Greg Ulrich, Chief AI and Data Officer at MasterCard, discusses the dynamic landscape of artificial intelligence in the financial services sector. He shares his journey from working in the non-profit sector to his current role at MasterCard, highlighting the exciting opportunities AI presents across various domains. His work involves differentiating between causality and correlation, aiming to provide efficient solutions that maximize impact using advanced data and analytics.

The conversation delves into how MasterCard utilizes AI, including traditional machine learning and emergent generative AI, to enhance fraud detection, personalization, and operational efficiencies. Greg explains how MasterCard's AI solutions strive to make commerce safer, smarter, more personal, and more robust. This involves leveraging structured and unstructured data, deploying digital assistants, and integrating generative AI into their processes to improve decision-making across financial transactions.

Main takeaways from the video:

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Key Vocabularies and Common Phrases:

1. holistically [həˈlɪstɪkli] - (adverb) - In a way that is characterized by comprehension of the parts of something as intimately interconnected and explicable only by reference to the whole. - Synonyms: (comprehensively, entirely, completely)

When we think about things in financial service, think invoice, reconciliation, bill pay, you'll have these things come in as PDFs, you'll have them come in as images, you'll have them come in as text in a variety of ways as we can start looking at things holistically, in different modalities and still come up with a single source of truth.

2. interventions [ˌɪntərˈvɛnʃənz] - (noun) - Actions taken to improve a situation, especially a medical disorder or a public/social issue. - Synonyms: (actions, measures, steps)

I was trying to help organizations understand the efficacy of different interventions.

3. correlation [ˌkɔrəˈleɪʃən] - (noun) - A mutual relationship or connection between two or more things. - Synonyms: (association, connection, relationship)

And that was really around how do you differentiate between causality and correlation?

4. modality [moʊˈdæləti] - (noun) - A particular mode in which something exists or is experienced or expressed. - Synonyms: (method, manner, mode)

One around sort of multimodality and being able to look at not just text, but the integration of text and images and voice and video and for a Lot of instances that we think about, like you think of what the insurance companies are now able to do where they can piece together photos of an accident with the data from the policy, from the information from their manuals and what they have internally to come up with a much faster and more effective estimate

5. fraudulent [ˈfrɔːdʒələnt] - (adjective) - Involving deception, especially criminal deception. - Synonyms: (deceptive, deceitful, dishonest)

I mean we've been monitoring transactions to try to provide insights to merchants and issuers about if a transaction is fraudulent or not.

6. multimodality [ˌmʌltimoʊˈdæləti] - (noun) - Refers to the use or integration of multiple modes or methods of communication or expression. - Synonyms: (multi-method, various modalities, multiple forms)

One around sort of multimodality and being able to look at not just text, but the integration of text and images and voice and video.

7. scalable [ˈskeɪləbl] - (adjective) - Able to be expanded to handle increased workload. - Synonyms: (expandable, extensible, adaptable)

Where do we think that there are opportunities? And I bring that to the business partners in hr, in finance, in fraud, securities, in open banking, in our core payments business to talk about these opportunities.

8. efficacy [ˈɛfɪkəsi] - (noun) - The ability to produce a desired or intended result. - Synonyms: (effectiveness, efficiency, potency)

I was trying to help organizations understand the efficacy of different interventions

9. orchestrate [ˈɔrkəˌstreɪt] - (verb) - To arrange or direct the elements of a situation to produce a desired effect. - Synonyms: (arrange, organize, manage)

How do you balance innovation and speed with the right governance?

10. reciprocal [rɪˈsɪprəkl] - (adjective) - Given, felt, or done in return; an action or feeling that is mutual. - Synonyms: (mutual, shared, bilateral)

You have this two-way street and communication where we get the best ideas.

How AI is Powering Payments, with Greg Ulrich of Mastercard

When we think about things in financial service, think invoice, reconciliation, bill pay, you'll have these things come in as PDFs, you'll have them come in as images, you'll have them come in as text in a variety of ways as we can start looking at things holistically, in different modalities and still come up with a single source of truth. That's very exciting.

Greg, thank you so much for joining us on in the Vault. It's great to see you. Thank you. Mark. Yes. Obviously, you and I have had the pleasure of knowing each other for a bit now, but maybe for our audience, you know, would love for you to share a bit about your background and your journey. Yeah, sure. Thanks. So, Greg Ulrich, I'm the chief AI and data officer here at MasterCard, and it's obviously a tremendous time to be an AI. We have so much excitement, so much energy, so much opportunity. At the same time, like, the future is still to be written on this. We're very early days and like, what the future is going to hold and more importantly, the path we take to get there is still unwritten. So it's an awesome time to have this job.

