 Hello everyone, welcome to the special Cube presentation of the AWS Financial Services Partner Series. The topic today is redefining finance, the role of AI in banking. I'm your host, John Furrier with theCUBE, and today we're excited to be joined by Jareth Mendez, who's the Worldwide Banking Industry Leader at AWS and Omar Paul SVP, Senior Vice President of Product and Engineering at bamboo. Gentlemen, thank you for coming on on this awesome topic. Thank you for having us. Yeah, thank you. I mean, transformation with AI has been significant every single industry. We heard that at AWS's annual re-invent and we're seeing it all over the industry, people looking at their businesses and actually looking at this next wave. The impact is going to be significant. Everyone's looking at their architecture, looking at how they do business, where they can optimize this clear efficiencies, but more importantly, transformation. Omar, talk about what you guys do. Let's set the table. How does bamboo fit into the banking space as we start talking about redefining banking? Yeah, for sure. Thanks. So maybe I'll start with what Mambo does just really quickly. So Mambo is a cloud-based banking platform and we support 260 customers across 65 and so countries. And that includes Neobanks, your fintech and traditional tier one, tier two, tier three style banks, as well as mortgage lenders, for example. You go get a mortgage loan, it's powered by lender. And I think we serve 75 million plus end users worldwide. So that's what we do, run the cloud. We've been doing it for a decade plus. Congratulations. Got a next tailwind with AI, certainly here with AI booming. You guys got a tailwind. We do. We do for a couple of reasons, I think, because since we operate from the cloud, it becomes really easy for us to put something into our stack and then pass it on to our customers really quickly. Something that AWS is familiar with when it talks about agility and time to market when it comes to clouds, that's exciting. Yeah, yeah it is. There are many ways I think we can do things with it. Cherith, on AWS side, we had you, we just hang out at a reunion event. I sat down with Adam Sileski and he said, if you're doing data in the cloud, you're going to have a really good ride with this new edge-generated AI wave. Talk about the partnership with Bamboo from a perspective of the transformation, how their position and what you guys do in that relationship. Yeah, John. And first thanks for having us. I know I must say thanks for being on this. Look, I think I love the partnership with us at Bamboo. It's been a long-sounding partnership and really helping transform the banking industry from a callbacking point of view to a complex callbacking point of view. And I think together what we've been able to do is not just transform what's happening in the callback but how do you actually change customer experience and how do you use machine learning around the callback to really transform how banking is actually delivered around the world, whether that's in Europe, whether that's in the US or Latin America. And I think there's some really cool things that we're doing together. I think more importantly, what we've seen with Bamboo is that ability to lean in and leverage the cloud to really drive that transformation and that speed and agility that Omar talked about. And I think it's great that, you know, Bamboo's actually listed on Marketplace as well that makes it really easy for customers to now just consume the Bamboo services and Bamboo platform on running on AWS. Thank you. One of the things that we saw in the industry of the past year is if people had good data practices, whether it's hygiene or position, they were well-positioned with this generative AI wave, certainly from a low-hanging fruit standpoint, relative to getting it into the applications. Omar, you're at the front end of this. You're in the cloud. You know data and you're in finance. What are some of the challenges you're seeing across the industry with AI? Because you have a lot of data. It's, you know, banking is all about the application. You got money involved. The AI is a good thing. How do you see the challenges? What are the opportunities that come from that? It's a good segue to talk about challenges and data. I think when it comes to AI, as Charif will know, is that the quality of the data, the reliability of the data and what semantics you can drive from it and who owns it is vital to what you can do with it, right? And as Mambu is the transaction source of records. So if a bank somewhere, somebody makes a deposit or somebody gets a loan and an interest rate calculation has to occur, we have that transaction information. So it's good data. It's crisp data and it's owned. So it's useful. The challenges we tend to have with AI are one, the use of data and how you can project it forward. So some of it is personally identifiable information. So you have to watch what insight you can derive from it and who sees it. And then the second aspect when it comes to financial data is residency. And we have customers in 64 countries. And so certain pieces of data by law can't leave certain places, right? AWS is very good in providing resources and underlying infrastructure. For example, the recently announced EU sovereign cloud, right? AWS also has some really good hybrid solutions like Outposts where we think about how we can run data in country. So I think our first challenge is generally around that, the use of data in a responsible manner that meets laws and regulations. The second challenge I tend to see is the use of AI in making decision-making. Like that seems to be one of the, our customers tell us that using AI to make automated credit decisions, risk