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Employees say:"Employees enjoy working with confident, effectiveness and management. My manager treats us fairly and encourages us to make decisions and work independently. TD Bank is amazing to work at. They inform you of any updates to products and services, understand your value, customer experience, core competencies, market niche and company culture. The work environment, people and workplace practices all help create a vibrant, positive, magnetic and infectious culture."

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"There is a genuine care about work-life balance, and there are multiple opportunities to learn new skills because of the multiple courses offered through TD Thrive. You can continue to grow throughout your entire career tenure."

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Best Large Workplaces for Women 2018_TD Bank

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DBS, Southeast Asia’s largest and most profitable bank, is betting big on artificial intelligence to define the next era of global banking. From algorithmic credit scoring and AI-enabled fraud detection to hyper-personalized customer engagement, the bank is embedding intelligence across its platforms to drive both efficiency and growth. With regional competition heating up and digital expectations rising, the challenge is no longer whether to adopt AI—but how to do so responsibly, securely, and at scale. CEO Tan Su Shan shares how the bank is navigating that challenge, what being AI-fueled means in practice, and how institutions across Asia can turn automation and analytics into real strategic advantage.

The Intelligence Dividend: How DBS Is Deploying AI To Reshape Regional Finance

Conferences

Artificial intelligence is transforming the foundations of global finance, accelerating innovation and challenging the way financial systems are built, regulated, and accessed. In this new landscape, the future of fintech must be global, inclusive, and collaborative by design. From real-time credit scoring for the unbanked to AI-driven regulatory sandboxes, the true promise of fintech lies not just in what it can build—but in who it can reach. How can we ensure fintech becomes a force for equity rather than division? And what steps can we take to invest in interoperable systems, embrace distributed intelligence, and embed ethics at the core of AI-driven finance?  

The Infrastructure Of Inclusion: AI, Fintech, And The Power Of Global Collaboration

