Generative AI in Banking: From Hype to Value Creation

Jim Marous (00:11):

Hello and welcome to Banking Transformed, the top podcast in retail banking. I'm your host, Jim Marous, owner and CEO of the Digital Banking Report, and co-publisher of The Financial Brand. In today's episode, we have Sriram Natarajan, president of Quinte Financial Technologies, the leading provider of cloud-based software solutions for financial institutions.

Jim Marous (00:34):

In this episode, Sriram will discuss why AI's true potential license capacity to streamline processes, elevate decision making capabilities, and ultimately deliver better services and better experiences to consumers.

Jim Marous (00:51):

He believes it's time to move from the initial hypes surrounding the use of AI or gen AI, to the point where AI becomes a profitable and practical foundation for ongoing resilience. Sriram also shares a delicate equilibrium that must exist between customer-centric approaches and regulatory adherence.

Jim Marous (01:12):

The foundation of digital transformation success is through optimizing processes, improving knowledge transfer, and delivering better internal and external experiences. Financial institutions must combine modern technology with data-driven insights and human expertise, removing friction from antiquated processes, and building engagement across the entire customer lifecycle.

Jim Marous (01:35):

So, welcome to the show, Sriram. Before we start, can you share a little bit about your background and share a short introduction to Quinte Financial Technologies?

Sriram Natarajan (01:44):

Thank you, Jim. My name is Sriram Natarajan. It's a pleasure talking to Jim on this platform. I'm a former banker or rather from the financial institution financial services industry. I worked in American Express. I worked in HSBC and last in G Capital before I moved into the ITS and fintech industry.

Sriram Natarajan (02:07):

My company, Quinte Financial Technologies, was formed in 2019 just before the pandemic. And my company is a process, transformation, analytics, and BPM services company. Our objective or our track record has been in helping financial institutions in the U.S. Banks, credit unions, payment processes with their processes with their data management needs and with their product and services support.

Jim Marous (02:45):

So, Sriram, your company touches a lot of different areas ranging from automation and efficiency through customer experience. How did the company actually get formed and how did you actually determine, is it pretty much a data company that drives forward better solutions for digital transformation?

Sriram Natarajan (03:05):

Well, our legacy is more on the BPM or the BPO industry. In our previous company, which was acquired by this company, we had a team of over a thousand people working in back-office processes like fraud, transaction monitoring and check fraud management, et cetera.

Sriram Natarajan (03:25):

So, our legacy is more a process centric which is why we have evolved along with the industry with this digital transformation that's happening. So, we kind of aligned ourselves to institutions and their digital transformation processes more at the back office than at the front office.

Sriram Natarajan (03:47):

Our objective is to help institutions not only to launch new products and manage their processes more efficiently, and at the same time also help them in saving costs and increasing their revenues.

Jim Marous (04:03):

It's interesting Sriram, when we talk about better customer experience, and when we talk about the front office, we often forget that simply trying to convert analog processes to digital is not enough to make the front office work well.

Jim Marous (04:19):

And you mentioned the fact that you work on process automation, all these elements, and I think financial institutions sometimes jump the gun. They try to build a great front office without realizing that they have to really fix the back office.

Jim Marous (04:33):

And one of the best benefits of fixing the back office is certainly the efficiency and the cost containment. But you can't build … we found more than a few times, you can't build a good customer experience without really rethinking the back office.

Jim Marous (04:48):

So, you said your company started in 2019, amazing timing when you think about it, looking back. But we found that COVID is not as big of an impact as today's economic conditions. So, how have the macro-economic environment impacted the financial services firms that you serve?

Sriram Natarajan (05:08):

Great point, Jim. Absolutely right. I think 2023 and 2024 has been more of macroeconomic trends and global geopolitical trends rather than the COVID. I think the COVID is well and truly behind us. I was just reading the financial stability report released by the Federal Reserve on Friday.

Sriram Natarajan (05:31):

I think a lot of good data in it, and I think it kind of resonates with what we are finding with our clients, both large banks and small banks and trade unions and community banks. I think the key thing is that there is a bit of a dichotomy or a bit of a divergence between commercial portfolios and consumer portfolios.

Sriram Natarajan (05:53):

Since mortgage has been kind of static, consumer mortgage has been static over the last three years, not much of a stress there. As you know, residential unit values are holding strong. There is enough demand but commercial portfolios as well, there is stress delinquencies are going up, particularly on the unsecured side of things.