My journey in this space. Well, I guess it started in university, but I really got into it in working in the nonprofit sector a couple decades ago. I was trying to help organizations understand the efficacy of different interventions. If you wanted to address clean water in certain communities, what's the best way to do that? Or if you want to address malaria, what's the organization as the best approach? How do you determine what intervention, what strategy, what approach, what organization is having the most impact on whatever cause you care about. And I realized the data, the analytics, the ability to analyze that in the sector was limited.

And I ended up working at a company after that called apt, or Applied Predictive Technologies. And that was really around how do you differentiate between causality and correlation? And how do you identify the real impact of any intervention or any test, any experiment, anything you're doing differentially in a market? And it has a lot of power for the nonprofit sector and what we're looking at. And I learned two things there. One, how able you are if you have the right data and analytics to understand what's working and measure that impact. And also how easy it is to misuse data and analytics if you're not careful about it.

And long story short, APT got bought by MasterCard about a decade ago. After that, I did a variety of roles here in our services division. And then starting in 2000, I led strategy M and A and Corp Dev for about four and a half years. And then ironically I was at an A16Z event earlier this year and our CEO called and asked if I would do this AI and data role. And I've been doing this since Q2 of, of this year and loving every minute of it.

So it's fair to say we're solely responsible for you getting entirely responsible for everything. I think that's the takeaway. Yeah, exactly. Amazing. Well, we're super excited you're in the role and I think we're in total alignment that obviously, you know, there's never been a better time to be thinking about some of this stuff. I'm curious. A company as large and important to the financial and money movement ecosystem as MasterCard surely has been using artificial intelligence for a long time.

But you know, as of call it 2022, with the onset of ChatGPT and things of that nature, we've now kind of moved into this wave of specifically generative AI. Yeah. So I'm curious, how do you think about the sort of distinction between those two sets of technologies and like the various sets of opportunities that exist for MasterCard today? Sure. And you're right, we've been using AI for decades. It's inherent in everything we do in fraud detection. Right. I mean we've been monitoring transactions to try to provide insights to merchants and issuers about if a transaction is fraudulent or not, help make better decisions and make sure that the ecosystem moves in a secure and efficient way.

And artificial intelligence behind a lot of that as well as what we do in personalization, forecasting intelligence products we're giving to our customers. So this has been inherent to what MasterCard has been doing for quite some time and obviously two years ago or you know, two years and maybe a month ago with, with the advent of, of gen AI really to the forefront is created new opportunities. But for us, like what technology to use, it's, it's really dependent on the use case.

If you have structured data, you're doing forecasting models, a lot of the fraud management, traditional artificial intelligence, machine learning is going to be more efficient, more effective and certainly more cost effective as a way to do it. When you're dealing with knowledge management, when you're trying to synthesize, when you're trying to create new content, we're dealing with unstructured data, then you're looking at newer and different techniques.

So for us it's very use case dependent and we're trying to understand what is the problem. We're trying to solve and then what is the right analytic approach to get there? And for some of it, we have fraud solutions that are based on machine learning that we've been using for years, where now we're able to bring in sort of new features using Gen AI techniques to make an existing product better. Gen AI, if you will, is a feature as opposed to a product in other places we're using it for digital assistants and other things where it really is a Gen AI based solution. But again, it comes back to the problem, it comes back to the use case and what we're trying to solve for 100%. Yeah, no, that makes a ton of sense.

And I imagine if you were looking at the whiteboard of possibilities of all the places you could apply this technology, the list is sort of never ending within the context of MasterCard. But as I was preparing for this conversation, I spent some time looking at some of the announcements you all have made. And it sounds like two of the early things you've shipped with respect specifically to generative AI have been to the point you just made. One, a digital sort of onboarding assistant for some of your customers.

And then two are specific new fraud capabilities that maybe actually are leveraging the generative parts of AI and not just the traditional machine learning techniques you alluded to. Maybe you can just tell us a little bit about how you sort of landed on those two things as early use cases to launch in the organization and how they're going thus far. Sure. So I'll get to those two specifically, but maybe I'll give you the frame of moreover, how I think about where AI has opportunity for MasterCard and I'll bring it into four buckets as shorthand. They're safer, smarter, more personal and stronger.

