Moody's Corporation ($MCO)

We're happy to have all you here. I'm Curt Nagle. I'm the new senior analyst for the sector. Really excited to be covering the sector. Really excited to be working with you all. We have a really terrific lineup today, full gamut of the -- basically the entire sector, covering what we think will be the most pertinent themes and then, of course, headline with all things AI. Opening the conference today, really pleased to be welcoming Rob Fauber. He's the President and CEO of Moody's. 20-year veteran of Moody's, started at BofA his career.

Yes, that's right.

And under his tenure as CEO for the past 5 years, led the company to record profitability both in ratings and in analytics. And is embedding -- I think turned it off a second. No, okay. Sorry. Embedding AI across every facet of the business. That's a huge focus for him, driving internal efficiencies, new product development, solutions, and revenue streams based on the proprietary and as you put it, Rob, their decision-grade data. So again, with that, welcome, Rob, and why don't we jump into the questions?

Yes, thanks for having me, and it's great to have you covering Moody's.

Yes. Great to be here. So yes, in terms of the first question we're starting with, at least for the info service companies, is unsurprisingly on AI and moats, right? We'll get into some of the -- what we are -- or could be the biggest opportunities, how you're harnessing the technology. But yes, sticking with moats, I think it's generally understood that anything around credit is pretty walled off, right, defensible. But if we think about some of your other data assets, the stuff that comes through licensing, commercial agreements, IP agreements, stuff that isn't outright owned by Moody's. What are the moats around there? And I guess in terms of -- maybe moats that people don't recognize or don't appreciate as much, whether it's regulation or switching costs. How would you address that?

Yes. So I knew the first question would be AI. I'm sure we'll have a lot of discussion throughout the day on AI. And you're right, the Ratings business is a benchmark business, and we'll probably touch on that. I hope at some point, about how we're thinking about the Ratings business and an AI future. But in general, there's more demand for understanding credit and risk than I can remember in a long, long time. As you think about basically, our content state, I'm going to move now over to analytics. I would say a few things. One, and I'm going to zoom in, you talked about our company database, so I'll zoom in on that in a second. But in general, we have deeply contextualized content, right? So there's a whole context layer on top of our data that provides -- it's a structured representation of the data with governance and auditability and all those things that are super important to banks like Bank of America, who come in and send in their internal audit teams to audit our processes. So that's one. Two, a lot of what we do is underpinned by very proprietary data sets. Orbis is a little different. I'll talk about that in a second. But if you think on the credit side, we, for 3 decades, have curated the world's largest proprietary give-get default database. And then that's what we use to calibrate our credit models. So it makes them unique. Similarly, with -- you go over to insurance, like our catastrophe models, those are calibrated, but we get access to the entire industry's claims data. And so we use that to calibrate the models. I've been asked before, can't AI build the models? AI can build a model, but it can't be calibrated on actual loss data. So that's the second thing, I would say. And third, you have to think about why institutions are using us. You use the word decision grade. We use that all the time. Banks and financial institutions want to credentialize what they're doing. So our credit models, our stress testing, our cat models, our customers' use of those models and those data sets is being reviewed by their regulators, right? They're going in and looking at credit files and reviewing their processes. So there's a very strong indirect regulatory support for all the things we're doing. So imagine that everybody in the industry is using our stress testing and economic scenarios and forecasts except you and then there's an error in yours, and you can't figure out why there's an error. That's a very, very bad day in the office. The last thing I'd say is I'm just going to go to the Orbis data. So this is the world's largest database on companies. And think of it as really 3 layers of content within that. First is what I'd call basic firmographic information. Can that be collected, some of that be collected by AI and scraped off the Internet? Yes, and it happens today, and we compete against those companies and have been. Then we have information that we get from these bureaus that is private. And we get that because we have paid access and IP rights that we negotiate and then the IP rights are about how can we create derived data and distribute that data. And then we transform some of that data and create proprietary ownership structures and trees. That is transformed proprietary data, it's where most of the value is in the Orbis database. I'll pause there.

Okay. Maybe a good segue in terms of thinking about adoption, right, for some of these AI-enabled products, agentic workflows, stuff like that, AI-enabled databases. I think on the last call, you talked about your large customers, right, consuming at much higher rates, I think twice the rate for the rest of the business or your other customers, in part due to, as you talked about, more sophisticated AI tools. Where specifically are you seeing the adoption curves really start to ramp? And then I guess thinking about growth rates for the rest of your customers, the smaller ones maybe less sophisticated. How do we think about that ramp?

