Augmenting Bankers with Generative AI Solutions

Jim Marous (00:00):

Jim Marous from Banking Transformed here today from nCino's Annual Conference, nSight. About 2000 people here today, and the dynamics of interaction, the networking, the sharing of ideas, banks, credit unions of all sizes from really across the globe.

Jim Marous (00:28):

One of the bigger events I've been to and one of the more dynamic from the standpoint of the people involved. It's a lot of energy, it's at the Charlotte Convention Center, and it's really kind of interesting how when you get these dynamics, you have the opportunity to meet people that can share insights that I want to know about, which is pretty cool.

Jim Marous (00:45):

Today, I'm with Chris Gufford. What role do you have at nCino?

Chris Gufford (00:49):

So, I'm the general manager of commercial lending. So, all things focused on commercial lending from a product perspective, that's what I do and that's what my team does.

Jim Marous (00:58):

So, tell me a little bit about your background.

Chris Gufford (01:00):

So, I've been doing banking since I was 19. Started as a teller way back when I was doing my undergrad. Gray hair would indicate that I'm not 19, but been at that-

Jim Marous (01:10):

Me as well by the way.

Chris Gufford (01:13):

I was just assuming, but you don't want to do that. I transitioned into commercial lending, oh gosh, almost 20 years ago. Almost directly after that, started as a commercial credit analyst, ran commercial loan operations for a while, done DFAS stress testing and a number of things along the way.

Chris Gufford (01:30):

And then ultimately transitioned into the technology world and worked for some of the other large FinTech technology providers in the banking industry for a couple three years, and then found my way to nCino about eight years ago now, and been doing all that since.

Jim Marous (01:43):

Well, it's interesting, having been here today, the amount of change that happens from year to year to year is insane. Your CEO started off the session today talking about change never happened this fast, never happened this slow again – e was taking a lot of terms that I use on my podcast quite a bit.

Jim Marous (02:01):

He talked about the fact that composable solutions allows organizations of all sides now to do what only the big guys used to be able to do. You can take small solutions, build them, and work with other partners you have, and all the partners work well together for the most part, and it really makes it so that your solutions and everybody else's solutions are really powerful.

Jim Marous (02:21):

You really got in the cloud space a few years ago at a time when many of the financial institutions were worried about security issues. Now, those issues are kind of gone because most organizations now realize I have to worry more about my back office than I have to worry about the cloud performance.

Jim Marous (02:37):

And that's the kind of thing we're seeing everywhere is that so much talk about what's next? What's coming up, how do I keep up? And we haven't been to any conferences this year that at some point, in the first five minutes, the word “AI” or “generative AI” comes up.

Jim Marous (02:53):

And it's interesting because nCino's not unlike anybody else, they're trying to build solutions with the help of generative AI. And so, it's interesting, it’s one of the things that I found that you're working on is something called Banking Advisor that's going to leverage generative AI and AI to build a better solution. Can you describe a little bit about that solution?

Chris Gufford (03:11):

Yeah, sure. So, Banking Advisor for us is really about efficiency. And you talked a lot about the cloud and a lot of the things that we've done from a workflow perspective and changed how the industry – and democratized the cloud and the capabilities that it brings to folks. And for us, Banking Advisor is really our democratization of generative AI for all the players in the banking space.

Chris Gufford (03:35):

So, it's really about leveraging the latest and greatest technology to go, "Hey, we can make this even bigger, even faster, and even stronger. Make the folks out there much more efficient at what they do by having a generative AI solution standing over their shoulder and helping them do their work every day."

Jim Marous (03:52):

Is there going to be a commercial solution or are you simply in the commercial space and you have ownership of this product?

Chris Gufford (03:58):

Sure, no, that's a great way to ask the question. So, actually, there's an nCino solution, but there are unique use cases in commercial. There's unique use cases in consumer. There's even small business has unique use cases.

Chris Gufford (04:11):

We started in commercial, given that a number of our customers live and work in that space. But what you'll find and what you probably saw today is a number of the skills that we went and worked on initially traverse all of those business lines.