profiling, those can have a good impact because they can move quickly and you can match trends across. But you want to make sure it's fair, it's unbiased. They're responsible AI is a thing and it applies in finance. So that's our second challenge is that our customers want to make sure the use of it is fair and balanced without bias. So. Yeah, Charith, on the large language side we saw a lot of foundational models, certainly multimodal, but certainly on the language side really kind of easy areas to innovate where there's a lot of data, customer service, document processing, investment recommendations, analytics. But it's a lot of engineering going on too. So like, you know, the financial banking area, they're no stranger to data and how to engineer the data at scale, protect it. But there's a lot of now new things emerging. What's the customer situation with you guys? How do you see the macro challenges on the customer side from an AWS perspective? Yeah, I mean, it's really interesting that you face it that way, yeah. Like I think when I look at financial services, we've always been in the business of data and information and how to interpret that either for risk or service. So when we think about customers like from BVA, what Goldman and JP and Bloomberg are doing, I think like the use of alternative AI and the use of even traditional AI to really drive innovation in that industry is really accelerating. You know, I think Adam Slipsky said this earlier in some of the conversations that he had, like we're still here, we're three steps into a 10K race. Like we're early on the journey. Well, we see so many interesting use cases in terms of everything from, you know, how do you do customer service and how do you do human in the loop to really remove the friction in that customer service point of view? Well, how do you improve the efficiency of the knowledge worker? Whether that's doing document processing in the mortgage business or an investment advisor? And really, how do you drive that? You know, I think Verifrin and NASDAQ company talked about how they were actually supporting the KYC, AML process, so a transaction processing process where they were able to improve SAR reporting activity by removing the manual asset of collecting, summarizing, and writing it in. Now, I think what's important in that was also how they talked about that not every problem is a genitive AI problem. Some things are AI problems, some things are automation problems and some things are genitive. And that's how I think the banking industry is really adopting this and the financial services industry is adopting this in that context of how do we effectively use data and the models in an explainable and regulated way that helps drive a customer outcome? Now, it would be a miss if I don't talk about Code Whisper and what we're seeing in terms of code generation as well. I think there's a lot that we're seeing in this space in terms of helping reduce the effort from coders to do code generation, but also the self-documenting nature of that you can use to document code as well. And so I think there's some really interesting things when we think about code whisperer, code assistants and developer assistant tools that we're seeing in the market that has actually got to transform, speak to market with some of our customers in terms of how they deliver code. Okay, we set the table, market's there, my most positioned, well, Amazon's got good cloud for AI banking, reinvention happening. Now the transformation story has really happened. This Omar, this is where it gets really interesting and as you guys have been participating in the marketplace on the front end of this wave, you're at the front lines and you're making that happen. What do you guys see happening from a platform perspective? How does your platform work, I shouldn't say? And how does that support the customers, especially traditional banks and people transforming their legacy systems because with AI, you don't have to kill the old to bring in the new, you can bring them together. We're seeing that a lot where there's actually bridges happening, it's the new ways to do things. How do you guys work with customers that are transforming right now? That's a good question. I was going to jump off of what Charlotte said and I think we'll get to it later in the conversation as well, because you said a good structure for how we think about how to use AI in the market. Let me start with how Mambo worked. Hold on, I'm in on the comment. We'll come back to the questions. Yeah, so what the story of Charlotte was laying out, when we think about the use of AI and how we classify the use cases and how we can help our customers, it starts with internal productivity, which starts with the code whispers and how you can move the needle faster so you can just get better, you get more efficient with your resourcing. And then the second way we think about AI is how our customers interact with us. So knowledge bases, RFP proposals and things like that, maybe we'll get into that later. And then the third one is the use of AI into our products stack, which then makes it easier for our customers to use. So it's like a dress like just Kate, if you will, of how you think about where you go. Well, you've got touch points with your customer. It matches the height, the height matches the reality because this is why I love this wave because certainly there's a lot of hype around AI, but what you just mentioned is there's engagement points that you have directly now that you can engage on. I think this is where I think you see a lot of development, reinvention, again, back to the transformation. The next step is, okay, great, what are they doing? Like, how are they engaging with