Yes. So I'm going to focus on banks. It's our largest customer segment. And there is a tale of 2 customers. So you're right, at the big end of town, the Bank of Americas, they're all about building out their own AI workflow platforms, maybe across the corporate and investment bank or the commercial bank. And it's not about consuming software from us. It's about consuming I'm going to use the phrase our connected intelligence, right? This is our contextualized data, our models, our insights, our ratings. And they want to consume that in their platforms, right? And increasingly that they're building those platforms, and that means it's either -- historically, we've -- people have consumed our content from our workflow software, maybe through our website, and a data feed, a good old-fashioned data feed. And today, it's about, hey, now can I take it through a smart API or an MCP server into my AI workflow platform. And every big financial institution I talk to is talking to me and I'm sure others in the industry about how can I access actually more of your content because I'm building this layer across the corporate bank. And I know that I've got the data siloed in all these different parts of the bank, and I want to have my own internal bank data and my very important third-party information providers, feeding into my AI system. That's a conversation we're having at almost every single bank. That's a great conversation for us to have. That's why we're seeing the highest growth actually from the strategic -- the largest banks, and that's very encouraging to us. If they wanted -- if they thought it was commoditized, we'd be having a very different conversation. But then when you go down to Tier 2 and Tier 3 banks, the regional banks, the credit unions, it's a very different story. We serve a lot of them. There, one of our fastest-growing products, and we talked about in the earnings call is lending workflow software. And what those banks are saying to us is, "Hey, look, I don't have an army of AI experts. I want you, Moody's, to bring to me AI enablement on the workflow platform that I'm going to implement and tell me what other banks are doing". And so we've got an AI layer that sits on top of the workflow that does things like spreading financials into your chart of accounts, creating automated credit memos, covenant monitoring, all those kinds of things. Both of those are driving very nice growth for us.

Understood. I guess another good segue. I think maybe a little long term here. And considering all the things you said, how does this -- how do we think about the evolution, I guess, of pricing models, right, over the next few years particularly, again, not to belabor the point, but companies like yourselves that have mission-critical proprietary data. Do you foresee, I guess, a shift to more consumption, more value-based, which you've talked about a little bit right away from these strict enterprise models. And I guess how does that impact I guess the arc of revenue growth going forward, let's just say, over the next 1 to 3 years?

Yes. I think that's the right time line, by the way, the right way to think about it. We are piloting consumption pricing in one small part of our solution set. This is with the smaller customers where it's more of like a product-led growth kind of model. I would say a couple of things. So first of all, a lot of the conversations we're having now are about, hey, how can I think about changing the labor leverage model within my institution, whether it's KYC and anti-financial crime or whether it's my credit process, right? So that's a great discussion for us to be having. It is a different discussion. And then it's all about business case and ROI and all those kinds of things. And you start to say, well, the data is going to be the data and models and contextualized intelligence is going to be very valuable, right? And there's going to be a huge return on that. How do we think about the pricing? I think you're going to see us move over that time horizon to implement elements of consumption into the model. And I say that because a few things -- you have to think there's a few things that have to happen. One, we have to have the revenue operations and the technological capability to meter all of the usage and to be able to charge for that usage. So I mentioned these good old-fashioned data feeds. We don't know all of the usage in a traditional data feed environment. We have -- there are IP restrictions and usage restrictions. We audit our -- we don't know right? So there's a case where it's not as easy as just saying, "Hey, I'm going to charge you based on consumption". So there's a RevOps piece. And then there's a customer piece. A lot of times, people lose sight of this because you think aren't you just going to get uncapped upside for usage of the data. That sounds great, but the customers -- think about what the customers are dealing with uncapped cloud cost and want -- many of the customers want budgeting certainty. And so I think you're going to see hybrid models, right, where we have access to the content, some set amount of consumption and then perhaps thresholds and kickers for additional usage. And I think back to the conversation we're having about hey, take more of our data, more of our content into your AI workflow platforms. That means there's going to be more usage across more of the institution. We'll see you in a couple of years, and we'll have a different discussion at renewal when you have a chance to really understand how you're using and getting value out of our contextualized intelligence.

Yes. And I guess maybe as a byproduct before maybe pricing consideration stickiness, I would imagine would be a good consequence of that, I would think.

Right. The more embedded we are, the stickier we are, and we're deploying our customer success teams to say, "Hey, great news, you've got access to the content here at the institution. Now we're going to deploy our customer success teams to make sure you get as much value as you possibly can out of it".

