AI-Powered Intelligence: The New Advantage for CFOs

Narrator: 0:08

This is PYMNTS ON AIR, a PYMNTS podcast. In-depth conversations, expert panels, and exclusive research brought to life by PYMNTS Intelligence. In this episode, Mastercard's Raj Sheshadri shares how AI is letting CFOs anticipate market shifts, optimize liquidity, and outpace competitors.

John Gaffney: 0:32

Hi everyone, welcome to this keynote conversation for our B2B payments virtual event. My name is John Gaffney. On the docket today, AI is changing how finance leaders see their businesses, make decisions, and move money. It's shifting the CFO agenda from efficiency to foresight. In this conversation with Mastercard's Chief Commercial Payments Officer, Raj Seshadri, we'll explore how AI and data are giving CFOs new ways to manage cash flow, working capital, and risk, while at the same time mobilizing partners across the B2B ecosystem to accelerate growth.

Raj Seshadri: 1:06

Thank you, John. Thank you for inviting me. It's great to be here.

John Gaffney: 1:10

It's good to see you. Let's start kind of on a general level. Let's talk about data and AI at Mastercard in that context. How do you feel like you guys have led the industry in tech and payments? And how have you engaged your partners in this area across the ecosystem?

Raj Seshadri: 1:28

You know, John, that's a great question. And I think at the core of it is innovation and staying dynamic. And when I think about innovation, there are essentially three key ingredients. There are many ingredients, but there are three ingredients in my mind. One is people in collaboration. The second is uh the data, as you mentioned, you know, and the third is AI and technologies like AI, right? So let me start with the third first. We've been using, you know, AI in all its forms, from the simplest of algorithms to machine learning to gen AI, now a gen tech AI, for a couple of decades in Mastercard. So you know, staying uh at the leading edge of the technological evolutions and you know, incorporating them, experimenting with them is what sort of gets you there. So AI is very key for us. Now you can't apply AI until you have really good quality data at scale. And so that's the second thing that I would say we've really focused on, which is we have a lot of proprietary data and we have it at scale globally. And what we've done again for the last couple of decades is you know, cleansed it, warehoused it, made it machine readable, make made sure the quality is there, the quantity and the quality in particular matter a lot. So spent a lot of time on that. And then the third element that feeds into the innovation is the people, right? And the collaboration. And here, what I would say is that uh, you know, our teams, we spend a lot of time thinking about how to, you know, um give get them new skills, allow them to experiment, try new things. We emphasize co-creation with our partners and customers because we like doing it with the market and with the ecosystem. But as we do a lot of this, we're also focused on trust. At the end of the day, trust is paramount. And so things like, you know, especially with deploying AI into our products, services, processes, et cetera, one has to make sure that we're doing it responsibly responsibly. So we spend a lot of time thinking about governance, tools, guardrails, and you know, making sure that whatever we put out there is fit for purpose, does what it's intended to do, and doesn't have any unintended consequences. And then as it is in the market and as it evolves and as it's adopted, that that continues to be true. So all of these contribute to you know the innovation that drives a lot of uh a lot of what we do.

John Gaffney: 3:46

Very well said. So let's try to bring this back into the B2B payment space, Raj, if we can. So how do you feel like AI has proven to be a differentiator for Mastercard, particularly in B2B?

Raj Seshadri: 3:56

It's a huge differentiator because if you think about AI, it gets you know continuous learning and evolution of the decision making based on the latest data that's real time, coupled with the ability to take immediate action as soon as you see something, right? Or or tee it up for action in other cases. And so that power of AI is pretty transformative across our business, has been and will continue to be so. So it's you know, it's true internally. We think about efficiency and effectiveness in what a lot of other folks are also doing, coding, you know, generating the first version of a document or an image, onboarding customers, sales, service. Actually, our CFO function, and we'll talk about um finance in a minute in more detail. Our CFO function has been a huge adopter of AI. You know, for example, FX forecasting, things like that, right? They're very useful for it. Now, in B2B payments overall, I think the many roles that AI plays, and I'll give you a few examples. Maybe it's easier to illustrate it with a few examples.

John Gaffney: 4:55

Sure.