you? How do they solve some of their problems? How do they go to that next level? Because you can move faster now. Right. And I think I ripped off of that and I'll come back maybe to your first question, which is how do we leverage AI and it's, sorry, AWS and it's placed in the world of infrastructure and then take it to market because we're cloud-based versus some of the traditional legacy systems that we see because numbers cloud native, one of the biggest advantages we leverage with AI is its regional coverage, right? Financial institutions that use our service require us to be in a certain place with certain network, with certain latency, with certain capabilities, with audit, compliance, like all of those things, right? So we get to use a plethora of AWS services which we don't have to build ourselves. We get to use that and then go to market. So that's really, really effective. When you think about that in contrast to some of your older legacy systems, just as data centers transformed to AWS, you have these old cores that you can't put new tech in because the code's just old. Some of it's mainframe. And so like, how do you use that? Now you pull the data across and stitch it over. That's where we think one of our durable advantages is, is being able to leverage the cloud and AWS to move faster. So just to think about it, we have customers who, we have a customer that's ready to go live in New Zealand. And it becomes really easy for us to give them options on where they have data residency roles. And I get to look at the AWS region map and go here. How do we think about this? And it's so easy, right? It's such an accelerant. So that's kind of cool. Charles, how are you guys collaborating with Mambo to get customers to migrate over to the cloud? I think a number of ways here because I think I think I'm set up really, really well, yeah, like I think we're in this way. We're banking institutions and move financial. So the small part of it are migrated without including very critical core systems like the core mechanism. Now, I think what we do is a couple of things. Yeah, like when you think about what Omar saying, like that is at the key of this year. Like when we think about back book migrations or how to run independent course, we help customers. And I know we call the book Omar with your team in terms of how do you actually do large scale cloud based migrations. And that was a really interesting piece. I don't know, reminding out to that as well. And that kind of out buying kind of common pitfalls, et cetera that we see organizations have. I think the other part that's really great is like that ecosystem and the compostable banking. So that Mambu provides the core banking platform, but there are other activities like KYC, AML, like personalization, et cetera, that we can connect because all of those partners are built on the cloud. So it really simplifies the way that banks are actually taking this migration as Omar says, like from going from legacy and monolithic cause to being able to phase and stage the migration in a way that deep risks their organization, but also delivers on that customer experience and that customer promise. That's really key. And I think, I share this all the time with people that asked this question, like, doesn't this just make it easier for FinTechs? Well, no, I think it actually makes it easy for banking in general, because now everyone has access to really improve cloud and core banking capabilities that allows them to move much faster. They're not, I'm strung by their VSAM file system anymore. They can make a choice to use Mambu with the Composable Architecture running on AWS and move fast to meet that customer demand. In the same way that a traditional bank can do it, a FinTech can do it. So it's kind of evening the playing field, which is really core in the market. It's like, they're not buying a product. It's like the old days of cloud when everyone just used cloud, they built their own SaaS apps with AI. Omar was talking about how they use it internally. So it's kind of like, it's not like they're buying AI, they're using AI and then making their product better and for the customer, but also their product. So that's the next topic that we see in the industry. And this is what I want to unpack a little bit, Omar, if you don't mind, is that people are using AI to make themselves better using internally and using it to engage with the customers and then the customers are using it. So that flywheel is kicking in. So the next question is, how are you guys enabling the AI piece to make the products better on the development side? You got, you know, obviously the future of banking is going to be a lot different. How people engage with the data, data aggregation, the interface to the expectations of the user experience. So much is going on. How are you guys making the products better, enhancing the products? Yeah, sure. So there, I think when you think about the cloud and offering a product from the cloud to your customer, there are two or three really key things that come to mind. And the first one is being able to innovate quickly. So today's consumer, today's end user has this digital first experience, right? You get your photos, you're using a mobile phone, you're doing a thing, you get in use and take back. It's great, it's a great way. So what I want to do is when I want to make my payments, I have the same, I have that same experience. So the faster I can take a customer problem, embed it in my product and take it to market, the faster my bank can move. So the first thing that we do is improving our productivity in getting something going and AI helps us there. If I can write code faster because AI is helping me generate code. If I can