s. I’m Frank Devlin, senior editor at ProSight Financial Association. For years, financial institutions have balanced growth, risk, compliance, and customer service as separate priorities. Now, those priorities are increasingly converging around a single competitive requirement, the ability to deliver accurate financing and risk decisions at speed. Rising customer expectations, intensifying competition from traditional institutions and FinTechs, the growing threat of fraud and the rapid emergence of AI are all putting pressure on banks to rethink how they operate.

Today, we’ll discuss what the future of banking looks like in this environment, why speed and safety may no longer be opposing forces and how leading institutions are approaching AI and data modernization. Joining us to share those insights today is Chris Stanley, banking industry practice lead at Moody’s. Thanks so much for being here, Chris.

Chris Stanley: Thanks for having me, Frank.

Devlin: So, the major theme of the new Moody’s report called the Intelligence Edge Banking’s New Decision Advantage is that competition, technology, and customer expectations are all demanding that financing decisions occur faster, which I was mentioning at the top of this podcast. And then I imagine this is felt across the organization—risk, data, even culture. Can you talk a little bit about what the future of banking looks like in these and other areas and maybe what is still needed to get there?

Stanley: I think it’s a really interesting thing to talk about in the sense that we so quickly want to jump to this being a technology story because of everything that’s happening in the banking environment and the wider market around technology at this point in time. But I think a big part of what’s coming through in this report, in conversations I have with bank boards and C-suites every day, and what I’ve lived firsthand as a banker is that the pressure to decide faster isn’t a pure technology story. It’s really a customer story. The customers that come to banks have always needed a quick answer, even if that is a no answer. And what’s really changed is the tools that now exist to give them one and the institutions that figure out first how to use those tools and next how to really reorganize themselves around that concept of decision speed will be the ones that really stand out and differentiate themselves as we go forward.

Devlin: That’s a really interesting point about getting to ‘no’ faster as well, which I could imagine that some businesses would appreciate. Anything that’s not holding them up, maybe they can come back for their next project and still have better feelings about your institutions.

Stanley: I would just say the investment of time by the bank and the client in a deal that they can’t do has a material cost to both parties. Even if no is the answer, getting there quickly is in everyone’s interest. The other piece of saying no that I would take a look at is we’re not thinking about this speed to decision purely in terms of how do we speed this up? But there’s a lot of discussion in banks right now as well that’s really around if we had a better signal, if we could see a distinction where everything looked the same, what are the things we would stop doing that would reframe that decision timeline?

Devlin: That’s a really fascinating way of looking at that. And there is a lot of fascinating, interesting data in the report and a compelling statement, I guess you could call it, that said in the context of risk management’s interaction with the business line, speed and safety are not necessarily a trade-off. Now for someone that goes way back in risk, that might seem counterintuitive. You have that deal and certainly don’t want to rush through anything. So how can that be? And is this something that’s future state? So in a couple of years that will be true or is that true on the ground right now, speed and safety are not a trade-off? Can you talk about that?

Stanley: Absolutely. It’s true right now. I think it’s only going to continue to evolve as technology allows us to consider more things to include in that ensemble of signals that we’re looking at. I can tell you firsthand when I was a banker, we did something that has become a template for hundreds of other banks I’ve worked with since coming to Moody’s. That was where we took a look at that entire decision process and said, “Is there something we can do here? Is there a signal that we could add that could kind of be a Rosetta Stone that would help a bunch of different disciplines speak the same language and really look at each customer with a consistent view of risk?”

And that became this opportunity to take a look end-to-end at that decision timeline and say, “What are all the places here where we’ve got disagreements, where we’ve got experts doing the same thing on every customer without differentiating customers by risk?” And that kind of wholesale look around one consistent signal that everybody could agree on and that became that foundation that we could cast through the lens of many different disciplines, it created some remarkable results. And that was really just with the one measure of credit.

So I think that’s absolutely how you see it happening in real time today. I think technology is going to allow you to expand that ensemble of signals and say, “Hey, that credit model that Chris worked with all those years ago did all those amazing things. What if we could show some other signals to those same experts that help them see additional distinctions over and above the ones that Chris and his team were looking at? And what kind of transformation does that then enable is what we’re after here.