Sriram Natarajan (06:15):

We have new, should I say, new forms of credit like BNPL, installment credit and app-based credit, and all these nice new things which are making a lot of difference. But at a macro level, things appear to be stable. The liquidity ratios are good.

Sriram Natarajan (06:36):

I think the household debt to GDP ratio is holding strong mainly because mortgage has been kind of static. So, there are no visible trends like 2008 or just during the pandemic but there is a lot of undercurrent which you can see particularly inside the bank, in the back offices.

Sriram Natarajan (06:58):

I think the biggest stress is in terms of new loan booking or new bookings or adding to the portfolio, that is proving to be a challenge. The cost of doing that, especially with people, the staffing costs, and the technology costs.

Sriram Natarajan (07:15):

And there's a lot of hype going around on gen AI and all these nice things that everybody wants to be — it's like the fear of missing out. Nobody wants to be seen as not doing these things, particularly that everyone is now calling themselves a digital financial institution.

Sriram Natarajan (07:32):

And there is this thing, whole hype around digital when people see all these things happening around them, it is making a lot of people think and that's where I think it's this confusion more than the macroeconomic factors which are really affecting people.

Jim Marous (07:52):

So, when you talk about gen AI, when you talk about the back-office efficiencies, when you talk about digitalization and deploying new technologies, it's the biggest challenge if you're taking a broad look at it. In the legacy financial institution, business is trying to become more like the digital first organization, because digital first organizations, number one are more apt to have the back office in the right shape.

Jim Marous (08:23):

And secondly, they're probably using data for customer experiences and personalization stronger, and they're probably further along the curve when you look at the application of gen AI. So, with those things in context, are the challenges similar, do you think, for smaller and larger organizations or do they differ significantly?

Sriram Natarajan (08:45):

Well, at the basic level I think the challenges are similar in the sense that I think the large and the small organization trying to figure out what is the ROI in all the efforts that they're to make and how is it that they can convince their customers that they have something which is in line with their expectations at that level is not much of a difference. Because of the fact that almost all customers are turned into digital natives.

Sriram Natarajan (09:10):

You have whether it's a small bank or a great union or a large bank, most of the consumers are now adopting digital … are kind of very comfortable with the digital and actually forcing the hand of many of these institutions.

Sriram Natarajan (09:34):

But when it comes to taking those decisions or actually investing in a particular area whether it's gen AI or whether it's any other LLMs or any other form of digital investment, I think there are some very limiting factors which are more hampering the smaller or the midsize institutions.

Sriram Natarajan (09:57):

Particularly because as you rightly said in one of your earlier podcasts is that the core banking systems, or many of these smaller institutions are archaic. So, do they rip and replace, or do they invest a lot in putting in middleware or other things which will help them do it? That's a big question, especially for these smaller institutions.

Sriram Natarajan (10:24):

A lot of it is also, should I say, it's very people centric in the sense that there's a lot of processes which are dependent, there's a lot of dependence on people and it's not so easy to start automating them or putting them on the digital platforms without first having to sort them out even from an internal perspective.

Sriram Natarajan (10:49):

The other thing is also there what we discussed earlier, the gap between the front office and the back office. The front office is much easier. The customer facing digital implementations is much easier.

Sriram Natarajan (11:01):

So, from that perspective, I think whether it is gen AI or anything, anything as an experiment with a customer's love is great. But when it comes to actually making an investment and putting that across even in the internal processes, that's where the challenges are.

Jim Marous (11:20):

Well, it's interesting, you've written quite a bit lately for industry journals and such around actually moving beyond the hype of gen AI and AI in general and actually starting to look at building value with these tools.

Jim Marous (11:32):

So, how should organizations prioritize their deployment of gen AI solutions and is there a way to deploy these solutions in a composable manner where you're not doing it all at once? What's your position on that?

Sriram Natarajan (11:47):

I think that's a great point. I don't think there's any big bang approach that will work. You just can't get up one morning, get up in the morning and say, "Hey, I'm going to go all out for gen AI." It just doesn't make any sense. There are too many risks for the organization.

Sriram Natarajan (12:03):

So, it's important to identify what are the needs or where is it that they would get to see some real tangible benefits or real benefits. It's not just about gen AI or any implementation of anything should not become an IT project. It should not just become a project that everybody's doing it. So, I got to do it.