I'll explain what I mean by those, but as I mentioned earlier, like where we started in our AI journey, where a lot of what we're doing today is in fraud management, fraud detection, fraud identification, and how we're doing things to make the ecosystem secure. That's on a transaction by transaction basis. Again, how do we provide intelligence to determine if a specific transaction is fraudulent or not? But also how do we look at scams, how do we look at pain points across the entire ecosystem to see if there are hotspots in the network where we're seeing more fraud and there might be malicious actors and how do we shut that down and how do we deal with it? So that's how we make the commerce ecosystem safer.

The second one is how we make it smarter. That's how do we route transactions in the most efficient way? How do we provide insights to acquirers, issuers, merchants to help them optimize their portfolio and deal with their own customers in the most effective way? Providing intelligence based on the data we see and the analytics we can deploy. On top of that, when should you authorize a transaction, should you retry a transaction, et cetera? The third one is how we make the ecosystem more personal. So we are a B2B2C business. We don't have much in the direct-to-consumer realm, but we do help our partners, the banks, the merchants, personalize things so they can provide the right offer at the right time to the right customer.

And we have software tools that do that. And a lot of those rely on artificial intelligence. And that's the more personal element. And all of those are external. How do we create value for the ecosystem? How do we create value for our customers? And the fourth bug is how do I make MasterCard stronger? How do we improve our own operations? A lot of that is on the productivity, the efficiency pieces it's around how do we create put knowledge in the hands of our employees? How do we make their roles simpler, be it software engineers from coding our sales team by providing better access to information.

And those are the sort of areas in which we're deploying AI and particularly gen AI overall. So I'll put these things that we've shipped into those buckets. The first one around fraud. The product we have is called decision intelligence. And that's where in the milliseconds we have between when a transaction passes through our network, from when you tap to buy something in a store, it passes through MasterCard to go to the issuer.

We're providing a score on that based on how likely that is a good or bad transaction. We use Genai, recurrent neural networks and other technologies to effectively add features to that by understanding not just your transaction history but the overall ecosystem of, of of merchant behaviors and create basically a merchant vector database in there to help understand whether or not this is likely to be a good or bad transaction, even if you've never shopped at that merchant before.

Because we can see like behaviors from other consumers. And so it's, it's basically adding a feature to the model to give us a more accurate score. And we've been able to prove that over time. So that's one of the things we've done in fraud. In personalization we have something called Shopping Muse which is basically enabling the in store experience online.

So you have a chatbot effectively where you can type in your own language and ask for recommendations. I'm going to be on an A16Z podcast with Mark. What should I wear? And it'll give recommendations or I'm going to a wedding. So and so what should I do? And it's allowing you to interact like you would with an in store agent using all of Gen 8 techniques to make again personalization a better experience for the end consumer.

And then the digital assistant you mentioned, one of the things that we're trying to do is make it easier for our customers, the banks, the merchants to consume and integrate MasterCard products. And these have a lot of technical specs, a lot of steps sometimes to bring them on board within the bank ecosystem, within the merchant ecosystem. So what we've done is we've created a digital assistant that uses rag and points to all these technical databases, the Q&As. We've had over time where we've tested and fine tuned these models to enable customers to onboard our product easier by automating a lot of manual tasks and by creating an environment where they can ask questions and we can get answers back to them much more rapidly.

We do put a human in the loop of this, so the chatbot is actually directed at our agents, but it still really reduces the time that it takes a customer to bring on a MasterCard product which helps them drive value from those much more rapidly. Sitting in my seat, I've had the privilege of speaking to a lot of folks at large enterprises who are trying to make sense of this gen AI wave and figure out what solutions do we want to buy, what solutions do we want to build, where do we want to partner, et cetera.

And I think there is this notion that, you know, one of the most hazardous things a super early stage company can do is go to a prospect and say, oh well, we built this amazing AI thing, but in order for us to prove how valuable it is, you have to share a bunch of your enterprise's data with us. And it's like, well, hold on just a second, you know, we have to safeguard that asset. Whereas again, you all occupy such a privileged position of being the trusted partner to millions of merchants and acquirers and banks.

And I think it probably makes for a much easier ask to be able to leverage this data in ways that are mutually beneficial, understanding that as you always have, you will continue to safeguard it. So I think that, yeah, and you need to make sure there are partners that have the same values, the same approach, the same commitment to working through these things as as you do otherwise, you get in a difficult situation. But we put this, the safeguarding of data at the absolute pinnacle of what we need to do here because we, we recognize the downside and we recognize the importance of maintaining the position we have as a, as, as a trusted brand and trusted ecosystem.