We're going to turn to a conversation about how AI and global collaboration are reshaping the finance industry to be more inclusive and resilient. From expanding access to credit to rethinking regulation, we want to know more about building fintech systems that serve people, not just profits. Please welcome Global Finance and Technology Network co-founder and group CEO Satanhu Mohanty and Fortune's Clay Chandler. Thanks Andrew, um. So it's a real privilege to have Fernando here with us this afternoon. This is a man with a rare career and and a remarkable set of insights into how the world is organizing, you know, when we talk about AI and technology, we often are talking about uh developed economies. We're talking about Silicon Valley, we're talking about what's going on uh in uh in China, but there's a whole big world out there. There and a lot of people don't have access to this technology and I want to talk a little bit to Subundo uh about that but first I wonder if we could uh just have you explain a little bit about uh the global fintech uh network. This is for those of you who don't know who spent many years in the monetary Authority of Singapore and then it was uh in charge of uh Fintech for the central bank at Singapore and then. Uh, very entrepreneurial, helped the central bank develop this entity called Elivandi. Which launched the Singapore Fintech Festival. This is, I would say, probably the most successful Fintech festival in the world. It's been going on, how many years now? 10th annual. So 60,000 people descend on Singapore from all over the world for this festival and discuss every aspect. We went last year and we're thrilled to be a part of that, but it's it's. It's blockchain. It's talking about, you know, different forms of finance, but it's also made a real effort to look into the developing world, you know, the global South, and how FinTech can help people that are unbanked, gain greater access to financial markets. So I hope we can talk a little bit about that, but talk to us about uh. You know, the, the global fintech network and what it's evolved to be because it has 4 different functions, right? Oh yeah. Well, um, it's kind of an extension of my last 10 years in MAS because we are, uh, If you take the last 10 years in Singapore, we built a fintech hub, uh, which kind of expanded beyond the Singapore mandate and we went to the global ecosystem. We influenced a lot of the choices financial sector are making. So we decided that to really super scale that idea, uh, MS created this GFTN. Um, and, and the, and the core idea behind this is to create this global network of things what we are doing here, primary information, right, talking to each other at a scale so that we can capture around the world conversation around. The innovation and financial services. Not in isolation, but in partnership with the regulators, investors, founders, fintech CEOs and all. And to do at a scale is the first mandate we thought, uh, do as part of GFTN. Which is an Eliandi extension. Then we added, OK, now we have done this. Let's add the advisory piece. How do we advise low income, middle income, emerging market on a strategic policy, uh, for innovation and financial services, uh, almost replicating the Singapore success in many emerging markets. And then we bring the platforms, the public good platforms to the market, create a more connected equitable system around the world. And then we thought, OK, now we've done the first three pieces. Let's put some venture money to boost innovation to upcoming startups. This is a comprehensive ecosystem building framework. So this is really a unique entity that's part think tank, part advisor, part investor, part investor, um. And has this tremendous global reach. Now the uh Syntech festival here extremely successful, but you've extended it. You now have a version in Japan and you've also brought a version to Kigali, is that right? Tell us about that and why you've done that. Well, we wanted to take the narrative to the emerging market because it Good to have a program in Singapore or in Hong Kong or in Tokyo, but how do we take this program at this scale to emerging markets, to Africa, to Latin America, right, to deep in ASEAN, our desire is to take this conversation to every hotspot in the world because we know the future of financial services are not going to be. Created out of developed market, it will come from emerging markets. It is going to come from low income countries. They're going to redefine the future of finance with better innovation, better technology. We got to go there and have this conversation. That drove that whole network build up. So tell us where do you see some of the greatest, you know, opportunities for bringing AI and FinTech to uh empower uh the developing world. I mean, is it in India? Is it in Africa? Well, where are the kind of urgent opportunities? Well, I'm going to be a little more uh provocative here, OK, today, if you take every history of technology, we have seen different tech stack coming and solving certain piece of financial services. Now, financial sector. Historically, is the biggest paymaster for technology. Even today, look at the AI revenue breakdown. Almost 40% or 50% goes to the financial sector, but we are, but when we see our, our design, we are archaic. We are not the best. Tech tech tech stack in the world, right? So we pay the bill, but we get the bad technology. So where is the issue? The core to the issue of, of AI or any technology solving financial sector challenges is there's an elephant in the room. It is the trade market now. If you, if you say to somebody, I'm a bank, one expectation is they lend you money. Yeah. Now, when you are borrowing money from a bank, the banks ask for you from your collateral, right? Right. That's the standard process of lending. That model cannot be solving the global need for credit, right? The financial inclusion challenge is the lack of banks' ability to extend credit to low income segment, right? The low income segment has no collateral, right? What they have behavioral data, right, intend to pay back, right? They can if you give them money, they will do something about it and they'll pay you back. To me, that's the. Problem AI should solve for create credible, predictive golden source behavior data which will replace the need for a collateral for a bank to lend. If you don't solve the problem, AI is still solving the classic bank problem which is make our productivity go up. We can fire hundreds of people. I think that's not what we want AI to do. We want AI to solve the foundational credit challenge banks have. That's very interesting. So when you, when you talk about, uh, you know, solving. I mean there are two ways to see the behavioral data and I've talked to companies that are kind of viewing it both ways, but there's a set of companies who are saying, oh look, this is just about risk analysis. If we know that we're gonna get paid back our risk is 0% the lending to low income. Uh, uh, you know, uh, people, then it means that we don't have to worry about it. We can set a fair rate and, and we can lend it, but there's another category of people that I've talked to that say actually we want that behavioral data because it's valuable to us. We can use it to make a bunch