Sriram Natarajan (12:23):

So, if the institutions start focusing on particular need areas on where is it that they can get a return on their investment of time and money and people that they're making. And so, it has to be taken into pieces. It perhaps has to be a salami approach that I'll take it piece by piece rather than trying to say that I'm going to disrupt the whole environment.

Sriram Natarajan (12:50):

And that's why people are not very comfortable with words like disruption, all these things, which we like to kind of put around. But when it comes to implementation these words can be quite dangerous.

Jim Marous (13:09):

It's interesting. We've had a few guests recently that have really focused on the fact that if you're going to prioritize your digital transformation efforts, your AI efforts, whatever it may be, that you really have to start first by knowing where's your biggest challenge.

Jim Marous (13:23):

Maybe it's in the customer side, maybe it's in the back-office side, to know where do you have to apply the resource. Now that seems common sense. You would think that every organization, every leadership team would know that they need to start with where the problems are.

Jim Marous (13:37):

However, as you mentioned, there's so much noise in the marketplace about deploy gen AI for customer experience, deploy AI for back-office efficiencies, that the prioritization gets lost even though it's still there.

Jim Marous (13:54):

So, how is AI being used with your clients? How is it being used to streamline back office operations delivering efficiencies and better digital results? What do you see being done out there right now?

Sriram Natarajan (14:08):

One of the things that first we identify is that "Hey, which area is it that you start seeing the benefits? Or where is it that you want to see the benefits?" So, for example, it could be in deposit gathering or could be a digital onboarding of customers or new applications.

Sriram Natarajan (14:27):

So, you pick up some of those areas where you are strong on the front end but at the back end just needs to support what you're doing on the front end. So, some of those elements can help an institution to get started where they see immediate improvement in efficiency.

Sriram Natarajan (14:48):

The other area is about distribution of new products or new solutions. Often enough it's a little bit of a risk to fiddle around with your existing products or existing solutions. So, it's easier when you come up with a new product or a new service that tends to be much easier or at least more manageable than if you're disturbing your current processes.

Sriram Natarajan (15:18):

Again, also about managing the data. So, a lot of the automation or AI stuff is, it's all about managing the data and where if there's a lot of efficiencies that can come in the way you just manage your data or pull your data together.

Sriram Natarajan (15:39):

Because in any bank there are multiple systems from which there is data pulled in and put it into your data warehouse, and there are people running queries and trying to extract the data and try to make sense out of it.

Sriram Natarajan (15:52):

A lot of it if you can just focus on the basics without trying to do anything clever, there are substantial benefits if you just focus on some of these areas of data management or analytics or generating even CRM reports and those kind of like for like.

Jim Marous (16:13):

Well, when you look at AI with your customers again, you're a technology solution that really helps organizations build more efficient solutions and work with data and AI to build a better experience, both for internal and external audiences.

Jim Marous (16:30):

A lot of times, organizations not consumers, organizations believe that AI solutions actually are — it's either AI or human. How do you see financial institutions retaining the substance of the human expertise while implementing the powers of generative AI tools?

Sriram Natarajan (16:51):

I think it's absolutely important that human element or the expertise element is retained, and that is not lost. The biggest thing is that it should be the people involved in these processes who should be doing the process changes, or if they're implementing an AI solution, then it should be the people running those processes or who are being managing those processes, who should be doing it.

Sriram Natarajan (17:19):

Often enough, sometimes it just becomes that there is a chief digital officer, or there is an office of process excellence or innovation that sits outside these processes, and they manage these projects along with IT. It is kind of the people running those processes currently they kind of get put off by that and that's where the problems occur.

Sriram Natarajan (17:47):

Because the guys running the processor would want to justify saying that, "Hey, I'm doing it right." And many of these guys often believe that some of these processors, there is not much sense in automating them. I mean, that's the other thing that you need to get into.

Sriram Natarajan (18:01):

And often enough they're right. We had to look at the whole ecosystem of IT and infrastructure within the organization before you say that this process has to be automated from A to Z. Some of these processes may be fine with just 50% automation or 70% automation or parts of that. And that’s the thing that only the process managers can really talk about. And they’re the ones who would own that.

Sriram Natarajan (18:30):

So, I think giving ownership to the process managers would be the right thing to do. And of course, being supported by the innovation officers and IT of course, has to be there. But at the same time, if you leave it to the process experts, you'd find that it's a much easier sell as well as the benefits also start coming out much quicker.