Jim Marous (04:25):

And goes from the back office to the front office very quickly. And it was interesting, I was in a group of an executive session today, and the one takeaway was the front office, the top of screen is not going to be very good if your back-office sucks, for lack of a better term.

Jim Marous (04:42):

And the reality is the consumer, the small business commercial entity, is really looking for you to know the customer, understand the customer, enable to build solutions for the customer that makes them feel rewarded. And we're seeing that in all kinds of technology today.

Jim Marous (04:58):

We see that from our streaming services, TVs, we see it from Amazon, we see it from Uber. Uber, you get in the car, you think that's the end of the transaction, that's only the beginning of the transaction. The longer the trip, the more they're going to hit you with things such as, “Here's a takeout restaurants, here's restaurants you like” because they know what you've eaten in the past. Or when I go into cities I haven't been to for a while, they're going to show me things that I may want to do based on my other travels.

Jim Marous (05:23):

So, when you look at your generative AI solution, your Banking Advisor, what do you see as the major differentiator that's going to give your clients in the marketplace?

Chris Gufford (05:33):

So, I think it's a couple of things. And as we look at the new operating paradigm that I think has evolved in banking over the last 18 months or so and where efficiency has really become ... it's always been a word. I think now it's taken on a whole new meaning from an operational perspective.

Chris Gufford (05:48):

And I think the differentiator is this is the next level of efficiency for a number of things, but it's also the newest way, to your point with your example around Uber – it's the newest way for us to start to present to the folks on the operating line. The lenders on the front line, the credit analysts doing the underwriting work, even the folks in the back office doing the things that they're doing – the best way to go service the customer.

Chris Gufford (06:12):

And it's a way for us to marry a lot of the analytics that happen in the background. Whether that's predictive AI or deterministic AI, or simple analytics, if then statements and whatever else, or just simple data elements. Deliver those to people in new and unique ways at the point of execution, at the point of engagement with the customer to ultimately create a very different customer experience because we're creating a very different internal user experience as well.

Jim Marous (06:35):

So, it's interesting because your research that you presented today at the session was 72% of financial institutions say that efficiencies are a number one priority. And it's interesting because efficiency three years ago even was all about saving money for the bank. Efficiency now is much bigger than that. Mind you, no employee has enough time on their hands, so efficiency is very important to them.

Jim Marous (06:58):

But the consumer expects you to get from destination A or point A to destination B quicker, better, just like a GPS system will be, and you need to know that North star to get there. Well, generative AI gives you that power to do that and allows you to say, "You know what, you've come after others that have taken the same general path. We're getting more and more fine-tuned as you go along.”

Jim Marous (07:22):

However, that said, organizations have all the data in the world that they want; deploying it is the tough part. And we're even seeing this especially in the generative AI. We talk a good game. We talk about with generative AI, we're going to be able to build better personalized solutions, we’re going to be able to make greater efficiencies.

Jim Marous (07:40):

However, actually doing it is a whole lot different than talking. We're all talking it right now. How do you move your customers, how do you move your own organization forward where the destination is not as perfectly defined?

Chris Gufford (07:57):

So, it's one step at a time which sounds a bit cliche to say, and I like to keep it super simple. And I think the thing that gets really intimidating about when we start talking about data, and data being the underpinning, and everyone goes, “I don't know, I have this giant data warehouse, data lake, I've got all these things,” and suddenly, it feels like the problem of that gets so large and I can't address it.

Chris Gufford (08:22):

But when you actually get down to going, "Well, what business problems would I like to go solve? What customer experiences would I like to go enhance?" What you'll really find out is that's five data elements. That's six data elements. That's 10 data elements. That's not a thousand and one data elements that live in that data warehouse.

Chris Gufford (08:40):

So, we start to go, "Okay, what business problems can we really leverage this new tool for? And then what data do we really need to do that? Alright, let's perfect those four or five, let's perfect those six or seven data elements” and we chip away at it like that.

Chris Gufford (08:54):

And suddenly, what you'll find is all of a sudden, you have 40, 50 data elements that honestly are super common across a lot of business problems. Like it can become that easy. So, rather than trying to boil the whole ocean of data, let's narrow it down, make it a bit of a puddle, but that puddle creates a whole lot of value.