of predictions. Uh, we can use it to kind of gain people's behavior. We can use it to sell it to other people. So I mean, do, are you advocating both of those things or just but I think, I think that's less of a problem today. If you ask a credit officer, they want to establish two things ability to pay, uh, intend to pay in, OK. Collaterals gives the ability to pay matrices, behavioral data gives the intent to pay matrices because the way we lend today, you go back to the default side, the non-profitting loan, all our intent, yeah, largely intent, right? So we are denying people because we don't understand the intent and at the same time we, we, we, we give people money and so that they have a bad intent later on. Now the gaming behavior comes. Also for the same segment of people who have collateral, you want them to use more of the collateral to borrow more, but banks will never lend to people who have no collateral. So my sense is that there's a two sets of problems. The bad use of data, I think we can't control because regulators have enough protections in place, but what regulators cannot. Is the bank's inability to lend to people who don't have collateral that has to come from technology that has to come from a different set of business models which I hope this AI revolution can get us there. It's really interesting. So to anchor this evaluation of behavior though, you've also got to be able to establish some firm. Identity matrix for the uh uh you know, the borrowers and, and, and consumers in, in low income countries. Now I know this because I spent a spent a fair amount of time in India and I watched Nandan Neilani build the, the Adhar biometric identity system and It's fascinating because I had several conversations with Naan about this, and he explained to me that look, you're all hung up in the developed markets about privacy. You want to protect your privacy, but you don't understand that if you're somebody out of a village, uh, that's come into the big city looking for a job in India. You don't care about privacy as much as you care about getting your identity on the grid so that you can be paid, so that you can receive government benefits, so you don't have to deal with all these middlemen and and money change money changers, uh, to get what's, what do you. Africa must be the same, right? How do we Well, if you define the last 10 years of financial sector, I say DPI, digital public infrastructure, API, how to abstract complex system and connect to third party value providers, and then comes the front end user experience, apps, the super apps, that's how defined the last 10 years, the code to. FinTech revolution was the digital public infrastructure which people like London design which took the burden of identity from corporates individuals to the state. Now the question is, do many countries trust that state to hold the identity? So the first single biggest friction to replicate the India success is the. for a state to hold the data of identity and determining your your transaction at a national infrastructure, state infrastructure sovereign level. But what is going to happen for the next 10 years is an interesting shift with the coming of the tokenization and the blockchains and the digital asset piece, we are, we are decentralizing ideas now. I think if there is a there's a mechanism of of able to take the text stack of tokenization and the ethereums of the world and create a decentralized ID on those networks, you don't need a state to control and deliver that trusted ID. So there is a hope 10 years from in the next decade to shift identity from a single single state controlled mechanism to a decentralized design, you know, in addition. Another big problem for for people from these uh emerging markets is is cross border remittances, moving money, right? They're not they they don't have bank accounts. It's not sort of simple, uh, to do, and especially when it involves going across border very challenges challenging lots of people kind of get in the middle of that and extract fees. You've had some recent success just for working with that problem. You wanna talk a little bit. I think this is the first time globally yesterday. Somebody from Singapore can use pay now, right? Go to a Fintech app, but we have now a thresh called Liquid Pay or one of them. Send money to India with a UPI ID from here with no bank detail to be to be no banks in the middle of that in the system in the user perspective, put the money put the UPI ID in a fraction of a second money goes to other side. Of course, the bank rails are behind, but the consumer experience able to send the money at a full price transparency. You know exactly what you're getting the other side with the price being displayed with a very attractive. Cost of transaction. The target is to make it below 3%, including FX, yeah, so you can send the money. It took us 3 years to build it, and it requires a great amount of regulatory policy synchronized, yeah, yes, and that that is now going to be the global template of how to connect cross-border payment across different banks using national infrastructure with regulatory policy synchron. Multiple jurisdictions Project Nexus, which BIS was heading in, it's actually going to happen in Asia. 5 Asian countries have come together, are taking this model of India, Singapore, India, Malaysia, and Singapore, Malaysia, Singapore, Thailand and creating a multilateral connectivity architecture called Project Nexus which will go live hopefully next year to do replicate at a multilateral level this whole experience of connecting. National IDs or identity reflected to different IDs to send money to each other without the complexity of banking systems. That's a huge issue. I mean, particularly within this region, right, especially emerging low income migrant workers sending money back home, right? They can now send small ticket size money without without a huge cost on the top of those transfers. Wow. So, uh, what's next for you in terms of priorities then? My biggest. Priority now is going to be upskilling. One of the single biggest challenge in our economy will be lack of job right. I think killing for the future is is an acute crisis now with AI coming, the number of jobs in the market is going to dramatically go down. If we don't upskill our people, I think we are heading to a massive economic disaster. It is already real in many countries, unofficially. 2 out of 32 out of 3 don't have a job or will not have a job, and especially for a young workforce, there's a very little hope for a job in the job market now. We got to get it right. So upskilling for the future will be my single. Biggest priority for the next 2 or 3 years. People often say AI is a very democratizing technology, but it's only democratizing if you've got skills and and I think AI can be a great tooling for our new future upskilling designs. That's fascinating. So, uh, the next, uh, FinTech festival is in November. Yes, and it's the 10th anniversary. We're going to celebrate last 10 years of super success. We're going to talk about the future 1010 years of possibility. I strongly encourage your audience to come and join us at Singapore 10th anniversary. It's very exciting. Thank you for joining us and sharing you. We really appreciate it.