I mean it's, it's what enables our payment network to thrive the way it does. It's when you're in a place you've never been shopping at, a place that you've never shopped, that you know if you tap your card or dip your card or swipe your card, that the merchant knows they're going to get paid. You know you're going to get the product. You know, if there's a problem, you have recourse like the ecosystem works and it's because the network enables trust.

And that goes, that goes the same with how we use data and how we treat data and how we treat the products and services that sit on top of that. Yep. Well, I do have to ask, given I'm sure there are many founders and operators who are listening to this, many of whom don't have the benefit of decades in business as a trusted and known entity, but are very eager to work with the mastercards of the world.

I'm curious as you think about which solutions you're interested in in sort of buying or partnering or plugging in a vendor for and you're approached by, you know, an earlier growth stage technology company. What are some of the criteria you and the team are using to help you decide, you know, where you're going to prioritize your time and resources and the types of players that you're excited to do business with.

Yeah, so a couple things there where we're prioritizing, where we're spending our time still comes back to where are the areas that we think that we're focused on AI, the safer, stronger, personal, smarter. We then look within those about where if it's a new gen AI solution, where there are manual tasks or unstructured data, where gen AI is actually the right tool for the problem, back to the, you know, when you're using gen versus traditional AI solutions and it comes down to sort of the feasibility, the viability of, of the different opportunities that we're seeing in those spaces, we're looking to prioritize what we're doing.

MasterCard has a long history of partnering with early stage companies. We do it in a variety of ways. We, we, we've embraced the fintech community and we have relationships with so many early stage fintechs in our ecosystem we have a program called Start Path that sort of enables early stage companies to plug into the MasterCard network, get the value of what we bring as well as some support.

Alongside that we've had connections from my old job in corporate development about where we've invested and partnered with a lot of early stage companies. So we embrace the early stage community and we're looking to partner with them whenever we can. It just has to we're strategy first. It has to fit within where we're trying to deploy solutions based on what needs we're solving for our customer and then sort of the prioritization areas that we have within that.

But then if there's a company that has sort of we think the best approach to that and we think that they fit within our approach to governance and security, then we're always happy to partner if we think that that's the most efficient way forward. Yeah. And in that process, I'm curious. So you now occupy this really interesting sort of top down horizontal leader charged with making sense of this AI wave and making sure that the organization at large is taking advantage of it.

But I also imagine that there are P and L owners, GMs folks in a more vertical setting who are making similar decisions for their specific business unit and they can help answer those questions of how know specific gen AI capabilities are going to make them smarter, stronger and sort of all of those criteria like for founders who are looking to navigate the organization, both MasterCard but also generally speaking where there is like a senior horizontal decision maker charged with innovation or AI.

And then there are vertical business leaders like how do you think about, could you articulate sort of how decisions get made in that capacity? And I think you've described it to me as Hubbard spoke in the past, like maybe just walk folks through how that, how that works at a big company like MasterCard. Sure. So what we have done in this structure that our CEO put in place earlier this year was create a new group for AI and data.

Now that's not centralizing every activity in a company the size of ours. Again, artificial intelligence, like electricity, it's just a part of so much of the organization. It would be foolish, dangerous and probably impossible to try to strip out all those elements and centralize them into one place at the same time. You can have people trying to solve similar problems in different places in an uncoordinated way, which is problematic.

So the way the hub and spoke model works is I from A and my team from an enterprise wide standpoint are looking at where we think the technology is the greatest opportunity. So to drive value for our customers and to drive value ultimately for our shareholders, where do we think that there are opportunities? And I bring that to the business partners in hr, in finance, in fraud, securities, in open banking, in our core payments business to talk about these opportunities.

At the same time, the leaders of all those businesses are always trying to innovate, always trying to find new solutions. They're coming up with ideas and they're bringing them to our team. And you have that two way collaboration and discussion. And what we now enable is one, if they're trying to go about something, we have a lot of learnings across the organization about what's working, what doesn't. A lot of this requires the right access to the data, the right tools.

We want to reuse things that we've and patterns that we've seen work and avoid those that we haven't. And so there's a lot of that learning that my team can bring to that part of the organization to make sure we're innovating in the best way and the most efficient way. And at the same time, we're not trying to own the ideation and the product development and the business development that those groups are doing. That's their job. That's what they're doing by running their businesses and they're coming up those ideas and bringing them to us.