deploy faster in a region because I have a configuration as code and AI has helped write that, right? We run some tech on AWS Lambda and it's a really cool piece of functionality and we're letting our customers extend the core. So today, if you will, you want to make a mortgage payment and now you have some extra money and so you send that mortgage payment, you send some more money and your principal changes, your interest rate calculation changes. But the rules are different in the United Kingdom versus Australia versus let's say Canada. And so if I can let my customer write the rule calculation really quickly, but I process that, I actually use AWS Lambda to do that. This is really interesting because if I went to the customer, I'm giving you serverless, they don't care. But if I tell them, you can now customers the interest rate calculation overnight, they will go, oh, I can run a campaign like Christmas is coming, you know, I'm waiving interest fees for a week. That agility happens. So we get to use AWS tech to increase our, to improve our productivity. So that's the first way. The second way it comes up to data, we respond to tons of RFPs. We respond to customer making requests. We respond to all sorts of checks and requesting to us and reports. I can use generative AI to find patterns in that response. So I can answer RFPs quicker. I can help my customer make a quicker decision when they want to migrate. And that's huge because time to market is now reduced. Omar, that's it. Oh, good. Got to finish that point. And then the third bit is the use of AI in data insights to help my customers make decisions. You have data locality. If I've got a customer which has branches in five countries in the EU, data laws allow me to use that, right? Now, if I went from the EU to the Middle East, I can't do that. But within the EU, buying patterns may be different because we are the transaction source of record, I can give customer predictive information that says customers in France tend to think about it this way, whereas your end users in Belgium think about it that way. When it comes to how often they pay, that can be gold. And I can point AI to do that for me, right? Some of it's machine learning, but some of it's generative. You know, what you just said, I was kind of like smiling because this comes up all the time on theCUBE. And you're highlighting and illustrating, I think, a new concept that's kind of emerging. Certainly, fintech's not no stranger to the word latency. I remember back in the day, high-frequently traders wanted that extra millisecond, nanosecond on packets because they wanted a trading advantage. Now the word latency applies to insights, productivity, workflows. So you're seeing an end-to-end impact, a new kind of latency advantage with AI. This is kind of like the secret hidden value proposition or I should say, not secret, it's what people are doing. That's where the action is. 100%, 100%, it definitely is. I don't know, what do you think? Well, I think if you're not in the cloud, you can move quickly. So you have to be in the cloud to begin with. I mean, you just said you spin up a Lambda's function, boom, so again, this comes up, I was talking about this at re-invent as well. The end-to-end workflows are now very much in play and it's just a whole another dimension. There's a latency involved if you can optimize pieces of it. It's an architectural thing. I think this is where it's exciting and this is where I think the redefinition will be focused on is that, I don't want to say engineering, but that business logic and Charth, you're seeing that when the cloud at the top of the stack as well, the applications being fed. Oh yeah, 100%. And I think, John and I and like the way I talk about is like that friction in the value chain. Like what AIML is now doing, letting you do, whether that's generative or predictive AI is letting you remove that friction that we had created over time in that value chain because of the technology issues, because of the processes, et cetera. So I'll give you some really cool examples, yeah. Like, and then there was so many and John, I know you got the chance to be a reinventer. You heard some of these live as well, but for those that haven't, like there was some really cool scenarios. Like let's just take the traditional way I am out here. Like Mastercard had shared previously, how they've been able to use machine learning to actually increase the catch rate of fraud by 3x and then reduce false positives by 10x. Now that application of machine learning into that fraud capture process is actually about customer experience and friction reduction because now what we're doing is actually stopping fraud where it matters and reducing false positives had unnecessary fiction and machine learning is actually able to do that. I think another great story is kind of NatWest and I love SAC story and storytelling just in general. But like, you know, he shared previously how NatWest uses machine learning to help customers in low searching areas. They were able to use the data and machine learning to identify that there were customers that were using fee ATMs when they were transacting and if they just walk three minutes away that they would be able to use a free ATM and not be able to pay charges. And they found that they were saving their customers in six month period, half a million pounds, which is crazy. You know, just the application of data and machine learning. But I think I also love the story that he shared at ReInvert, you know, being able to that, Omar, to your point, like further personalize that with content and imagery, like how we shared that he was using machine learning and journey of AI to be able to actually change the copy of what