Understood. Okay. Switching gears maybe a little bit. So again, we've talked about AI customer relationships, revenue opportunities, value of the data. How should we think about AI within Moody's internally in terms of asset and labor efficiencies, again, kind of, let's call it, the 1- to 3-year time horizon. Just looking at your K, right, headcount does seem to be going down, at least if I look at AI, I don't know if that's a consequence of just good old-fashioned expense management or whatever but -- yes, just basic question of kind of how to think about internal utilization and asset efficiency.

Yes. There -- you talk about 1 to 3 years, and this is coming out as fast. So I'm a real bull on the efficiency opportunity across our organization. So we, like many other companies went after customer -- customer service and some other things. That was low-hanging fruit early on. Now we're going after product development life cycle. And what that is, is if you think about a company like us, we have product people and we have engineers. Product people engage with our customers, create specifications for products, work with the engineering teams who then have historically written code. We know all that is changing. So we have literally -- we have redesigned our PDLC to be AI first. We have AI coding tools that we are deploying into that PDLC. We have changed job descriptions, and we're moving towards a world where I think instead of product people and engineers, we're going to have what you think of as builders, right? I'm a builder. I was telling you, I built a website at 3:30 in the morning the other day, I mean -- so that is a real opportunity, both in terms of efficiency. We already see the data on how much more efficient it is making, especially our best product and engineering people but also product velocity. And even the selling motion is going to change, right? Because we can do rapid prototyping now and we can create specialized agents in -- literally in days, right, and prototype that with a customer as a way just to access more of our intelligence. It's just another vehicle to consume our content. So I can't change the -- I'm not going to change the medium-term targets, but I can tell you we're going after this aggressively, and it starts with me, and I provide coding and everybody at the company knows that. And -- so I think there's a real opportunity here.

Okay. And maybe just to put, I guess, a final point on that. Can't change the long target, right, or the medium-term target, high 30s, but I guess in terms of -- not to sort of again a belabor a point, but in terms of the arc of potentially reading -- getting out there, maybe even at some point, thinking about maybe you get to 40, I don't know, but yes, the general margin implications.

Yes. So I guess there, I would say I'm bullish on the opportunity. As you can see, I'm not ready to build that into changing the medium-term targets, but I see this as a real opportunity for us going forward.

Okay. Talked a lot about analytics business, AI and ratings, right? Your highest margin business by a pretty good margin. I think your guidance for this year is above your targets and that is obviously on things like the volume leverage and you're lapping some investments. But yes, how does -- just very broadly, how does AI change your ratings business?

I would say 2 ways. And I know a lot of people focus on, hey, with these AI tools, can't you just be much more efficient, right? And the answer to that is, yes, of course. We will be able to. The interesting thing I think about what's going with AI, it forces you to ask questions about your source of sustainable competitive advantage. And of course, spreading financial statements and making adjustments and stuff is not where the value is in the rating agency, so that means those things are going to get automated, and they are being automated and leveraging AI as fast as we possibly can. That's all happening. And you've seen already the operating leverage that's continued to come into the business even last year, right, as we have issuance growth because we've been working hard on what I'd say is traditional workflow automation. And in the second half of last year, we deployed AI capabilities that really accelerated our ability to automate and enable our analytical teams. The other thing is we're going to capture as much data from across the organization and the ecosystem as we possibly can, right, and feed that into our models to continue to provide us with unique insights that the rest of the market doesn't have.

Okay. Fair enough. Sticking on -- so well, I guess, put a bookend on AI. Sticking on the Ratings business. I guess a question -- and I know there are a lot of moving pieces here. Just generally, I guess, assessing the puts and takes, the risk opportunities for the guidance you gave, low single for issuance for the market, whether it's uncertainty on pulling forward refi walls, geopolitical risk, obviously, a huge question right now or just general data center CapEx, that's been a big driver. What specifically is in the model? And again, how should we think about the puts and takes?