Jim Marous (09:11):

It's interesting you talk about creating a bigger challenge than it has to be. I was at an organization, gosh, about nine months ago in Amsterdam, and they said 80% of what we're trying to do and solve for is already well-defined. We don't have to mess with it. We need to focus on that 20% that really is different from organization to organization.

Jim Marous (09:32):

However, a gentleman in Nairobi said one day, “The biggest challenge with digital transformation is bankers can't get out to their own way.” In other words, you can tell them 80% of this is going to be the same no matter if you go this way, this way, this way, this way in Charlotte at different financial institutions. It's going to be exactly the same.

Jim Marous (09:49):

How do you keep them out of their own way? How do you keep them as you're trying to present the product to financial institutions? How do you keep them from trying to mess with the 80% that won't make a difference at the end?

Chris Gufford (10:00):

Sure. It's an interesting question to pose and I feel like I've made a 20-year career out of trying to answer that question sometimes. But I do think that there's a bit of a recipe to it and I actually just go back to keeping it simple.

Chris Gufford (10:14):

And I'll give you the example of one of the first skills that we're presenting with Banking Advisor, it's knowledge base. And you talk about the 80% of, I'll just call it commercial lending for now, but it could apply to any of these solutions and business lines.

Chris Gufford (10:28):

You all have a credit policy and that really defines a hundred percent for the most part of how you do lending. We can give banking advisor access to that credit policy and you could ask it any question and it can help you understand what to do in credit policy.

Chris Gufford (10:43):

So, we keep it really simple. We go attack that and we solve a litany of issues, you're going to understand when I have policy exceptions, you're going to understand when the right time to do this covenant or that covenant might be, you're going to understand whether I'm out of compliance with an LTV that I should or shouldn't have, those sorts of things.

Chris Gufford (11:01):

But I just did that with one document. That document might be 600 pages for, might be 200 pages for this institution, and it might just be 75 for that one. But we can solve a pretty significant knowledge gap with one pretty simple thing.

Jim Marous (11:15):

So, traditionally, financial institutions are risk-averse or risk is poison. We're trying very hard to make it so that organizations are starting to manage risk as opposed to avoiding risk. Because that's where the real power of AI is. That’s where the real power if lending is.

Jim Marous (11:35):

Is it's not only taking the A loans, it's going deeper and making a lot of money on those people in the mid ranges and those corporations in the mid ranges. How do you work with an organization in a normal course of business, not just with generative AI – to really accept more risk, and to look at the balance between risk and reward?

Chris Gufford (11:57):

Sure. So, I think two or three things that I would talk to there. The first is really, you've got to start to be able to manage to the exception as opposed to the a hundred percent, and being able to identify those customers that are truly at risk and manage to those.

Chris Gufford (12:15):

And it's pretty simple to do that in general terms, but I think offloading the everything from everyone and focusing on the exceptions tends to let me go get the right things done at the right times for the right customers.

Chris Gufford (12:27):

And you've now got a platform to do that. You've got solutions that enable that kind of vision into the data and into the customers and a way to manage to that sort of exception.

Chris Gufford (12:37):

I think the second thing when you speak about AI or models, whether it's generative AI or predictive AI, the ability for it to present, actually pull through that data and find the uniquenesses, to your point, I've got a set of customers over here that while on the surface of it, they may look a little bit risky, they always pay. And those are the ones I want to go offer more for.

Chris Gufford (12:59):

And to your point, I increase profitability, but that profitability is in line with the risk that I'm taking, and my ability to crunch that data and understand those customers in a little bit different way has gone up quite a bit. And then my ability to present that to the front line so that they can go do it.

Chris Gufford (13:15):

Now we've taken the systems of analysis and we've married them to the systems of execution. So, I'm analyzing those customers and understanding them in a way that I can, and I'm presenting them as the appropriate sorts of expectations and exceptions to the frontline to go get the business, and we manage through that in a really …

Jim Marous (13:31):

Well, so, you also have the ability to test with generative AI or with AI in general; the ability to say, “If I went down here, what's going to happen here?” Mind you, we have more moving parts than we did a year and a half ago. Because interest rates … if interest rates would’ve stayed the same, it’d be great.