he was sending to customers, whether that's shortening it for a text message, extending it for an email, or actually just turning the internal language off it and making sure that it's on brand. Now, you could probably tell from my accent, I'm Australian. So like, how do you tailor a message to an Australian? You know, you start with, hey, mate. So you can see, imagine how the technology can actually help that. They'll get beer in the equation, they'll get their attention, right? Beer in the equation, nice, it's nice features. That's what we care about. Well, this is a great topic. And I got to say at the AI wave that's coming, again, we heard that, I actually interviewed the mastercard executives at re-invent on theCUBE. And the thing about that success is they're already in the game, it just made the game better, right? So again, the point Omar, you made earlier about using AI now, because you have existing stuff and then making the products better is kind of where the action is. But then also now the question is, okay, what's next, right? So Omar, as you look at the horizon, okay, as the engineering and product person, you got to look at the 20-mile stair and see all the possibilities that AI can bring to the table. What are you seeing in banking that's on the horizon that you're looking at? I'll see things like quantum, blockchain, DeFi, security, regulations going to get probably more predictable. What are some of the future trends that you're looking at that we should be paying attention to that you think customers will be capitalizing on? You asked me to dream here. I just go one year out then. It's just like a dream, dream big. I think one of the things we... Latency was a really interesting statement, right? As an engineering, as a geek, you think about latency as time between two hops on a network or something like that. But when you think about latency and some of the decision-making, today in the United States, when I do my credit report, it's a slow process, like three, eight, and bureaus get involved, it takes a while, it's irksome. And I wonder why it takes so long. And I'm thinking if I could make that faster, if they could make that faster as a consumer, my current state more accurately reflects my buying power and my credit risk. And that's going to help the lender with me. So I would really love to see how you can make point-in-time decisions almost instant, like at that point. We have a use case with a customer in Asia Pacific and they're in the US customer as well. So they're with a joint customer, they can make credit decisions within a day based on how the person has been paying back loans in the last week. And they do it on a per person basis across millions of human beings. And that is really interesting. So if that was just mainstream, that was just normal sort of bank and I get that. So I would really love to see automated point-in-time immediate decision-making because, you know, if I know my payday is coming and my payday is coming, Best Buy may be able to do a little bit better. Versus I'm not going to repay because I can look at one year's worth of history, but I can make that just fine. So I want that because I think it'll make the flywheel of commerce no faster. So that's one. The other one is digital currency. Banks look at it suspiciously, governments are not sure of course you have your Bitcoin and you have the types of coins around, but digital currency by definition is digital. It's in the cloud, it's and it's tech and you have to be able to move quickly. I would love to see some innovation there. The challenge I think that will come from governments. Confidence is key. I love the personalization angle. I think that's a fast decision-making, making things personal with the data. And I'll say, I mean your point really about the sovereign cloud, I think it's going to be a really big deal. And again, that's a strength on the AWS side. A lot going on on AWS too. We heard a little future trends there. What's the thought there in terms of AWS is intersecting the redefining banking future? Yeah, look, and I think about the future. I always think about, I kind of take a different method. Like I would say like, I think about what are those mutable things that are going to change? And then how does technology transform that? Okay, so customers will always need access to capital. Customs will always want to buy a house. Maybe the future of mobility changes to the way we buy cars. Those mutable needs or those durable problems will always be there. And it's amazing how then technology and specifically Gen2BI and AI is really helping transform that, yeah? So how do I get more personal? So how do I know what Charity needs at the point that Charity needs and the mode that Charity needs at? We're seeing machine learning, like what NatWest is doing, like what others are doing, really drive that transformation. And we see Gen2BI transforming that. Now, I think, as I shared earlier, like we're still so early in the use of Gen2BI, whether in financial services or across the board. So I think one of the things that we've indexed on and you would have seen this job in kind of what Adam was talking about, et cetera, was a choice, okay? I think we're way too early to know which foundational model will rule them all. So being able to have that choice that some things are better to use a translation model and other things are better to use a summarization model. We see that actually evolving in terms of what actually happens. Now, this is everything from compliance officers to loan officers, to CFOs, to customer experience, agents, et cetera. We see all of this actually being transformed because those things that were manual process or comprehension of data or