Yes. Take this for who it's coming from. But it feels like just about every year around this time, there's something that happens. It's COVID. It's [ Ukraine ], it's liberation days, right? And here we are. And so the questions every year have been gosh, the market is feeling fragile. And what does that mean for your full year guidance and so on. Well, guess what, last year, in Liberation Day, we basically lost the month of April, right? The markets went to a risk-off mode and look where we came right on top of our guidance, our original guidance for the year. And what I would say here is what it's hard for us to build into our annual guidance is geopolitical risk and the inevitable then market volatility and kind of risk off mode that happens. So you end up losing a week or -- but what I would focus on is -- so while that is certainly the environment at the moment, heightened geopolitical risk, questions about oil prices, Fed easing, spread widening, all those things. From where I sit, I just think, gosh, all of those funding drivers that we have been talking about for years, which you have seen come through the business over the last 2 years, they're all still firmly intact. What kinds of things are they? So economic growth certainly has been one, but BlackRock put out a $68 trillion of infrastructure funding needs by 2040, that hasn't gone anywhere. You put AI and data center and not just data center, but all of the related energy production, transmission grids, renewables, transition finance, all of that, that's all still there. Heightened geopolitical risk has meant military buildups. Massive investment is going to go on in militarization and defense. And guess what, sovereign balance sheets are pretty stretched, right? So the governments are going to have to rely on the public and private funding markets to do a lot of this. And by the way, we also have a huge amount of debt has been issued over the last 5, 6 years. That's got to get refinanced. The 2028 refi walls in particular, are quite substantial. And then there's all of the private equity exits that have to happen, we know they have to happen and all of the money that's got to get deployed, that's got to drive M&A, still all there. So we're going to have some risk on and risk off weeks, heightened geopolitical risk, that stuff, those medium-term funding, it's all there.

Structurally, so no change. Okay. And yes, March is always tough...

It's early in the year. That's what we have to keep in mind.

Okay. Maybe just a segue here, private credit, right? [ Small ] has been a high-growth business for you. I guess how should we think about a, if you're willing to say just the revenue contribution for this year and then going up maybe a few? And then just kind of thinking about it like, you've got certainly some puts and takes, right, things you talked about, impact private credit. We've got concerns about outflows and credit quality, which would technically be a negative. On the other hand, right, that theoretically drive more demand for a deeper analysis of portfolios and specific companies. So how does that all balance out? How are you feeling about private credit?

So I feel much better just where our franchise is now than several years ago. It's interesting the discussion with investors and analysts several years ago was isn't private credit a big negative to the rating agency because it's the disintermediation of the public markets. And that was a huge negative. We geared up. We found ways to serve that market. There's still lots of that market that are unrated. We have seen very strong growth in parts of the rating agency serving parts of the private credit market. And now questions are, hey, is this now a headwind because there may be, as you said, heightened defaults and fund outflows. And well, that means the public markets are going to take up this lack. And I have said before that a lot of the direct lending is like a deferred mature -- it's like another maturity wall for us, right? And we've already seen this year some pretty robust refinancing out of the private credit deals into the public markets. Why? Because they're cheaper, right? And -- so I think we're going to -- to some extent, I'm relatively agnostic, right? I mean, I'd rather rate the direct credit now, and I'd rather be able to express an opinion on it for the market, but we're seeing some of that come back into the public markets. So that's one. And just in terms of what do we assume, we had very robust growth last year off of a smaller base relative to the overall size of the Ratings business. We've assumed that growth is a little bit slower this year, but still quite healthy. But if that slows down more than we expected, my guess would be we're seeing that come into the leverage loan part of our business. And the second thing, just to your point is, I've been saying this for a couple of years now, and I feel like I've been speaking into the wind about this market will benefit from rigorous third-party credit assessment. And that will provide confidence to the investing -- to the investors and allow this market to scale. And I would hear all the reasons that didn't need to happen. But I think now there's a much, much greater understanding of the benefit that third-party -- rigorous third-party credit assessment can provide this market in helping understand the credit profile of what people are investing in, so they can invest with confidence. We're seeing that demand then materialize in our analytics business because remember what we have in analytics. At the core is the world's best commercial credit franchise, right, the proprietary default databases, the gold standard credit models and guess what they're ideal for assessing private credit. And so we're addressing that market opportunity.

So I guess in the context of -- I mean, just judge it by the headlines we're seeing, in terms of rate -- I mean, is that rate of adoption accelerating in a fairly linear path of relationships?

I would say, it's very small. So the investor use of credit models, not surprisingly, has been small. The biggest customer base for all of our credit models are bank credit apartments. But now you have a new customer segment who's saying, first of all, we have to educate them. I didn't know that you had those capabilities, right? And now talk to me about what they are and can you actually -- do you actually have the ability to create a -- give me a probability of default mapped to a rating level with the confidence that you can put the Moody's name behind it. And the answer is -- if you give me the data, the answer is yes, and we've been doing it for several decades for banks. So it's small. But in part, what we did with MSCI was about saying to the market, we have this capability, and we and MSCI are working together to bring this capability to the investors in private credit.

Setting the table, I guess.

Yes.

Okay. Fair enough. Switching just quickly back to MA. So I guess, kind of breaking it down by subsegment, right? Your KYC business is doing really well. One, in terms of how I'm thinking about the rate of growth, roughly 20%, is that a sustainable rate? Insurance, you're calling out -- and we've talked about this a little bit more demand for sophisticated products. So how should we think about that, again, as a rate of growth this year? And then I guess, what is -- what needs to happen within your banking business to get that to reaccelerate? I know there were some purposeful I guess, pullbacks like transaction revenues. But how do we think about that segment?