Raj Seshadri: 4:56

So let's take uh fraud as an area, right? Fraud prevention is a huge imperative. And so here we've done things like good examples might be we put a product called Safety Intel into our network. It's machine learning in our network, it's been there for a long time. It detects, you know, patterns across the network. And over the last three years, for example, it has stopped about $50 billion in fraud losses for our partners. Um, another example would be decision intelligence, it's a score that travels on the transaction. And what it allows our partners to do is to reduce fraud as well as reduce false declines. So that increases the user experience because the last thing you want as an employee of a corporation when you go use a card is to have your have a false decline. Other examples might be in personalization, where dynamic yield is a capability we have, where it allows a personalization down to a customer of one, uh, an individual, uh, in terms of messaging, offers, notifications. And we work with hundreds of brands around the globe. And uh and in cross-border payments, um, actually, some of this comes together in cross-border payments because we have things like fraud management that are very critical. We also have you know adherence to compliance and automating that, uh, so that, you know, for example, expense management, so that as you make the expense, it's automatically in compliance and it's reimbursed right away, right? And can all be done automated in in real time. And then the things like um agentic AI, which you know, we talk about across both consumer and uh B2B transactions, where as agents develop to uh facilitate e-commerce, commerce, and uh shopping externally with consumers, with employees, etc., uh we're able to uh provide uh payments into it. So agent pay, the ability to facilitate payments and provide uh that capability to agents when they need it, the ability to distinguish an agent from just a bot, the ability where we have information that is valuable, delivering it in agentic forms. So there's a lot of work across the board, but AI is fundamental to all of it.

John Gaffney: 7:10

Yeah. Um, so let's talk about the office of the CFO, which you alluded to. Um, you're commercial chief commercial payments officer. So CFOs are a key stakeholder for you, um, and you provide them better ways to pay and get paid, basically. But how have you witnessed the role of the CFO evolve, say, over the last year? And do you think AI has played a part in that evolution?

Raj Seshadri: 7:34

No, absolutely. We work with a number of CFOs and their teams to facilitate um, you know, better ways to pay, get paid, and also move money. And you know, the trend here is that uh there's more embedding of payments in finance into software platforms, into workflows. There's more real-time data and more integration of data across different sources of data, internally and externally. And then there's also sort of a uh blending of traditional roles in the office of the CFO, the roles that used to be different, that now you know um need to collaborate with each other. And when you do it properly, you actually reduce friction, you reduce error rates, you create more uh intelligence and insights and better decision making in real time. And you've most importantly, I think you free up capacity so that the finance function and the CFO can focus on more strategic challenges and opportunities and tasks. So it's everything from you know moving away from traditional methods and modeling to um more dynamic investment models, predictive analyses, um, you know, not just reacting to market changes but anticipating them, being able to deploy capital dynamically to optimize working capital and reduce you know liquidity needs. Um there's just a continuous evolution and a continuous uh dynamic disruption of uh things in finance that require CFOs to harness data and AI in order to make finance more efficient, more effective, and substantially more strategic.

John Gaffney: 9:13

Right. So, what what are some of the pain points in your uh from your observations that CFOs are feeling that AI can specifically address?

Raj Seshadri: 9:23

So the number of them. And I think if you're in the if you're um in the shoes of a CFO and looking at a finance function, the question is more about prioritizing where to start because there are lots and lots of opportunities. And so you really got to think through where you are, what you're doing, and what's the best way to start to maximize the return on investment, right? So it's everything from you know, let's let's start with the CFO function itself. You know, historically there have been two types of roles. There's sort of the uh what I'll call the internal to finance roles, things like um tax, treasury, accounting, where you have deep subject matter expertise. And then the other kind of role that you often find in a finance function is you know, business partners, uh finance professionals who work with the business in order to help the business measure, get better, drive strategy, et cetera. And what you're seeing now is a um a little bit of, you know, these roles are not as separate as they used to be. There's more blending of the roles because there's the ability to connect dots across all these different functions in order to come up with better insights and analytics and decision making. So that's that's a place to start. But it then goes into things like you know, forecasting, where historically there were models that were built, they're backward looking, tested, and then deployed. Whereas today, what we're increasingly seeing is um models that are created dynamically, that uh evolve and are optimized continuously. And you're not relying on a deployment of a tested model on a monthly, quarterly, or annual basis. You're just constantly in a cycle of improving it. And um, and it's real time with real-time information flows across multiple sources of information. And so then the decision making becomes more becomes smarter and more dynamic. You know, there are uh lots of other examples, payments decision, another one where you'll see in the treasury function, you know, uh there's much more intelligence and data and AI embedded in order to tee up decisions to a treasurer in terms of you know what uh payment method method to use, what's most optimal uh, you know, to uh make maximize use of the terms with a given partner because they're always these are contractual relationships and the terms embedded in those relationships in the contracts. So, how do you maximize that? What payment method is best? What money transfer method is best? How do you release working capital um and reduce liquidity? So you see a lot of that in, for example, the treasury function. So across the finance function, there's a tremendous amount of opportunity. And the biggest challenge for a CF or a finance function is prioritizing where to start. I think starting is an imperative. It's you know, our finance function has been adopting a lot of this for a long time. Um, and it is an imperative to start and get going, but a decision and a prioritization of where to start, depending on you know on your business and where you are.