So it's that two way street and communication where we get the best ideas and then also make sure that we're not being duplicative or if we found an approach or a vendor or model that's efficient, that we build on top of that, because you know, that's going to be a faster time to market, it's going to be a more effective solution. We know it's going to work within our, our governance and our privacy rules and rubric. Yep.

And then what about for like ongoing, okay, you've implemented a vendor or a model or you know, you've leaned into a solution in some capacity and it's been in market for a year and you're trying to assess, you know, did they deliver upon the ROI or did we collectively deliver upon the ROI that we thought we were going to deliver. Are you thinking about that process separate and distinct from what I'm sure is a very robust vendor management motion that already exists at the firm? Or at that point, is it okay, the current machinery we have in place already does a good job of figuring out which vendors are working, which vendors are duplicative like does it get kind of put into the machine or is it, is it still kind of carved out as like this is new and exploratory in a island.

And we need to figure out a different way to measure roi. Yeah, we are like my team owns that. We have sort of spun up an approach where every new initiative has a key set of KPIs. We establish targets for those and we have a plan to measure those over time. I think particularly at this stage of these new gen AI use cases to find out what's working, how we're driving value for our customers, how we're driving value for our employees, how we're driving value for the ecosystem.

It's important to look at those specifically. So if I look at what we're doing for our software engineers and developers around coding assistance. Yeah, I want to understand what the efficiency is for them. I want to understand we have different interventions for training and pointing in at different elements of the life cycle if that efficiency gets higher. But just as importantly or more importantly, what's the feedback?

What is the customer satisfaction with these? Like, if we're taking away parts of the job that people they don't find as exciting and they can redeploy that time to answering harder questions and doing more thinking and to improve the throughput of the ecosystem and that increases satisfaction, that's really what we're after. And so you have different sort of measurement and KPIs for different initiatives. But right now I am looking at everything that we're rolling out there, trying to establish, like what are we trying to measure, measuring that over time and making sure those learnings come back so we figure out where we're going to deploy effectively, you know, our capital and, and scarce resources to have the biggest bang for the buck.

While we do that, for everything we do, I am taking a slightly bigger focus and putting a shinier light or brighter light on some of the gen AI solutions that we're putting out to market. Yep. One other question I had was your role is really interesting in the sense that I think MasterCard's almost 33,000 people. It's a very large company. And in order to figure out where AI could have the highest potential impact, you have to have a very deep understanding of the operations of the organization, which for a company that large, you could spend all of your time kind of mapping out and figuring out simultaneously to make sure you're picking things that are best in class and the breakneck speed at which this software is developing.

You kind of also have to have an ear to the ground of what's happening outside the four walls of the organization and kind of stay up to date with all of the rapid progress that's being made. Like, are there any either sources or processes or rituals? You have to make sure that you're kind of staying up to date on all the latest. Yeah, I say it's a combination of a few things. One, I guess some of the good news is my last job in the strategy role gave me pretty good training of trying to keep an eye over a very broad ecosystem and what's happening in that.

But the amount of time I spend listening to podcasts, reading external news, and also just networking and talking to others, both about how they're deploying AI, how they're organizing their organization like this hub and spoke model, there are a lot of ways to do it. What do you federate? What do you centralize? How do you coordinate most effectively? How do you. How do you balance innovation and speed with the right governance? Like, there's a lot that you can learn from others. So I spent a lot, like just today, if I think about it.

I've. I've listened to a handful of podcasts, I've read a bunch of articles. I've done two networking calls. We're doing this podcast. It's a lot more time. External. We had a. An event last night with a lot of external parties at a dinner, which was just sort of a sharing ecosystem and talking about what we're doing and try to learn from others. When we presented to our board of directors on AI in September, the way we structured is I had seven external speakers come in. Right. To give different perspectives from an investor perspective.

The CEO of one of the leading LLMs. We partnered with databricks quite a bit. We had someone from their C suite come in, we had academics come in providing these perspectives. So our senior leadership, myself, my team are hearing a variety of different perspectives because it's changing rapidly and unless you keep an ear to the ground, like, you're not going to stay up on the. On the latest and greatest. Yep.

And while you and I sit here and I think can clearly articulate why we're so excited about this technology and where we see potential for ROI. I'm curious. You know, MasterCard again sits in this really interesting position at the nexus of issuing banks and acquiring banks and processors and, you know, merchants, all sorts of distinct constituents in the same sort of payments and commerce ecosystem as you've been out in the market thinking about the future of this technology Are you finding that everyone is ubiquitously excited and eager to adopt it, or are there any constituents in that map that I just drew out who are really hesitant or, you know, are worried?