Devlin: Can you think of any examples of actually a deal happening, a deal that’s in the works, the lifecycle of it, like agile risk management? Risk is there from the very beginning and they’re helping mold the offering or the product. That way when you get to the end, there isn’t this delay. Is it something like that? And what are some examples? Can you walk me through the way a deal might work and how speed is not the enemy of safety?

Stanley: Yeah, I mean speed is the goal. We want to move fast but do it safely. And so I think a big part of what I look at is across a lot of organizations, expert judgment plays a very important role in managing risk and delivering a distinctive customer experience. The critical thing about expert judgment, as much value as it can add when it’s applied in the right spots, it’s something that’s very difficult to scale. Everything that you know, Frank, is very difficult to find a carbon copy of. If I grow too much for you to handle the portfolio by yourself, really difficult to find another Frank that possesses all that same knowledge.

A big part of where we maintain that expert judgment but make a more scalable process is to introduce an empirical anchor. Something like a probability of default-loss given defaults has been a traditional starting place because that gives us that Rosetta Stone that gets used all through the life cycle of that customer, not just to decision a prospect or accept a new deal, but managing a portfolio, setting reserves, planning capital, running ALM, doing all those things. That sharpened view of the credit risk associated with that customer is really important.

So give those experts that analytical anchor where they can suddenly start to tell customers apart and they can say, “This customer is more risky. They require more expertise or additional steps for monitoring. This customer is less risky. We can get them to a quick yes or a less onerous deal structure or other things like that.” So we’re starting to really not only reallocate how we use expertise to create some scalability in the back office, we’re also differentiating a customer experience at the same time.

Devlin: That takes data, I’m sure, getting to know the differences between customers faster in a dynamic environment. And you’re talking about scaling, expert judgment as well, technology, efficiency. It sounds like a job for AI agents. Seems like that’s part of this equation and how so?

Stanley: Just like with anything else, the question becomes do you trust the information? And are you being given information that is actionable or are you just being given information that’s interesting? And I think this is going to be where you’re going to see a lot of experimentation and opportunity to find competitive advantage is to say everybody is looking for those distinctions that matter to how they’re going to take action with their experts or how they’re going to choose a customer to pursue versus one to leave for another bank.

And I think how we decide what are those signals that we’re going to let in? How do we rank-order to the signals? How do we think about a mix of some things that are maybe predictive like a credit model or something that helps us just provide a heuristic or a touchpoint for monitoring a customer so that we’ve got an ability in a bunch of different disciplines and a bunch of different states of the world to always know who to call first and where to apply our experts to delight our customers and grow the bank profitably. So managing that ensemble is going to be the field of competition.

Devlin: As things are going faster and we’re dealing with more information, we probably have more expertise. It’s all still going to be grounded by this framework or a heuristic as you’re saying. The humans in the loop, let’s say, and just the humans doing the work. They feel grounded. They don’t feel like they’re out too far out on a limb and they’re comfortable with what they’re doing. It all sort of makes sense even as they’re moving so quickly.

Stanley: Exactly. And I think with that, part of what I would be looking at is connecting all of these signals going to be a critical element of success. Being able to find Chris Stanley the depositor, Chris Stanley the mortgage customer, Chris Stanley Construction, the corporate customer, and link those entities across all of the internal and external data sets that the bank has and connect those data sets with the right tools to explore the big questions that the bank’s experts have. This is really what the connective tissue of AI is enabling and what the strategy around that technology needs to be really focused on.

Devlin: You mentioned some customers that might work with the bank in different ways, the same person or the same company might have different desires for different products. Obviously all this speed that’s necessary today is not happening in a vacuum. So a lot has to do with what customers are expecting and maybe what they’re seeing the the competition is doing. Is that everything that is prompting this? If so, how is it happening? Are there other elements as well that are prompting traditional institutions to get to yes faster or no faster and to do the requisite risk faster?

Stanley: I think we find ourselves in an interesting point. As I look back on my career, we as bankers have been aware of and competing against non-bank financial institutions or FinTechs or any of the other kind of non-traditional entrants into the banking market who are rethinking that process of getting customer needs met, whether they’re doing it end-to-end like a bank or just intermediating steps of the process and just really innovating there.