Jim Marous (18:55):

So, when you're working with organizations, I know it's not as simple of an answer as I maybe make it out to be, but how do you figure out the ROI of an AI implementation? I know it differs based on what the solution is, but how do you see this actually being built?

Jim Marous (19:09):

Because a lot of organizations recently have said they don't see the ROI that they thought they'd see, but it could be that they're measuring it wrong also. How do you suggest organizations look at determining the value of a generative AI or an AI solution implementation?

Sriram Natarajan (19:27):

I think the important thing is looking at that ROI or the benefits of any data management or any implementation, it's based on the end results. And it's over a period of time, it's not just over six months or a year and I think it's a continuous process.

Sriram Natarajan (19:49):

So, it's important to kind of agree upon what is it the benefit that the CEO over six months, nine months, a year, two years, three years and if that is the kind of benefit, like a 50% reduction in cost or for example or a 50% increase in deposits gathering or any channel implementation or any segmentation that needs to be done better, or when it comes to credit risk it's a lot simpler of managing delinquencies or the benchmarking delinquency numbers against the industry numbers.

Sriram Natarajan (20:29):

So, it's important to set that evaluation criteria and tracking criteria very clearly. And also, to be tracked and what are the lessons that you learn and how do you change yourself if there are any changes that are required? So, nothing can be worse than you setting something which becomes an end in itself and there is no flexibility.

Sriram Natarajan (20:56):

So, primarily it is about setting those metrics and agreeing on that, and there's buy-in from everybody. At the same time, there is something that's tangible which people in the organization can see.

Jim Marous (21:10):

So, we talk about the benefits of gen AI and AI in general, but what are some of the biggest challenges you see financial institutions face as they try to deploy, let's say, large language models or an AI solution in the marketplace?

Sriram Natarajan (21:26):

I think one of course, is like I said before, they don't have the buy-in of the people running those processes currently, which is the first thing to do. It's not enough that you get project sanction or project approval by the board. I think it's important that there is buy-in in the organization. So, that is probably the first thing.

Sriram Natarajan (21:47):

The second thing is of course, is what is a regulatory aspect? One of the things that people often forget is that how is this going to affect my regulatory and compliance reporting and that happens.

Sriram Natarajan (22:04):

Often enough many projects get a bad press simply because it leads to a lot more work with the regulator, or there are questions raised by the regulator. So, I think that's another critical thing that people often miss.

Sriram Natarajan (22:20):

And obvious ones the challenges which everybody's facing is about misuse, about intellectual property rights that gen AI may be infringing in terms of are they amplifying any biases or are your models carrying anything that's … and may not be biases, but even some bad practices you're doing today doesn't just get kind of integrated into the model that you're building, and it just carries on. That's the other thing that people need to look at.

Sriram Natarajan (22:56):

And the important thing is about rigorous testing and continuous challenging. And often enough, you may adopt the champion challenger strategy where your current model, which you implement first is probably the champion. And then after a few months, you start out with another challenger, with another challenger.

Sriram Natarajan (23:15):

So, I think it's important to get that process laid out correctly and at the same time has the buy-in of all the people.

Jim Marous (23:24):

So, when we look at the back-office, you talked about compliance but even AI governance services. How can banks and credit unions actually benefit from stronger AI governance?

Sriram Natarajan (23:36):

I think what's important is to understand that the traditional rule-based models, they're linear, they're simple. There's enough data that you can produce to show how those models are working. But when it comes to AI, ML based models and all these new algorithms it is a sea change. You have to learn and adapt based on new data.

Sriram Natarajan (24:02):

Some of these are often black boxes which nobody knows. I mean, and often enough, again, like I mentioned earlier, the biases that creep into such data. So, it's important to keep these things in mind and learn from what is coming out of your own data.

Sriram Natarajan (24:22):

Like, as you implement and as the data comes out, it's very important to track the data and keep looking at it with a very rigorous review of everything that comes out.

Jim Marous (24:36):

So, when you look at security and fraud, I'm jumping a little bit here because there's so many things that generative AI can do. And when you look at the value proposition as opposed to the hype, we're in a continuous dichotomy where we're trying to balance speed and security as part of the transformation process.

Jim Marous (24:55):

What's the best way to deploy AI to balance that speed and security in the way that you were talking about it? How do you look at iterative transformations as you deploy generative AI or how do you really look at the balancing of those aspects of, I can do things faster versus I can do things better?