Like, would you say it's been unbridled excitement or are you seeing kind of both sides of the coin? I don't think it's unbridled excitement. I think there's a lot of concern out there, particularly in a regulated industry, like some of the places in which we touch, in which we work. There's still a lot of, there's enthusiasm, but it's definitely not unbridled as people are very worried about the accuracy, the hallucination, the efficacy of these. And particularly as you start pointing these to customer facing solutions, there's a very high bar on what that's going to, what that has to achieve.

And I don't think a lot of these solutions are at that point, even with fine tuning, even with pointing at the right data. And so a lot of what we're seeing is a human in the loop or putting things behind a human to enable your own employees to get the insights and information, but sort of holding off on the direct application to the consumer. Now, not every organization is the same, but, and some people are leaning into this more, but I think there's a lot of people that want to see this, want to see this proven out and want to see the continued improvements in the underlying models, the improvements in the accuracy.

And we are early days, we're seeing improvements every time there's a new iteration in accuracy, in speed, in latency, in cost performance. And these things evolve over time. And so I think everybody's a little bit patient on this as well, to make sure that we take advantage of this while also managing the do no harm element of some of it as well. Particularly in financial services. Yep.

Maybe just to close out, you know, the world is your oyster in some senses in this seat. Like if I had to put you on the spot and you had to pick one or two things that you're just brimming with excitement over, like, whoa, what are you thinking about most for, for maybe the next year or two, like what has you most excited? So I think the things I have, I have my eye on for the next year are. One is how we interact with models and AI is changing, I think in a couple ways that we're seeing more and more. One around sort of multimodality and being able to look at not just text, but the integration of text and images and voice and video and for a Lot of instances that we think about, like you think of what the insurance companies are now able to do where they can piece together photos of an accident with the data from the policy, from the information from their manuals and what they have internally to come up with a much faster and more effective estimate.

When we think about things in financial service, think invoice, reconciliation, bill pay. You have these things come in as PDFs, you'll have them come in as images, you'll have them come in as text in a variety of ways as we can start looking at things holistically in different modalities and still come up with a single source of truth. That's very exciting. And then I think the reasoning models and the evolution we're seeing on those is very exciting as well. It opens up new use cases.

And for me, what's exciting is some of the experimentation I've seen on those where it's not just the accuracy that is improving, but the percentage of time, the significant increase in percentage of time that those models will basically say, I don't know the answer to a question. Which, as we go back to risk and we go back to how models are improving and what we need to see. Knowing the limits, just take AI out of it.

If you're a human being, knowing the limits of your knowledge is, is incredibly important. I remember when I took my last job and I was presenting to the board every couple months, the best advice I ever got was the best thing to say when you don't know the answer is I don't know the answer. And the worst thing to. The worst thing is to try to.

Is to try to fake your way through it. And that's true in life. But it's certainly the case with AI and AI, if it doesn't know the answer, it still sounds very lucid and erudite. It's wrong answer. So it's, it's not just wrong, but it's confidently wrong. I go back to where we started this conversation, Mark, around APT and the ability to misuse analytics and data.

And the challenge and the danger that has is real. And so as these reasoning models are understanding their limits better and knowing, hey, I don't know the answer to that question much more effectively than some of the foundational models. I think that's an exciting evolution in this space. So that first one is around how we're dealing with AI is changing.

The second one is, I do think that this move to trust, this move to responsibility, how that's incorporated, how we bring transparency in, still has its day, and we're leaning in hard on that dimension. I think that's going to be a big trend, not just in 25, but for the next few years. But I continue to think that how we do this in a trusted way is really critical. And then I think the third thing is, and who knows where the scaling laws will go and how much better the models will get as we add more data, as we add more compute, as we add more time to train them.

But I do think that the use of data becomes a more critical differentiator, inference becomes a more critical differentiator. And I think that's the other thing that we're looking at and that matters quite a bit for us, given the data that we have and the data we have inside our organization that we can use and leverage to help bring better insights to our employees and help make better solutions for our customers. Yep. Well, I got to say, talking to you reminds me that we're both very fortunate to sit at the front row of all this stuff that's unfolding. I'm leaving this conversation fired up about the many opportunities that I had for MasterCard with both generative AI just but also generally and so really appreciate you spending time with us today.

Thanks for coming on. Thank you so much, Mark. I really appreciate the time.

ARTIFICIAL INTELLIGENCE, TECHNOLOGY, FINANCE, MASTERCARD, FRAUD DETECTION, DATA ANALYTICS, A16Z