Temenos’ Barb Morgan Shares How AI Is Reshaping Banking - Ep. 246

0:10Hello, and welcome to the NVIDIA AI Podcast. I'm your host, Noah Kravitz. Since its founding in 1993, Temenos has been on a mission to revolutionize banking. Its open platform enables people across the world to carry out their daily banking needs and for banking providers to build new services and state-of-the-art consumer experiences using AI and other cutting-edge technology. Starting a bit more recently, our guest has been leading Temenos' efforts to drive digital transformation from financial institutions across the world. In October of last year, 2024, to be specific, Barb Morgan joined Temenos as Chief Product and Technology Officer, bringing over 25 years of leadership experience and global product development organizations with her to the role.

0:54Barb has done a lot in banking and financial services, to put it mildly, especially with AI and cloud tech. In fact, it'll be better to ask her to tell us about her background. So we'll start there in just a second, except that I will add that Barb holds a Bachelor of Science in Computer Science from the University of Central Oklahoma. That said, Barb is here to talk about generative AI and banking, Temenos' approach to AI, and the importance of sustainability in the industry, for starters. So let's get to it. Bob Morgan, welcome, and thank you so much for joining the NVIDIA AI podcast. Thanks, Noah.

1:26Excited to be here. Excited to have you. All right. So I teased it in the intro a little bit, but maybe we can start with you telling us a bit about your background, your journey into working with AI and how you landed at Temenos. Absolutely. So I actually started with my hands on the keyboard. So I was a developer many years ago. And when you said 25 years, I had to smile a bit because it reminds me how long career has been. But no, you know, my career did start with the hands on the keyboard, but I always really enjoyed that link between what we were doing with technology and how that was really impacting the customer.

2:02And so as my career, you know, continued to kind of go through my journey, you know, I really gravitated towards those areas that had a strong customer centricity. And so I've spent about the past 15 years of my career focused in the financial services industry. So I've led transformations in inside banks within techs, you know, side by side with the banks and really focused around modernizing core systems, building innovative products and accelerating AI adoption, which you can't have a conversation anymore with without AI. But AI isn't new to it. We've been using it for years from fraud detection, risk modeling, automation.

2:42But what's really different now is we're seeing that shift where, you know, Gen AI has shifted the entire landscape where it's not about efficiency. It's really about making the bank smarter, more intuitive and bringing that, you know, hyper personalization to the clients. It's exciting when we talk to our clients. So, you know, as you mentioned, I joined Temnos in October and I've spent, you know, in my past four months, a lot of time out there just talking with the clients, you know, understanding what they're thinking about, whether it's the CEO, CTO, and they really want to get back to their customers, right?

3:18You know, whether it's having us run a banking suite for them on SaaS so that they can focus on their customers versus infrastructure, leveraging AI, but that customer centricity is really coming out and it's paired so nicely with the gen AI. It's interesting you mentioned the shift from efficiency and not that efficiency is a thing of the past, I'm sure, in the banking sector especially. But kind of that shift from efficiency to, you know, the customer relationship and how can we better serve customers. And is that something that you felt happening before Gen.A.I. in particular kind of took center stage over the past couple of years?

3:58Or is it something that you think kind of followed the technology that, you know, people realize like, oh, this is a great way to do all these things with custom, you know, personalization, customer service, what have you. and that trend sort of followed the tech? I think the, you know, the era of the chatbots, right? You know, which was kind of that first introduction. Yeah. When you were sitting on the technology side, you know, whether inside the bank or at the fintechs, we thought this is fantastic. And what we realized was it was actually really frustrating to the clients. You know, I don't know if you've ever called into your bank and it's like, you know, press one for this, press two for this, and then you just end up mashing that zero key.

4:40I'm just screaming representative into the phone. And so now I think what we've seen where technology is leading is that Gen AI can bring that human-centered approach and really bringing more humans back to that immediate touch point with customer. Because now all that data can get pulled instantly. And so you don't have to go through that representative of representative. And so I do think that we thought technology was leading when kind of that era of the chatbots and some of the different, you know, customer type efficiencies, you know, were playing out maybe five, 10 years ago. But now I think technology truly is leading and people are seeing it as an enabler versus, you know, again, just that cost efficiency.