Jim Marous (13:47):

But we still had to, from a resilience standpoint and for a future of banking standpoint, we still had to test the things that weren't in place right then knowing that at any time, those can go wacko, and a lot of technical terms.

Jim Marous (14:01):

But it's interesting because when you do the testing, when you do the innovation, when you're getting out in the marketplace, it appears that Banking Advisor's going to start in the lending space for a while maybe. More so because that's where one of your strongest suits are, plus your clients are guided that way.

Jim Marous (14:17):

When you're knocking on the door in the first couple organizations, what are the challenges they put? What are the roadblocks that you see that they're presenting to you?

Chris Gufford (14:27):

So, it's interesting, the roadblocks have evolved. And as generative AI sort of made it splash on the world last year, we started to push into it into late summer and early part of the fall really heavily, the conversations were AI and AI sounds scary.

Jim Marous (14:46):

But the lending area is the first ones that have ever used AI, they used it for over a decade.

Chris Gufford (14:50):

Completely. Most of them are using it at some level depending on the definition, despite their concern for it. But the fact that generative AI was so different in the conversation and my ability to chat with it, and despite the fact that it can do far more than that – it was much more in your face than a parent.

Chris Gufford (15:08):

And so, I think there was this trepidation: “I don't know that that's what I want to do, and is that going to take my job? Like it feels like it might,” all those sorts of things. And as the last six, eight months have gone by, everyone's sort of settled in and realized, "Oh no, it's a really good helper." And it's great at helping me identify things in large sets of data that I wouldn't see, or it would take me days to see, and it can do it in seconds. That's really useful.”

Chris Gufford (15:35):

Or I have the ability to see something about a customer that I would have to spend hours sorting through, and I don't have to do that, it can help me do that. Or I'm new to the bank, I don't understand our credit policy or how we do things, or I'm not … part of helping me get trained up, it can help me do that.

Chris Gufford (15:50):

So, now, the roadblocks are really, look, I think not so much about, oh gosh, it feels … I'll just use the word scary – it doesn't feel that way anymore. Now, the question is, “Well gosh, where should we roll it out first? How should we roll it out, and at what pace should we roll it out?”

Jim Marous (16:08):

It's a deployment man. I get it. I understand how it's going to work, what do I touch first?

Chris Gufford (16:16):

Yes, that's really becoming the question. And there's still and appropriately so, no one's staring at generative AI to make credit decisions today, but that's for the traditional risk rating models and PD and LGD and all the fun things, or some predictive AI methodologies that you marry with those. I think that's fine. But from an efficiency perspective, now really the question is when and how soon, and what towards?

Jim Marous (16:43):

So, that's interesting because you're picking low hanging fruit saying, "You know what, we'll get to this perfect model personalization model later. But right now, in efficiency, that's low hanging fruit in that I can see weaknesses and this can get me there quicker.”

Jim Marous (17:01):

So, from what I'm hearing then, this is not only something that really helps the end consumer, this actually helps the employee experience quite a bit. This really changes the employee. You talked about employees being scared.

Jim Marous (17:13):

We see this all the time that we need to communicate a whole lot better to employees to say, “Digital does not mean the elimination of your job, but oh, by the way, you can really spend a whole lot less time on stuff that was wasting your time before, and you can use it much more effectively on what you were really trained for back in the day. You weren't trained in 18 layers of compliance, generative AI can help you with that.”

Chris Gufford (17:36):

Sure. It can know that really easily. And I just like to use the word “augmentation.” There's always going to be some degree of automation that keeps coming along and doing those sorts of things, but it's going to automate the repetitive tasks that you hated doing anyway. But it's also going to augment you on the sophisticated tasks that we still need you to do.

Chris Gufford (17:53):

And I think that's the really key point for the employee experience, is no one likes typing the same tax ID number 44 times. That's not fun, but can it help me take care of that? Sure. Right. Absolutely can.