comprehension of unstructured data actually changing so that people can actually focus on the value added activities, which is like if I was a mortgage officer, it's actually talking to a customer about affordability of their mortgage, not taking a document and typing it in. So like I see that really transformation. I think the other thing that we see, and you saw this with Amazon Q and the launches and the press releases around that, around bedrock agents, and a number of other things about how Amazon Q is being built into our Amazon Connect capabilities, et cetera. And what I think that's what's going to happen, and this is me a little bit crystal balling here, is I think we're experiencing the change of the human to computer interface, okay? So you've seen that on the personal side already, you know, I talked to my Alexa, how I interact with my iPhone. I think that same construct is actually now coming into what we see as banking where, you know, when you think about the document processing and how systems were engineered, I think you're going to see that change evolve in terms of how corporate employees actually interact with the computer interface in a different way. Instead of searching for a document to find a policy, they're just going to ask a question of a chat box and it's going to tell them the policy that's applicable to them as an example. Charles, great to have you. I'm going to riff off what Charles said, if you don't mind, John, because that was one of the things I wanted to talk about, the way the customer interacts, the user experience today. Like when your mobile phone came out, you went to apps and you did the Tappy thing, right? Before that you picked up a phone and then somebody sent you a letter in the mail. But today as a customer, as a consumer, if I can converse with my account, like literally I don't need, like the bank holds the account, okay? So you've got Mamo, it's got transactions that's all right. The bank holds the account, there's some payment work going on, but the consumer cares about their money and the account. I want to be able to talk to my account, like when did I do my last payment? Like I could change the way my consumer works with me. So the entire app experience can change and that's an interactive statement. So it becomes conversational. And so Charles' point was that, right? I was thinking about what could I do with the digital experience to my customer that they can take to their end users? And I think I would like to do something with AI there too. That's redefining, that's reinventing. And again, that's the application. I think we all saw and the non-techies, non-nerds that are our friends that are kind of scratching their head when they heard about AI. They saw chat GPT and they see you opening there, they go, that's magic. And we're like, really, it's kind of like, okay. But it helps educate. And I think why I like this wave is that it's an inflection point because the expectation of the users are changing, hence the experience has to follow to your point Omar, right? Why aren't we interfacing with my bank? Why can't I say, hey, bank, when's my payment due? Or how far are behind on me? What's my balance? What do I need to do in my spending? I mean, you can have a whole interactive agent relationship. Personalization. Actually, I just want the bank out the way. I'm talking to my account and my money. I literally am talking to my account and my money. That's what I'm doing. The bank is just a provider for it. So that is what I want. And again, it's early, early days, Omar. And you're at the front line. So I have to ask you, what have you learned so far in this journey as you look at this future of transforming how data is managed, how compute's going to be working, how applications call platform engineering services, how cloud's going to function in the environment. Yeah. What have you learned so far? It's a great question. I have to take off my engineering and product dreamy hat and put on my business hat when I have to answer the question some days. And it's a matter of return on investment, right? You can do lots of things, but it's the things that are going to change a customer's lives immediately, that are things that have the greatest chance of success. So you almost have to think about all the ways and then test which ones work. So there is a bit of a hurry up and wait thing, like a tortoise in the hay. You want to run really fast, but you know that maybe five to 10% will actually stick land and then give you that runway to take the next one. So I think my first challenge is how do I place my bets? Because there's a finite amount of cash, a finite amount of resources and there's a much larger scope of what we can do is which ones do we pick? And so I feel it's a bit like climbing a sandy and two steps up, one step down, so you stay patient, you keep inventing and I can move faster internally than my customers can. So if I'm going to focus on improving my productivity and my team's productivity using Gen AI, I'm going to do that first, right? Because now I have more capital in my balance sheet left over to run some experiments to my customers. So. Omar, great to have you on great insights. It's a little masterclass. I appreciate your commentary as senior vice president of product and engineering member. You guys, you're in the front lines, you get the product roadmap, you got to deal with all the engineering, you got to meet the customers happy. And of course, Amazon's big partner. Thanks for coming on this special edition of theCUBE. Appreciate it. Thanks John. Yep, thanks John. Okay, you're watching AWS's financial services partner series, redefining finance, the role of AI and banking. I'm John Furrier, thanks for watching.