5:31Right, right. Maybe you can unpack a little bit what it means from the banker's side to deliver a better experience and how how they're thinking about leveraging Gen. AI and related technologies. I love I I'm not even a developer, but I get on my high horse when people talk about Gen. AI as if it's the only kind of artificial intelligence. Right. So machine learning, predictive analytics, all these things. Those are not, you know, like we said, they're not going away. But from the banker's perspective, what are they excited about now? What are some of the, some of us banking customers doing to leverage this tech to deliver, you know, better experiences and make their clients happier?

6:08Yeah, I think, you know, we're seeing kind of two themes really kind of play out when we have the conversations with the banks. And the first one is, you know, how can they help their team? And then secondly, can they help their customers? So from the perspective of helping their teams, you know, they're asking us questions of how can we give our bankers instant access to insight? How can we leverage the historical data and make recommendations almost instantly? You know, how can we have those richer, more meaningful conversations enabled for our bankers? And so that's really kind of that internal look of his, you know, how can AI sit side by side and really be, you know, that plus one, right, in the conversation.

6:52I think for the customers, it goes back to that hyper-personalization, you know, whether it's tailoring loan options or giving them insights that they hadn't thought about before, both from historical but also predictive in the future. And then there's this speed that customers expect now. You know, everyone seems to have, whether it's, you know, chat GBT or perplexity or whatever loaded onto their phone, you know, everyone expects that instant answer now. And so they expect that everywhere. But we're in a highly regulated space. And so making sure that we do that, you know, in a very responsible way.

7:25But some of the things that we're doing that, you know, I really enjoy myself and try to entrench myself with the teams as much as I can on is instead of just coming up with solutions and then going out to our clients, we're doing a lot of co-design around the AI solution. So, you know, really saying, hey, here's five or six use cases, which of these stands out to you, which one or two of these do. Let's sit side by side. You know, let's think about how we can co-develop this together. You know, one that we're working on recently is an AI-powered solution that allows the product managers at the bank to create those financial products, you know, leveraging that data insight.

8:04And that was where that predictive future capabilities came into question, right? Right, right. Based off of the history, what can we predict? And we can give you the bank's knowledge. We have bank expertise as well because we serve a thousand different banks or more than a thousand banks. And so bringing that all together to think about future predictions, pulling it together and giving the right products to the right customers at the right time. And, you know, for me, it's also, we don't want to leave any of our customers behind. You know, one of the things that I really enjoy about what we bring to our clients is we focus on flexibility.

8:41And what I mean by that from a, you know, pure tech stack perspective is if you need to be on-prem, if they want to be in the cloud, or if they want SaaS, they have the optionality. And so, you know, partnering most recently, you know, with NVIDIA to bring AI to our on-prem banks has been hugely well-received. Just that ability to, you know, give that AI-driven analysis with those massive data sets, you know, that they have on-prem, but allowing them to control the security around it, allowing them to, you know, really not need to have the deep technical expertise to analyze. And so a lot of excitement there.

9:21You know, and it's good, too, because what, you know, we were talking with the bank the other day and he said our ability to have adequate data management, you know, is very limited. There's areas we've invested, but it might be 25 percent of our landscape that we actually can pull analytics. So these tools that can look across those massive data sets are really exciting. So to kind of take a step back for a second, if you don't mind, Temnos offers a platform And so when you're talking about, I just kind of wanted to unpack for the listener what Temenos actually does. And my understanding is, you know, it's services, but then you also have a platform where clients can build products for their banks.

10:03Yeah, so in kind of three different ways. So first, we have a end-to-end banking platform. And so for our banks within, like, let's say within the U.S., our tier three regional banks will come in and they'll take an end-to-end platform that provides all of the capabilities of banking. Almost like a turnkey banking solution. And that's where we see a lot of the adoption of SaaS bring us a bank, you know, even within kind of that neo-banking space as well. But then we also, you know, we also offer modular solutions to our clients. And so if you look at some of our tier one banks, they don't want to replace their whole platform.