John Gaffney: 12:19

Interesting. So, Raj, you talked about the responsible use of AI, and I know that's very important at Mastercard. What role does culture and talent play when it comes to adopting AI responsibly?

Raj Seshadri: 12:31

Uh it plays a huge role. You know, what we always say is AI is not just about technology. It's also about culture, it's about ways of working, you know, it's everything from um, you know, thinking about innovation, uh, thinking about um, you know, how to um upgrade and upskill your sales your teams, ensure that they have continuous learning, access to education, access to information uh that will make them better, you know, thinking about how to make them more collaborative and collaborate with each other. We always say ideas come from everywhere. Sometimes they come from the edges of the organization. Uh, you also need product and engineering and customer-facing folks to collaborate. And so, how do you facilitate a culture where collaboration is key, where working together is important? And then governance and tools, that's where the trust comes in. How do you allow your teams to experiment with the latest technology, to build with the latest technology, but with a level of governance and tools and guardrails to make sure they're using it in the right way and the outcomes of the use are uh appropriate and uh go to increase trust and not uh reduce it. And that's where the responsible AI comes in. That's where some of our principled-based approaches to things like data privacy and AI come in. So, you know, for example, we say data belongs to you, you you own it, you control it, you should decide how to use it. And so we are very principle-based as we develop the guardrails and the governance and the tools.

John Gaffney: 14:09

So it's interesting. I'm hearing a lot about trust in this interview, Raj.

Raj Seshadri: 14:13

Well, you know, at the end of the day, it's about innovation and it's about trust. And it's not one or the other. You have to be very innovative, but you also have to do it in a way that advances trust and increases trust. The two go hand in hand.

John Gaffney: 14:26

Very well, very well said. So, last question for you, Raj. What excites you the most about the potential impact of AI in the payments industry as a whole?

Raj Seshadri: 14:35

Oh my goodness, it's so exciting. It's a great time to be in this industry. Um, I it's uh what I'd say is there's a seismic shift happening from you know, from um legacy ways of doing things, from traditional approaches to things uh that are digital and data-driven and real-time. And uh, and in the process, there's a lot of value to be created in liquidity, in working capital, in end-to-end expenses, in uh compliance, in risk management. And so that's really exciting. And uh, you know, one conceptual way to think about it is as a consumer, we're all consumers. We're employees, but we're also all consumers, right? And in our consumer lives, we're used to such simple payments experiences that are digital, that are embedded, that are secure, and we're so used to using them. And the most exciting part is bringing those consumer-like experiences that are secure, fast, intelligent, uh into the B2B world to make sure in our working lives, payments are just as secure, just as easy to use, just as smart. And that sort of makes our businesses better. So that's what's exciting.

John Gaffney: 15:43

Okay, that'll do it for this exclusive keynote for our B2B.AI event. My guest has been Mastercard's Chief Commercial Payments Officer, Raj Seshadri. Raj, thanks for joining us.

Raj Seshadri: 15:54

Thank you, John.

Narrator: 15:58

That's it for this episode of the PYMNTS Podcast, the thinking behind the doing. Conversations with the leaders transforming payments, commerce, and the digital economy. Be sure to follow us on Spotify and Apple Podcasts. You can also catch every episode at pymnts.com/podcasts. Thanks for listening.