Sriram Natarajan (25:17):

I understand. Well, there are a couple of things here. One, you could adopt a kind of a parallel implementation that you continue with what you're doing today. At the same time, you pick some processes which perhaps are not high-risk processes, which doesn't have any immediate effect on anything. But you can review those benefits so that's often a safe way of doing it.

Sriram Natarajan (25:45):

And once you get those validations, then you start implementing it to other processes. But the thing about the trade-off between speed and security is always going to be there. And as the regulators get more and more clued in, as we have seen, and there have been the EU has announced an AI regulation which is about 892 pages long, but they have announced it.

Sriram Natarajan (26:12):

And UK has come up with a bill in the parliament about AI regulations or what they call as digital regulation. So, it's much wider than just AI.

Sriram Natarajan (26:21):

And so, this will keep coming in and there is no way that any organization can blindly go into implementation without looking at all the implications of what they're doing. And not just on the processes internally, but also externally and how the customers will react and how the regulators will react.

Sriram Natarajan (26:42):

So, it is often seen that if you start with some safe processes which are not directly affecting the customer, probably there's a good approach to go. A lot of implementations can be done like I said earlier, with new products or new launches so that it does not disturb the current setup.

Sriram Natarajan (27:06):

And also, looking at data management, how you are managing data internally. I mean, that's a good first port of call because most organizations any improvement in that is definitely on if you're implementing on data management.

Jim Marous (27:25):

So, when your company goes in to visit a financial institution for the first time, where does the conversation start? In other words, I'm an organization that needs to modernize my core. I need to change my legacy processes. I need to make my organization both more efficient plus better from a customer perspective and even an employee experience perspective.

Jim Marous (27:48):

When you're going into an organization that's just asked you to come in and talk about your services, where do you start that conversation? Where do you think the biggest return on investment or lowest hanging fruit is that an organization should start with as they look to build a value proposition for AI or generative AI?

Sriram Natarajan (28:10):

I think that our approach has been that we are process focused. So, it's not about implementing gen AI or it's not about the end result. It's more at looking at processes and more looking at process orchestration and what we would call as intelligent process management where you achieve operational excellence or through intelligent process automation, but always with human in loop as well.

Sriram Natarajan (28:42):

So, it's almost like I would say that we base it on with the people in mind or the customer in mind and use data as the foundation of what we are doing. And on top of that, we use AI or any other, any technology tool is just a tool on top of it.

Sriram Natarajan (29:03):

So, I think it is focus on the process, focusing on what is the benefits that you can derive at a process level and also speed in terms of how quickly can we do that? And also of course, what kind of costs and what kind of benefits do you come across?

Jim Marous (29:23):

Another component, we touched upon a little bit earlier, but when you're looking at the communication both internally and your customers I mean, or organizations, people, consumers, we don't like change. I mean, we get thrown off when our phone app changes a color on a component of what we're working with.

Jim Marous (29:42):

How do you work with organizations to actually help them communicate to employees and to their customers about the changes that are taking place. Because even something such as automation in the back-office has an impact on the end consumer, but also more importantly, has an impact on employees that touched these things in the past. How do you work to make it so that this doesn't self-destruct?

Sriram Natarajan (30:07):

There are a few things that we like to do with the institution we are working with. One is the fact that the digital transformation is inevitable. I mean, there's no running away from it. You have to do it. And that is something that the more that people talk about it and accept that, the better it is.

Sriram Natarajan (30:29):

The other thing is about like I said before, we would like to adopt a measured approach or a layered approach without having to go big bang. And we also like to start from where is the biggest need for the bank. And it often enough, it helps when the institution expresses their biggest need areas or biggest pain points and start there, rather than us going with a solution and saying, “You need to implement this.”

Sriram Natarajan (30:58):

So, I think we try to adopt a need-based approach so that one, it gets the buy-in. And two, also, it shows the benefit rather quickly. And we don't want to go with a solution and try to find a problem for it. But rather than we try to find a problem which is there, or need areas that if that can be solved, or if we can solve it, then that's when we like to go in.

Sriram Natarajan (31:24):

But in terms of continuous education, I think, like I mentioned before, the people running the processes have to be involved and involved heavily.

Sriram Natarajan (31:31):

And I think most of your back — is one, if you have those guys seeing the benefit and seeing that, hey, this will help me, or this will help them becoming should I say in some cases, even the heroes by adapting to this change rather than having it imposed upon them by another department or maybe just as is turned into an IT implementation project.