Chris Gufford (18:05):

But when it comes to making a credit decision on a $50 million commercial real estate loan, should it do that? No, you should. Can it help you put the credit package together? Probably, and some of those things, and gather the information and present it, absolutely.

Jim Marous (18:20):

We have a lot of friction in the back office of lending to say the least. I mean, you look at the mortgage process, you go, "Are we still really using the copier and using up a whole ream of paper every time?”

Chris Gufford (18:30):

How many fax machines exist?

Jim Marous (18:31):

Oh, exactly. And again, how do you stay compliant in the process? I talked to an executive group today and said, "You've got to bring compliance on the front end of all these decisions as opposed to having them approve at the end because it isn’t going to play well.

Jim Marous (18:46):

You talked about some of the negatives, the challenges: what's the greatest opportunity in the near term with regard to the efficiency that can be achieved through generative AI?

Chris Gufford (18:57):

So, I think it's really about the experience of the employee, and I think the experience of the end customer, especially in commercial, and commercial lending will feel it's that time to onboarding, it's that time to yes or no, or maybe.

Chris Gufford (19:17):

And it's the time to booking, and then ultimately, it's how we manage and monitor that loan on the backend and the efficiency that we can create. Because I mean, if we're all honest with ourselves, somewhere between probably 60 and 80% of the work that a banker does, whether you're an underwriter or a relationship manager, it's the reviews, it's the renewals, it's the modifications, it's the stuff that happens after I get the loan done.

Jim Marous (19:39):

It's the dirty stuff.

Chris Gufford (19:40):

It's the dirty stuff. And we can make that and much, much more efficient, leveraging tools like this. And again, managing to the exception and letting it help me do that.

Chris Gufford (19:49):

And you talked about personalization and some of the things that we're working on, and one of the things we have, and I mentioned knowledge base where I can go query credit policy or query the SBA standard operating procedures, how would I go do these things? But the next step for that is personalization for the folks internally.

Chris Gufford (20:06):

So, as I'm looking at an underwriting screen or a collateral screen, or something about the loan or a customer's profile, having Banking Advisor be personal to me in my role – so, whether I'm a relationship manager or an underwriter, I kind of want to see different things. I worry about different things, but having a personalized experience that says, "Hey, you should think about this because of this customer, or I see this, you should pay attention here."

Chris Gufford (20:29):

So, not just me asking it questions, but it being very proactive with me and having an interaction like you and I are having right now about, "Hey, these are the things I see, you're going to want to focus here." So, the personalization particularly on the employee level, is on the way. And I think it's a big efficiency changer.

Jim Marous (20:48):

And that again, gets down to the democratization of data. I used an example today that when I started banking before you did, even though you started at 19-years-old, which I didn't do – the reality is if I had a marketing program and I wanted know the results of a marketing program that I did in January, I'd have to wait until December, November time period to get those results back. And in the meantime, I've done four other programs, so nothing is the same anymore.

Jim Marous (21:11):

Now, you find a lot more organizations getting much more power out of democratizing data, letting people innovate, letting people test, letting people actually see what the customer's doing, which gives an opening for your type of solution in the marketplace because they're already used to the handling of data, the understanding the customer, understanding the process enough to say it's not just about turning paper into PDFs, it's much more beyond that.

Chris Gufford (21:39):

You talked about efficiency and I think defining efficiency three or four years ago is just saving the bank money. And I think when you wrap that data conversation and your commentary there into that – and I actually think people are sort of aiming at three or four things that I'm not sure everyone's articulated yet. They use different words to do it, but we've tackled process and workflow efficiency, which is sort of saving the bank money stuff.

Chris Gufford (22:03):

The question of data efficiency and that's sort of my way of thinking about it is starting to become really clear. And I think that's the oceans into puddles, and then we sort of move ourselves along pretty quickly through that.

Chris Gufford (22:18):

But then I think what really becomes interesting is how do I take those two things and how do I create knowledge efficiency. And it's this dissemination of knowledge from those things that have been accomplished thus far, and I think that the tooling is the AI tooling.