I think a bunch of that career in banking has also been shaped very much by some incremental advances in the way banks were managed. And I think the moment that we stand at, there’s kind of a need to do a healthy mix of both. Taking a look at the end-to-end process and saying, does it all make sense? We’ve got some new technology that maybe changes the path that we take from inputs to decisions, inputs to customer experience, but we’ve also got a long history of some successful tools of managing risk and keeping experts working together to delight customers and grow the bank profitably.

We’ve got this opportunity, no matter where you are in that system, to take a look at the new technology that’s becoming available and really reorient yourself back to that customer experience that you’re really trying to craft and working from the customer back is really critical at this point. I think a lot of that incremental development of risk management and systems in banking has led to silos, complicated tech stacks, disaggregated data sets, all the things that bankers are well aware of. But I think the timescale and the runway that remains for overcoming those limitations to remain competitive has been dramatically reshaped with the emergence of AI and that competitive landscape. You’re not just worried about bigger banks outcompeting you or a FinTech outcompeting you. That technology is going to democratize a lot of things and a disciplined smaller bank can outcompete against scale very easily in that environment.

So everybody is operating on that knife’s edge and really needs to take a look at the whole end-to-end process that says, what does the customer need? How do we figure that out and deliver it faster than we ever have before? And what are the prioritization of steps in all the things we could fix that get us to that place as quickly as possible?

Devlin: I don’t feel like many people are using the words incremental much while also exploring the concept of AI. I think the thought is things are moving in big steps now. It’s revolutionary. It’s not evolutionary, but I definitely appreciate what you’re saying about the need to be incremental. And the idea does make a lot of sense based on what you’re seeing from the customer. So you don’t just blindly go ahead and put in technology, just like you didn’t want to do that 10 years ago or 20 years ago without being very keyed into what the customer wants, which is the reason you’re doing this in the first place. You’re getting faster to, like you’re saying, delight the customer and do everything you can to keep them for deals going on years and years ahead.

You’ve talked a little bit about FinTechs. I’m just wondering, given this sort of philosophy you’re talking about, will the distinction between banks and FinTechs blur or will they actually widen because traditional institutions will continue to have their place and it will be more like what you were just talking about versus the sort of [inaudible 00:15:49] kind of idea?

Stanley: I think that depends almost entirely on which direction banks decide to move. I think there’s always been this assumption that eventually FinTechs are going to have to grow up and start behaving like banks. They’ll only be able to scale so far before they need a deposit franchise and that’ll force them to have to do all the asset liability management work that banks do or have some other thing that’ll draw regulatory challenge just like banks have. And that’s kind of what we’ve been waiting for. And as long as that argument has been made, in the meantime, FinTechs have been iterating on that customer experience delivery without the technical debt of banks, the disconnected systems, the siloed experts. They did get that carte blanche opportunity to start and say, “Here’s the experience I want to deliver. What are all the things I need to do to deliver that?”

As opposed to banks being shaped by gradual increases in regulation, gradual development in competing technology that delivered a bank that functioned but functioned around disconnected systems, siloed experts, processes that are anchored in how things have always been done. That’s what slows traditional institutions down. The banks that close the gap won’t do it by defending, “Hey, we’re getting by okay with the status quo even though we know we’ve got this technical debt.” The banks that close the gap are going to do it by asking the same questions FinTechs asked from the start. What does this look like if we built it around the customer? Now what do we need to prioritize in the gaps that we have to get to where we want to be?

Devlin: Right, and not think so much about the context of having 50 or 100 years of history like you’re saying, and so many other appendages and processes that have built up over the years. That makes a lot of sense for sure. I’m wondering, I thought you were maybe getting to this a little bit earlier, but just talk about culture a little bit and viewing the landscape in a way that [inaudible 00:17:59] laser-focused on what you’re going to do now faster and not so much on the legacy. What role does culture have in having this vision of faster lending, faster risk management become a reality and becoming competitive for now and the future?