Jim Marous (31:57):

So, finally Sriram, as you look at the intersection of technology, data, and people in banking, what opportunities do you see on the horizon and the short-term horizon that are going to be the most important to financial services organizations?

Sriram Natarajan (32:13):

Well, I think this intersection of people, process and technology is ongoing. It's always been that except that the people keep changing, the processes keep changing because of newer technology. But this spiral, as we call it, is always there. There's no running away from it. And it is going to be there in some form or the other.

Sriram Natarajan (32:38):

I think it's important that a couple few things that I think everybody should keep in mind, one is stay away from hype. Because as we have seen over the last five years, we had every kind of hype, like with open banking or cloud computing, or digital wallets-

Jim Marous (32:58):

Big data. Name it all.

Sriram Natarajan (33:00):

Big data, blockchain, metaverse, all these things. All has been very prominently hyped up. And we've seen reports by all the major consulting companies saying, “This is going to be a $20 billion business in five years, et cetera, et cetera.”

Sriram Natarajan (33:18):

But having said that, if you step back and see what has really happened is that many of these, which were hype factors five years ago or seven years ago, are now mainstream, like cloud computing, for example. It's part of everyday existence. 10 years ago, cloud computing was seen as a huge thing.

Sriram Natarajan (33:38):

Similarly, app-based payments or digital wallets which are now pretty much part of mainstream now. So, I think it's important that whereas some of the other things like meta for example, metaverse haven't really taken off, it doesn't mean that it, it, it'll probably fade away, but it may come up in some form or the other, maybe after a few years.

Sriram Natarajan (34:02):

And I think it's important that we keep that in mind that it takes a few years for any height to materialize into a mainstream process or a mainstream system. And I think that process will keep continuing now. Because as we see now today's gen AI, three years ago it was blockchain and after the crypto adoption, perhaps blockchain is not talked about as much as gen AI now.

Sriram Natarajan (34:31):

Now within gen AI, who knows, next year we may be talking about quantum computing and the BIS, and everyone has already released working papers that how dangerous quantum computing could be for this whole ecosystem that we have built. But it comes to security, or everything can be ... should I say everything can be knocked down with quantum computing. At least that's what appears to be.

Sriram Natarajan (34:58):

So, it's important that we keep an eye on what's coming in next, but at the same time, it's important that we just don't get carried away and just not just say, "Hey, I'm going to implement a blockchain project whether it's in use or not. I'll figure that out later." I think that's very important.

Jim Marous (35:17):

You said it really well. I think at the end of the day, we can't get distracted by the hype, but what we have to do is find out where the value is strongest. Financial institutions today, I believe, have so far to go to catch up where there's a lot of value just sitting on the table.

Jim Marous (35:35):

If we focus on those value propositions, be it in the AI and the gen AI, in cloud computing, in blockchain, whatever it may be. But if we focus on where the greatest value can come from, we won't be distracted by the hype and end up as banking, as any industry ends up doing. Not doing anything because you get confused by all the options available.

Jim Marous (35:58):

I think some of the basic options are very easy to see. I think the back-office operations that are potentially there and how AI can help that, it's easy to see as far as building better customer experiences when you're a non-digital finance institution moving to more digital, they're easy to see.

Jim Marous (36:17):

Don't get distracted by the hype, double down on the lowest hanging fruit. And when you get to that point, that hype, as you just said, probably won't be hype anymore. And you'll be ready to implement the things at scale. And one of the things we didn't talk about, but I almost mention in every podcast, so I'll mention here, is what can you do at speed and scale?

[Music Playing]

Jim Marous (36:37):

One-year implementations are no longer valid. You need to get implementations in a three-month and a two-month and a one-month process. Break things down. You have the ability to do that and work with partners such as Quinte because you can't do it yourself.

Jim Marous (36:54):

Work with the specialists that are out there in the marketplace to help and then move forward at speed. So, Sriram, I really appreciate your time today and I look forward to talking to you again.

Sriram Natarajan (37:05):

Thank you, Jim. It's been very educative for me as well and enjoyed talking to you. Thank you very much, Jim. Jim and team, thank you.

Jim Marous (37:14):

This has been a production of Evergreen Podcasts. A special thank you to our senior producer, Leah Haslage, audio engineer, Chris Fafalios and video producer, Will Pritts. I'm your host, Jim Marous. Until next time, banking must embrace new opportunities for economic growth powered by powerful AI solutions. But we also must acknowledge the underlying challenges and dangers.

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