Chris Gufford (22:33):

It lets us disseminate that, but you have to have a platform like nCino to land those things in the right places for the right people at the right moments at that moment of execution and engagement with the customer to ultimately create that knowledge efficiency.

Jim Marous (22:47):

And again, how many clients does nCino have right now?

Chris Gufford (22:51):

Oh gosh, I'm going to say somewhere around 15, 1,600.

Jim Marous (22:57):

So, the learning from each other, the knowledge efficiency is a great way to look at it because as this product gets rolled out and people start to use it, and you learn as we do in the GPS system – okay, we got to avoid this one and we get closer and closer, do you want to take the toll road, do you want the other way?

Jim Marous (23:13):

I mean, it works really well as an analogy because that knowledge is group knowledge, it's crowd sourcing knowledge, which is really what you're going to be doing in the gen AI role. When do you see the rollout of your product?

Chris Gufford (23:27):

This month. So, what we've shown this week will be available to everyone this month.

Jim Marous (23:34):

Which is May of 2024.

Chris Gufford (23:35):

May 2024. Yes. If you're seeing this at any other point, we're talking about May of 2024.

Jim Marous (23:39):

But you mean the starting points with the best clients that are already talking your language. So, on that talking your language, because this is brand new technology, we're seeing more and more organizations, depending on their financial institutions, as much as a financial institution depending on them for the growth – you're working more as a team to say, “We have to progress together in order to benefit all and for you to be at the front end.” How much do you seen that within your clients right now?

Chris Gufford (24:08):

Oh, a lot. I mean, we engage with them a ton. In this particular case, we had a number of clients working on a product development program, an early adopter program with us over the last three or four months. So, this has been a collaborative effort. The use cases we've come up with, they've all tested, we went back and forth, but our job is to-

Jim Marous (24:26):

They're asking you the question you forgot to ask yourselves.

Chris Gufford (24:28):

Yes, yes. And I mean, our job is to help them deliver, create value for their customers. And if we don't go ask them and we don't solicit that feedback and ask them to ask us the hard questions, we'll never get there. So, we take that super seriously actually and the collaboration there with our clients is fantastic.

Chris Gufford (24:47):

And I always talk about this, but we're changing banking, and we can't change banking without being in banking, and being out there with our customers and having those conversations and having-

Jim Marous (24:58):

You don't have those experiences working with your clients.

Chris Gufford (25:01):

Exactly. We've got to go do that. So, the interactions are fantastic and incredibly helpful.

Jim Marous (25:06):

So, you're on the front end of this brand-new technology, this brand-new usage, deployment of technology. We've got to, as an industry, think beyond what's going to be needed next January. We got to think not too far ahead if we're going to make it … we have to have scenarios that take us into multiple paths in the three to five-year span. What are you looking at as far as the future of your platform?

Chris Gufford (25:34):

Sure. And I'll speak specifically-

Jim Marous (25:36):

Not just your platform, as nCino, but the implementation of generative AI within that platform.

Chris Gufford (25:42):

As we think about it, I think it's more and more and more augmentation of the folks that are in the trenches doing the work every day, and a much more personal, and I'll just say persona or role-based approach to each one of those.

Chris Gufford (25:57):

So, think about (and I'll use AI-ish terms) an agent or a co-pilot for underwriting, an agent or a co-pilot for the relationship manager that helps them go sell and manage a relationship-

Jim Marous (26:07):

As a client is using an agent and a co-pilot for my identity because I'm right now building my own open banking platform with multiple providers. It'd be really good if that dialogue between different players was collected within my persona as I'm talking to your persona.

Chris Gufford (26:26):

Yes, yes. That's exactly the idea. As I stare at it three to five years from now, it's a number of agents interacting with other agents that are all augmenting the individuals involved. And we create this incredibly efficient process that has … I'll just call it the utopian version of a lending experience.

Chris Gufford (26:43):

And treasury and deposit and literally, the entire sort of commercial lending, commercial relationship life cycle. If I were to sort of paint the broad picture, I think three to five years, that's where we should be aiming for.