AI is rewriting the rules of growth, trust, and competitive edge. From real-time underwriting and biometric ID to large language model copilots and predictive analytics, banking and insurance leaders are integrating intelligence into every layer of decision-making. This session brings together top executives from banking, insurance, and fintech to explore how AI is reshaping the financial stack—streamlining operations, deepening customer insight, and expanding access. What are the new design principles for intelligent finance? And as algorithms take on more authority, how do we balance automation with accountability?

The Intelligent Bank: AI And The Next Financial Frontier

In a landscape where consumer trust is fragile and tech investments must deliver ROI, executives face a high-stakes challenge: how to unlock the full value of data without compromising privacy or brand integrity.
At Fuel Up With Fortune in Cannes, Jarrod Martin, CEO of Acxiom & KINESSO, Kaveri Camire, CMO of DXC Technology, and Michael Lacorazza, CMO of U.S. Bank joined Fortune's Ruth Umoh to share candid insights and real-world lessons on rethinking data governance, optimizing investments, and reaching more consumers.

Executives Turning First-Party Data into a Strategic Advantage

Uh, so at Fortune we interview C-suite executives, uh, at Infinitum, and I'm sure you all have heard, uh, the adage that Ze is oil. Data is the new oil. Companies are obsessed with collecting more and more data. I, however, would argue I would posit. That most companies today are actually drowning in data, uh, but starving for insight. So first question, just to kind of level set and kick things off, how are each of you ensuring that your data strategy actually drives measurable performance, uh, and doesn't just become another nebulous call center? Perhaps you can kick this off, Kari. Sure, um, thank you for having all of us here. So for me, I think, uh, you know, I come from 20 to 30 years of just B2B world where everything is data. Data is around how we look at our customers, how we derive value from, uh, the customer set. But I think in my mind right now for my teams, especially in the era of AI that everybody's talking about, we got to really look at. Get rid of the noise, cut the noise, and really sharpen our focus on what truly matters and how you look at data that actually drives value. But for me, the biggest thing of that is customer centricity. There's so much data out there, but I think we've we have to pause and listen. And that's something that we're not doing that much of, and my biggest focus for the team right now is think about what our customers are saying. Think about what our customers want from us. Think about the problems they are looking to solve for, and that's the golden nugget that I want to look at from a data perspective, and there's a ton of technology out there today to advance that and leverage that for real value. Uh, quick follow up there, uh, you. Firm, what are customers telling you? What are they craving? What pain points are you trying to address? Yes, I'll give a quick view. DXC is 120,000 people. It's a global company. We're operate in 60 countries. Fortune 500 Fortune 500 company. Thanks Fortune. And it's, it's really about understanding. We are a global company at scale, but we also are very focused on local excellence, and what I mean by that is there are cultural norm. responses there's data sovereignty. There's so many discussions that are happening, especially around AI right now, but customers are asking for answers. Nobody knows all the answers. There is a lot of hype. There's a lot of promise, but I think for us what's really important is we work with some of the largest brands in the world if it's Mercedes, BMW, Allianz, um, you know, American Airlines United, the data for each of these customers is the life. Line because it's dealing with patients, it's dealing with passengers it's dealing with people's lives and they are asking us, and we're very fortunate that we've lived in the notion of trust. Our customers have trusted us for decades, so we've been around for a long time. We've been through many cycles internet, cloud, mobile, AI is next. Probably Quantum will be the next one, and I think the single thread of focus is we trust you journey after. Journey helping us with the innovation. So that's what they're asking us. Show us the path on how do we advance in AI, but how do we do it ethically and safely? And that's what the trusted left for DXC is really important. Yeah, well, we'll momentarily zero into that to that angle on trust. Curious to hear from you, Michael, how you're using data to really pull out meaningful impactful insights for the US banks. Yeah, I'd love to build on that point around customer centricity. Uh, you know, there, there's no substitute for understanding your customers, what's important to them, what problems you're solving, how they make decisions, how does their journey come to life, and our, our, you know, larger companies like us have massive amounts of data on our on our clients and we um. We have also an expectation that we're going to do something productive with it and something meaningful and valuable to to make the customer experience better. So it starts with that kind of level of understanding and then what we start to think about is what do we know in our data that can help us deliver a superior customer experience that differentiates us in the market. You know what kinds of solutions, products and services are most valuable at what moment in time and you know when are those channels, those touch points when we serve them, and that's that's the foundation of where we start. And then from there it's then analyzing and understanding what signals you have that can enable that because there is, there is a lot of noise and you have to find the nuggets that really make a difference. And when you say signals, what do you mean by that? Yeah, so you know, there might be some examples of how a client is moving, moving money. Um, that