10:44Right. And so but they may come to us and say, hey, we just want your payments module or we just want your originations. And so, you know, giving that choice and flexibility, whether they want the full platform or they want modulars within the platform. And then we also offer products around what we call point solutions. So things that may be add-ons that they may choose to build themselves and plug into our platform, like their digital interface. Or we also offer a digital interface that they can leverage with our suite. Gotcha. Thank you. And you serve bank customers of all sizes? We do. So we have over a thousand banks globally.

11:24So we have a very global footprint from the Americas to MIA to APAC. And so, you know, with that, it's everything from tier one banks all the way down to the neobanks. Are you seeing similar or different trends in terms of, I guess, both adoption rates from, you know, smaller banks and larger banks when it comes to AI tools, but also what they're using them for? Or is it everyone primarily is, you know, customer service, like this is the big, it's not just low hanging fruit, like it's a potentially really big win. Is that just where the focus is now? I would say AI is no longer an option, right?

12:00So they have to have AI, whether it's embedded, whether it's actually more of a feature functionality, maybe within their digital to allow their customers to click on some type of an AI agent. But I would say we see two different lens on this. So one is regionally. You have different areas that are more prone to adopt AI faster than other regions. And then from the customer tiering, what I would say is the, you know, tier one, tier two banks, absolutely. They want it embedded in their product. The, you know, kind of tier three banks, regional banks, they're really focused on the personalization that it brings.

12:42Okay. You kind of alluded to this a little bit, but is just gathering data and helping the banks kind of, you know, find and gather up and scrub and prepare and use. You know, you mentioned the example of, you know, only 25 percent of the data actually going to analytics. I'm getting that wrong. I'm paraphrasing. But is that still kind of the biggest I don't want to say hurdle, but, you know, one of the biggest hurdles to kind of leveling up success? It is. You know, one of the first conversations that I often have or something I always try to click into and probably a bit of my, you know, kind of geeky background leads me into is what does your data look like?

13:20Because in order to have responsible AI, you have to have your data in a state that you can actually leverage the tools. And for us, we really take that AI explainability very seriously. And so we spend time with our customers to understand what is the state of their data. And oftentimes what we find is if you think back to the transformation 10 years ago that banks were undergoing, it was a lot about how it looked and felt to the customers, not about transforming the back end. So, you know, many banks are made up of acquisitions, mergers over time. And so the front end looks really slick and great.

14:01And it looks like you're dealing with one bank and it's actually hitting six different banks in the background. perspective. And so sometimes it is, you know, working with our clients, you know, we have a Temnos data product, our TDH product, and it is bringing that data into a state that it can then be leveraged. And so, you know, kind of back to that optionality that we talk to our clients about, you know, sometimes we may just take a portion of the bank and, you know, really focus on getting the data streamlined and getting it ready to be able to use that embedded AI and then proving it out.

14:40And I think as we see, you know, banks are highly regulated. That's not going away. The regulations are going to get tougher if not anything else. And so, you know, we always keep that top of mind, making sure that, you know, compliance standards are absolutely, you know, embedded into our products that fully auditable. And so that's where a lot of our conversations end up leading around data is, are you ready? Right. I'm speaking with Barb Morgan. Barb is the chief product and technology officer at Temenos, a role she started within the last year, the current chapter in an illustrious career in global product development, particularly across banking and financial services.

15:21And we've been talking about generative AI in particular and the banking perspective on the chatbot revolution, if you want to put it that way. But just as a leaping off point, so much more, obviously, before chat GPT and the bots came in the world of machine learning and obviously since the pace. It's just been breakneck. Barb, I want to kind of shift gears for a second here, if we can, and talk a little bit about sustainability in the industry. I know that Temenos has a point of view on this and is actively working with client organizations to help them be more green. And I would think that your perspective, both in the industry and also in kind of the international nature of the work that you do with Temenos.