Jim Marous (26:58):

So, final question: when you look at the client base, meaning the world of financial institutions right now, we have different organizations, different levels of digital maturity. You have your players right now that are on the front end and getting into the mud with you and in the sandbox with you, and really using all these tools. You have the others that are maybe mid-range that kind of want to be there, but maybe their priorities, they got to fix some basic stuff first. And then you have others that are going to be laggards, that's going to happen anywhere.

Jim Marous (27:28):

What's your recommendation to that middle set? The middle set that kind of knows they've got to keep aware of it, but they've also got to fix what's really broke up.

Jim Marous (27:40):

We talk about the length of time it takes a consumer or a small business to apply for and get disbursement of loan is just insanely long. Still, at 90% of financial institutions, your organization's working daily with your partners as well to bring better solutions to the marketplace. But as I said earlier in the podcast, but bankers get in their own way. What do you recommend?

Chris Gufford (28:01):

So, I'll give you my banker answer and my technologist answer, how about that? And then we'll marry the two together. So, my banker answer is look, go fix the people and the process and the technology first.

Chris Gufford (28:12):

And what I mean by that is look, sort out the processes that you think are slowing things down, and get the right people in the right places to go do the things, the speed and pace that you want, but you also have to have the technology along with it like nCino to help facilitate those things. But my technologist answer is, is while you're thinking about that technology, don't build for yesterday's approach.

Jim Marous (28:34):

Or even today's.

Chris Gufford (28:35):

Or even today's because I can tell you, what I've seen in the last six, eight months on the generative AI front, I mean, it's changed incredibly since October, and even in the last few days, some new things.

Chris Gufford (28:47):

So, while yes, you should go get the platform and the foundation set from a technology perspective in an appropriate way and in a way that you feel comfortable with, don't not engage with the latest and greatest.

Jim Marous (29:03):

That’s great advice. Because I think we get stuck. There's an overwhelming number of things we have to fix in the banking industry. It's like playing a whack-a-mole. I mean, it is just crazy because you hit one, you got another one that pops up. And that's the truth with generative AI. Just because it confuses us, doesn't mean we should step away from it. We've got to double down and the knowledge is there.

Jim Marous (29:26):

In fact, generative AI can help educate you on the potential for generative AI. It's not a biased platform. You ask it the right questions, it'll give you an educated answer.

Jim Marous (29:36):

I think it's important for anybody who's listening on the podcast today and watching the podcast today to realize you just got to just do it in the Nike terminology. And you've got to realize that while you may not be able to buy the platform or the solution you're talking about today, you've got to be working towards that. And if you’re not, you’ll be left behind. Because playing catch up is not a good place to be in right now.

Jim Marous (29:58):

So, thank you very much for being on the show, and I really appreciate your time. It's interesting to talk to somebody who's actually in the trenches doing it and able to say, “I'm not faking it with good shareholder talk, we're learning as we go.” And that's the reality of what's going on right now. But that's the only way you're going to become resilient and more efficient.

[Music Playing]

Jim Marous (30:20):

And you're not shooting for the moon right here. You're saying, “You know what, let's take care of these stages.” So, thank you very much.

Chris Gufford (30:26):

Absolutely. Appreciate it, Jim. Thank you.

Jim Marous (30:28):

This has been the production of Evergreen Podcasts. A special thank you to our senior producer, Leah Haslage; our audio producer, Chris Fafalios, and our video producer, Will Pritts. And a special thank you to the team at nCino who has helped produce this and film today's podcast at the nCino Insight event in Charlotte, North Carolina.

Jim Marous (30:46):

Is it North Carolina? I'm going to keep that by the way. I'm leaving that.

Chris Gufford (30:51):

Sorry man, half the time, I'm like, I don't know where I am either.

Jim Marous (30:53):

Yes, yes, yes. I need ChatGPT just to figure out geography. I should have figured out because it's right above the board.

Jim Marous (31:03):

Okay. I'm good. I'm going to go with the flaw at the end. Somebody's got to laugh. If you see how many flaws happen on a normal basis …

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