could, could signal some sort of a life change where they need a piece of advice or guidance and enabling to do that. And so if we understand that from just how they're working with us, then we can proactively reach out and be of service. I was just going to say like these are all really great points. I think you know advertisers are obsessed with too obsessed with collecting data to your question. And I think focusing on what matters is really important and what drives results and being customer centric is really important as well, so I'm not going to duplicate all of those really great points. What I would say though is on top of that is an enabler of these things is being able to connect the data together. So most advertisers would say they have more than enough data to make decisions, but they don't have it in a form that they can use to make decisions with. And so by connecting that data together, what do I mean by that, creating an identity spine that allows you to connect different lines of business together to connect different touch points between earned and earned media channels that enables you to really recognize the consumer and treat them as an individual and recognize their journey with you. That's really the key for me on top of these great points is having that connected ecosystem that can then power both both personalization at scale but also. Um having a way of Allowing consumers to control their own destiny. So if you've got a single repository of data that's connected, if a consumer wants to opt in or opt out of anything, it's a really simple process to give them that control and to show them in marketing that you're listening to their feedback. I say that that's the whole point. Like, are you listening? You're hearing a lot, but are you actually pausing and listening, and that's your point on like having those connective tissue and the dots of like, let's look at all the different facets and take a minute and listen. Yeah, I mean an example might be like email can be a bit of a bugbear for some people where email doesn't cost much money to send, so everyone gets their inbox spammed, right? But email might pick up signals from the consumer about who they are and what they like. Let's just say they opt out of that email. Now what the smart thing is to do is to not send them any more emails, but. But then recognize what you've learned from those emails to better inform the other channels that they still want to speak to you through. If you do that, you're managing the best of both worlds. You're creating the customization, but also the control. Uh, Kaveri brought up trust earlier and that's really at the crux of this conversation. So let's lean in, uh, there, Michael. I want to turn to you. US Bank, obviously, uh, a financial institution ostensibly highly regulated, so you cannot afford to treat data carelessly. Uh, what is your internal scorecard for evaluating third party data partners and, and just what's your overall framework for trust, for quality, for compliance, and how that shapes your marketing? Yes, and at US Bank, trust is at the center of our brand. And it's one of those, it's one of those elusive type of, you know, emotional connections where it takes a really long time to earn it and if you make a mistake you can lose it really, really quickly and data and caring for that data is at the center of that and the expectation that we will care for it, so. With respect to how we onboard third party data, we have a very, we have very well established processes and procedures to to to evaluate, you know, the veracity of the chain of control. Do we have the proper usage rights, do we have the proper, you know, IP protections and so forth, and, and so for for a partner that ends up in our ecosystem, you know, they, it's a pretty high bar. Uh, let me turn over to you, uh, Jared, because you, you alluded to this at the top of the conversation. Everyone is chasing personalization at scale. I think we're in the era of hyper personalization. What does it actually take to do that well, kind of make this really sticky for for those in the room? Yeah, I mean, personalization at scale is an easy thing to say. It's a difficult thing to do. I think we talked about creating a connected data environment to enable that personalization. That's a really key infrastructural element. I think we also talked about marrying the personalization with the control that the consumer has so that they can opt in or opt out whatever they like. I think the other thing to think about is that a lot of advertisers might personalize because they can, not because they should, and I think we should think about what elements of personalization are actually driving results versus what elements are driving complexity. The way I think about things is, is we need to bifurcate our thinking between how we operate as a business, as an advertiser versus how we appear to an end consumer. We can be increasingly sophisticated under the water. But we need to be increasingly simple above the water and increasingly customized to that consumer. So personalization at scale is around simplifying the end user experience, focusing on what matters to that consumer, while having complex decision logic and data sets to sit underneath that. A similar question for you could vary, you know, Seeds, as you already noted works with clients like Mercedes-Benz, like United Airlines, uh, like Lincoln Financial. These are all brands with vast and complex, uh, data ecosystems. So how are you helping them to unlock value while still balancing innovation with integrity? Yeah, so I think this goes back to what you were saying. Um, I think we would not exist if our customers. did not trust us and value us for taking them to these risk-driven journeys, uh, in technological advancement. tech is moving at such a crazy pace and such a fast pace. Um, the way we look at DXC and how we are operating with all of our customers is, uh, it's not just about ROI, it's trust ROI. Because if we don't have that trust ROI, it's