16:01Curious to hear, you know, both the company's perspective and what you're up to with clients to help them be more green, but also your take, you know, having been around the industry and around the world literally for a few decades now. So to me, sustainability, you know, it's not a trend. I think when it really started becoming part of the conversations, call it, you know, maybe 10 years ago where it actually was part of annual reports and things like that. I think people kind of questioned, like, is this long term? And now what we've seen the shift is it's really a responsibility of the organization.

16:33And so, you know, when we sit down and we talk with our banks and why it's important to us and bringing some of those solutions forward that allow them to be greener, you know, we talk about both what it means to them, where their focus is. So, you know, to your point, we serve banks globally. And so we see different parts of the world where, you know, it may be more around their carbon accounting or areas that may be like, hey, we really want to understand, you know, how our cloud deployments are helping. And so understanding, like, how can we drive a greener banking future? And there are always great conversations because they really often talk to the values of the organizations.

17:18And so you get to actually spend time in the cultural side of the bank. And what's really kind of cool, for the lack of a better word, about those conversations is when you can use AI to bring the culture forward and the solutions through something that really matters to them. It's a very rewarding solution, right? So, you know, like I talked about the smart carbon accounting, helping our customers track their carbon footprint, both through their how they're using their software, but then being able to offer that to their customers. You know, there's many customers who want to know, like, hey, how are the purchases that I'm making impacting my carbon footprint?

17:55Right. So not only are we talking to our customers, but we're actually impacting their end customers as well. So kind of to piggyback off that a little bit and open it up a little more abstractly, I guess, this is a big question, but I'll throw it at you. You can handle it. How do you see the future of banking being shaped by AI? And I guess the flip side of that is, you know, how do you see the future of AI growing in banking? But I think really, you know, there there's been so much we kind of joked about it for a second. There's been so much in the past couple of years with Gen AI and the pace isn't slowing down.

18:26And, you know, we have in our in our notes here to talk about AI agents, which is, you know, kind of the latest thing buzzword wise in the past few months. Right. But certainly not a new thing and certainly something that like could wind up really shaping, you know, the technology going forward. We'll have to see how it plays out. Do you have a strong view on what you think is going to happen with AI and banking and banking kind of being reshaped by the technology? And that can be, you know, short term, next couple of years, take it a little further out if you like. What are your thoughts kind of generally on this?

18:56Yeah, I think even before stepping into that, I think that the one thing that I really see and I think it's important to kind of talk about is the leadership of an organization really shapes how AI is going to be accepted, right? Yes. It's seen as a friend or a foe. You know, if you have your top leadership just talking about how much money we're going to save from AI. Right. It's a foe, right? But we're seeing the leaders of the organizations really look at AI as, you know, not as a threat and really talking about it as an enabler, getting people curious, getting people engaged, more and more organizations.

19:36And we do this ourselves, you know, whether you use the term eating their own dog food or French would say drinking their own champagne. You know, we've been doing that ourselves to say, hey, let's actually use this on ourselves. And then if it works well for us, great. We can start to expand it to our customers. And so when you start to see the leadership of the organizations, whether it's the CEO or any of the C-suite, talking about how they're curious, how they're using it in their daily lives, how they're, you know, getting in there and playing around themselves and thinking about how can I get rid of repetitive, time-consuming tasks and focus on, you know, deeper.

20:13matters and more strategic work, you start to see that really come out. And I think that's important in order for people to see AI as a tool to amplify the human potential, not to replace it. Are the bankers, the employees, to put it that way, are they thinking about it the same way? Is there excitement? Is there fear around job replacement? Any change, right? There's always going to be a bit of fear. And I think it's up to us as the banking experts in, you know, as partners to our clients and then, you know, working with their teams to help kind of show how the change does actually help them.