Yes. So you kind of talked about the big 3, if you will, sitting inside Ratings, our Analytics business. And I mean you can see from our guidance, we're generally expecting the portfolio to produce roughly the same rate of growth. But let me break down kind of where that growth is coming from. So in banking, I talked about both what we're doing with the large banks who are accessing, Think of it as our contextualized intelligence and the Tier 2 banks who are actually buying the software. And we talked about the growth rates of that lending workflow software are very robust growth rates. The drag in terms of revenue. So when you look at ARR growth, the ARR growth in our lending suite is faster than MA overall. That's very encouraging. But when you look at revenues, we historically have had transactions implementation services. We've been deemphasizing that for years now. And so that's just a drag on reported revenues. That's low margin revenue anyways. We want to move away from that. We've moved to a partner model. With insurance, the drivers there -- we -- not only do we have the cloud-based platform adoption of our core catastrophe models, but we've now moved into -- we acquired a company a couple of years ago that provides AI geospatial intelligence to support insurance companies in underwriting, property underwriting and then that feeds into our catastrophe models, and we've expanded into casualty. And casualty is actually one of the biggest sources of insurance claims. You think about things like asbestos -- mass torts and litigations and asbestos and things like that. And that is a huge need for the insurance industry to understand how to get a more data-driven approach to assessing that kind of risk. And so we're building that out. That's going to support the growth in insurance. And then, of course, we talked about KYC, the demand for that continues at pace. And the only other thing I would say is that what we've done this year in terms of just how we go to market is we've tried to kind of cluster our product launches into the first quarter of this year so that we can have a really concerted go-to-market. Historically, we kind of spread them out throughout the year. And that included the second half -- so what we did is we kind of took the things from the second half of the year, held them, put them into product launches this year. What that means -- the only reason I mentioned this is just there's going to be a little different cadence of ARR growth. So I would expect in the first quarter, we probably have a little bit of a downdraft towards the lower end of our high single-digit ARR guidance. And then that will pick back up through the balance of the year because I think it's really about just the calendarization and the selling.

Okay. Understood. Maybe quickly touch on capital allocation. One, just in terms of -- I don't think it's a huge focus, but bolt-on M&A, what assets would look attractive? And then I guess, just thinking about buybacks considering current valuation and just -- yes, how are you thinking about that framework?

Yes. So we -- we always like to invest back in the business first whenever we can. And I always say, like, if I can invest in Ratings, I'm going to do that. That's the -- one of the best businesses in the world. You've seen us make. There aren't many opportunities to do that inorganically. We bought the largest domestic rating business in Africa a year or so ago. It's a great generational investment for us. And then from an analytics perspective, I mean, gosh, we have had to change how we think about what makes the most sense from an M&A standpoint. I think you would expect us to do that, right? So when you look at -- do you want to bring more workflow into our solution suite. It's got to be something that has a real proprietary data asset and data rights inside of it. And not all workflow is created equal, not all of the rights to the data that sit inside these systems that is a real focus for us as we think about that. I think it would be very unlikely we would buy workflow for the sake of workflow at this point. So -- and then obviously, everyone is thinking about, can they get access to proprietary data. But for us, if you think about what we have as a connected intelligence system, that's really what underpins all of our solutions, right? It's the world's largest database on companies, and a knowledge graph that we are building out that connects all of the companies to all of the different data sets and models and insights and ratings that we have, right? And so wherever we can find uniquely valuable data sets that we can put into that connected intelligence system, make this system itself more valuable, make that data more valuable and monetize that through multiple customer segments, that's attractive for us. Then I think Noemie, the last thing I would say on the earnings call, she talked about share buybacks. So obviously, we have a lot of dry powder if we decide that there's an attractive acquisition opportunity. Absent that, Noemie talked about a $2 billion share buyback this year. That's up, I think, something like 25% from last year. And Noemie did signal that we're aggressively buying back stock here in the first half of the year.

Yes. Okay. Maybe one quick -- very quick lightning around word association. So first, refi well.

Very strong.

Very strong. Okay. Private credit?

Needs independent credit assessment.

Margins?

Very robust.

Very robust. Okay. Rates?

TBD.

TBD. Fair. And M&A?

It's coming.

It's coming. Okay. All right.

By that, I mean the market.

The market, right. Clarification. All right. Well, I think that wraps up time. Rob, really appreciate the conversation. Than you so much for joining.

Thank you.