not going to work for us. We have governments, we have, uh, you know, like I was saying, we have airlines where lives of passengers matter. We have banks where people's, uh, you know, money is sitting in those banks, and I think a slight glitch in the system is not just unacceptable, it is existential. You can't operate in an environment where you don't have that. So from a marketing perspective, I'll just say something as simple as we just had a client advisory council a couple of months ago where we brought closed door sessions, something it's sometimes it's even nice to just go to simple things that existed in the past that we don't do anymore. Let's get in a room, talk to our customers. And I asked them a simple question, where you're headed? What is your biggest challenge? And the third was, how do you like to communicate with us and how do you communicate on a daily basis? What is the tool or the platform that you use the most? Is it long lead blogs? Is it videos, is is a text? You will not believe they all said we don't want long form things first. We would prefer to look at videos, and this is C-suite, CIOs, CTOs, CEOs. Time is money, and they're constrained. So I think those are the things that we're starting to do is really listen to hear how our customers who are some of the most, you know, in the most mission critical spaces are consuming information. They all said video, start with small video. Give us the TikTok version of the story, and then if you want to lean in more, we will come to you. They want digestible. You said you also asked that cohort, what are the biggest challenges and obstacles? What did they say were the were the pain points, any trends that emerged there? So in the room without naming, there were banks. There was a bakery, believe it or not, they are using robotics to distribute thousands and thousands of loaves of bread. And they are all trying to figure out how do they drive efficiency with AI. A lot of them have very private focused networks with an AI right now because they're not kind of sure yet of is their data going to be exposed. So this goes back to my point where they are working with us and leaning on us to show them, uh, opportunities and direction of. Take advantage of AI, but our goal is to take advantage of AI in the right ethical way, which is going to take them to the next level. So that was a big question like show us how, how do you find the balance uh between helpful personalization and perceived surveillance, you know, the, the privacy conversation, and he thought, Michael, and I'd actually be curious to hear from all of you. Absolutely, yeah, yeah, well, you know, surveillance is for me if defined you're listening in in a in a sort of cynical way, right? And um with with if if we get personalization right, we're listening to our clients, we know it's important to them and we're using the data and the signals to to improve their experience with us to deepen those relationships, um. You know, I, there are several examples out there where, you know, one was, you know, a woman was shopping for, you know, one particular item and it signaled that maybe she was pregnant and then the brand starts to serve diapers and it's like to me that's that's crossing the line, right? And that's like that could be felt as too invasive and personal and surveillance, but But um, but if you but if you find a way to do it where um you're delivering value, you know, value exchange for the information that you're acting on, then you've kind of got it dialed in. Let's talk financial metrics um as as as we begin to wrap this up, uh, what would you say is the most important question a CMO or even a CEO should ask before making their next big data, uh, or more tech investment? I would say um. Is it going to simplify and connect what I have today, or is it going to complicate and further add cost to my business? I think when we talk about marketing ROI, we talk about the cost of the communication versus the benefit that that communication drives. What we don't talk about is the underlying cost of the infrastructure that's used to create those communications, and we don't actively manage the total cost to try to make it work better for us. If we can unlock the billions of dollars that are spent in Martech around the world. And make those work harder for us. That marketing ROI on a net net basis will go through the roof. I have two simple things I ask my team all the time. Show me how it's going to get us closer to the customer. Show me. Give me examples right off the bat. And the second one is, um, let's not just invest for investment's sake. Ask yourself what's the outcome that you're getting at, because I think sometimes we're All just moving at such hyperbolic speed that we're not stopping and thinking, what am I trying to get at and what these are some very basic questions, but I think oftentimes to your point where you said, we're investing and we're not thinking about it, you have to ask yourself what is the outcome that we want to get at, um, you know, and for us it's global. Most of our implementations and executions are global, so you've got to really, really be thoughtful about what's your outcome. Call us out, please. Yeah, so. I would say before you you contemplate adding on any sort of analysis tool or technology on top of what you're trying to achieve, is your, do you have the right data sets and are they in the right condition and format to be able to act upon? Because if you don't have that foundation in place, then you can put any fancy technology, AI, whatever on top of it and it won't matter. So you start getting that, get in that place first, then you can start to begin to contemplate what you put on top. Fantastic way to end. thank you all, uh, lots of great takeaways. Uh, trust is not optional when it comes to data and you have to be outcome oriented, really cutting out the noise. So thank you, thank you, thank you.

TD Bank NA

TD Bank NA in Fortune Rankings

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