Stanley: There’s a story a mentor of mine used to tell me. There were some smoke jumpers out in Montana in the 1940s and they got caught in this fire because it wasn’t behaving as they expected it to. The wind was swirling. All these kind of unexpected things suddenly started happening and the foremen of the crew ordered everybody to drop their tools. And in the after action study of what happened, who got hurt, who survived, what became really important was the struggle that those expert smoke jumpers had in letting go of their tools that they had … Every fire they’d ever been useful in had involved them using those tools.

And I think this is a great analogy to the place that we’re at in managing bank culture through an enormous transition, that resistance is inherent in how we define our identity as experts. We want to hold onto those tools. And the thing that sometimes is going to save us or the thing that’s going to really be the great opportunity that we uncover, it’s going to come from a willingness to drop those tools, to take a look at the things we’ve always done in banking; credit memos, committee processes, the experts that own everything.

And remember the point of all this was never the tools. It was always the customer kind of managing that culture to say everything’s on the table. It’s all about the customer. We’ve got some new opportunities to find our way to the same destinations as well as some new destinations that are coming into human reach that were never there before. Let’s get our experts deployed. Let’s not be afraid to drop a couple of tools on the way to the target state and let’s be hyper-focused on finding the people in our institution who really know the difference between using an AI output and understanding it. And that’s how this new technology is going to be culturally brought in and enable that really tangible transformation in how we run a bank, in how we delight customers, in how we grow portfolios profitably.

Devlin: That’s a great anecdote. Thanks for sharing it. I had not heard that. And it’s so true that I think many of us want to continue doing things the way we do them. It hurts. It’s difficult emotionally. We get attached to the way we do things. And the more advanced the technology, the more different it seems and the less we do the things that we do day in, day out and we do them well. It’s definitely a point well taken. We need to be flexible, adaptable to get where we want to go culturally.

So we’re about out of time. I did want to ask you one more question as we talk about this very important area of thought leadership in the future of banking. What do you think is some accepted wisdom about the future, which we’ve been talking about a bit, that might be missing some nuance for perspectives, things that we might be reading about all the time or folks are talking about? Are there some ideas or factors that you don’t think enough people are talking about or appreciating to the extent they should be?

Stanley: The thing I would push back on the hardest that I’ve seen so far is that the banks that win this era will be the ones that deployed AI the fastest. I think speed of deployment is the wrong scorecard. It’s leading banks to ask questions that are too small. What thing do I have that I can fit AI into? What thing have I been doing that I could automate with AI? These are the wrong questions. What we need to be doing is taking a look at bigger questions. What are the things that can fundamentally be rethought or what are the things that were always out of reach because we ran a business that was based on humans and spreadsheets that now that we have this new magic tool in our arsenal suddenly become possible? They don’t just diminish prior obstacles, they completely eliminate them. So fundamentally rethinking what are the questions our experts should be asking, not how quickly they automated what they were already doing is really the refocus that I would push for.

I know you like stories. There’s a paper that was written a really long time ago back in the 1990s called The Dynamo and the Computer by an economist, Paul David. It’s short. It’s totally worth your time. It totally reframed how I thought about AI and banking. It covers the use case of when steam engines were being electrified. They couldn’t find in the Industrial Revolution any gains in productivity for many, many years after electric motors came on scene. And that was because everybody was saying, how do I fit an electric motor into the steam engine-driven factory? And the real change came when they said, how do I redesign the factory around the electric motor? That’s the moment we’re at in banking. The paper does a great job of helping you reframe what are the questions we should really be asking here and how do I take my bank through this substantial inflection point that we’re all standing at the edge of?

Devlin: That’s a great recommendation. I’ll take you up on that and it’s a good place to end this as well. So Chris, thanks so much for sharing your perspective with our audience today and thanks to our listeners as well for spending your valuable time with us. If you like this ProSight Banking Strategies podcast, please spread the word. I’m Frank Devlin, senior editor. Have a great day.

The views expressed by the speakers are the speakers’ own and do not reflect the views of ProSight Financial Association, BAI, or RMA. The views expressed and information shared are of a general nature and are not intended to address the circumstances of any particular individual or entity. No one should act upon any such views or information shared during this podcast without appropriate professional advice after a thorough examination of the particular situation.