20:54Right. And so when we see that kind of pivot away from, oh, my gosh, this is going to replace me to wait a second, I'm actually going to sit side by side with AI. Plus one. Yes. Forgive me to interrupt. I just wanted to give you credit. I hadn't heard somebody use plus one to talk about AI before, and I love it, right? It's, you know, the co-pilot, whatever you want to call it, but plus one is great. Yeah. You know, and for me, I really focus on, I hate the word artificial intelligence because, you know, artificial, it's fake, you know, there's just that negative connotation. And so I often start out by talking with our clients about thinking about it as augmented intelligence.

21:37And that gives you that plus one effect. Right. And and then when you show, you know, the bankers, hey, someone's going to walk into your branch. You're instantly going to be able to know more about that customer than they know about themselves. Right. And you're going to be able to have a really deep conversation both about what's right for them today, what's right for them in the future, you know, how they're shaping those things. Their eyes light up. Right. Because, you know, oftentimes they would have to. The customer would sit in the lobby. They would do a bunch of research. They might be pulling paper files out.

22:11They're trying to remember, okay, this person has been with us for 10 years and they have a mortgage and they have a car and they have this. Right, right. Oh, gosh, what else could I offer them? When they can, through natural language questions, say, you know, how long has this customer been with us? What is their familial history? Right. So, you know, this may be a, you know, it may be a 30 year olds, but maybe their family's been with the bank for 25, 30 years. And, and then when, you know, their customer walks up and they say, Hey, it's been great. You know, we've served your, your parents and so excited to have you here with us.

22:47And we had these great, you know, we're looking at what loans that you have with us. We could consolidate those together. We could offer you a better rate. We have this great potential over here. They're exciting, right? Yeah, it really kind of, you sort of made real and listening to you talk about that. It's a great example because it makes me think of the sort of abstract talk about machine learning, AI tech, kind of, you know, freeing humans up to do what humans do better or best. And in this case, I can relate because it's not quite AI, but if it's not in my phone calendar, I forget it. Right.

23:21And so I can only imagine being a banker, having so many clients to serve. As you said, you know, I'm in the lobby waiting because the banker is doing their best to kind of do a crash course on my whole history with the bank to serve me because there's so many customers. I'm getting frustrated and waiting, et cetera, et cetera. Yeah. Let the AI do it. And then it just in real time, it pops up. And yeah, that's that's a great example. And today, I mean, the future is, you know, the future is going to be all about human and AI collaboration. You know, we're already seeing kind of AI agents, so to your agentic AI, right, you know, latest buzzword, you know, handling those routine banking tasks.

23:58But if you can set it up where it's doing your segmentation, then it's doing some product suggestions, then it's seeing as you offer those products, maybe it's actually shaping that segmentation. And so those agents are continually learning from each other, you know, and then you can bring that to that human collaboration. It's just exciting. You know, I think we'll start to see digital humans in making, you know, so that you aren't saying representative, representative. I was going to say, you know, we were talking about that before. I'm I'm 100 percent for, you know, the plus one, the augmented intelligence is also I like I like that way of thinking about it as well.

24:37Right. And I want to see humans use the technology to thrive and not talk about things like replacement, et cetera, et cetera. That being said, I'm an impatient person. And so I always gravitate towards the self checkouts at stores. And so if the automated banking menu could give me because I never call unless I have some, you know, weird question or like I've missed four payments and want to try to beg somebody to give me grace. Right. So, yeah, get the automated system to that point and I'll be happy. Yeah. Or even, you know, think about how great would it be if it, you know, your phone pops up because I know we all or at least I know my phone is always within and anywhere.

25:17anywhere. And if it just said, hey, Noah, looks like you missed your last payment. Would you like us to auto debit from your account and we'll forgive any late fees? And you're like, yes, done. Yeah, that's exactly the one. And so that proactive monitoring, bringing that, you know, so that you aren't even having to call in, right? You, you know, how much better would that be if it automatically, you know, reaches out to you? Yeah. If my creditors are, happen to to be listening to the podcast. I just made that example up. We're good. Barb, before we get to wrapping up, I wanted to ask you, and I think this is something I need to start asking guests going forward.