Moody's Corporation (MCO) Earnings Call Transcript & Summary

Hi, everybody. I'm Andrew Steinerman. Welcome to the info services track of the Ultimate Services Investor Conference. If you get a chance, pull up an information services data book, which is our quarterly claimer on the sector since 2013. This is Rob Fauber, the CEO of Moody's. We appreciate you coming back every year.

Thanks for having me.

It's always a really good discussion. And don't worry, everybody, we will get to discussions about AI. I just thought we'd ask some questions beforehand. So when you look at just this year in terms of issuance and ratings revenues, your expectations were more modest at the beginning of the year and have been more robust as the year has gone forward. What's driven that kind of upside to issuance relative to initial expectations just this year?

We adjusted downward after liberation days, as you remember, and then we've come back since then. I would say that -- a few things. We, originally, at the beginning of the year, had a view about kind of M&A in the Trump administration. I think there's a little bit of a fall start again with Liberation Day. But as we've seen in the second half of the year, M&A has really picked up and a lot of strategic M&A. And we're also looking at sponsor-backed M&A because there's a real flywheel effect that goes on in our business when we see sponsor-backed M&A. But you've got M&A volumes picking up. You've got economic growth that, while has slowed a bit, not as much as people thought. So it's actually been better than market had thought. You've got default rates, which are slightly above long-term averages, but have generally been coming down, maybe a little slower than we thought, but spreads are really tight. They're at near multiyear lows. And all of that's pretty conducive for issuance. And so the strongest issuance that we've seen this year has been in the corporate segment, both opportunistic investment grade, we see a lot of big infrastructure financing getting done, some of that getting done through corporates, and then leverage finance, both high yield and leveraged loans.

And when you say M&A, usually the ratings and the issuance happens closer to the close, right? So like M&A announcements this year should even help issuance even more so next year, right?

Yes. That's right. As we look into next year, we have a service called Rating Assessment Service. So we have companies that come to us, and we'll understand what their rating profile may be in an M&A transaction. That pipeline is very strong at the moment. That's the same thing that we're hearing from bankers that the M&A pipelines look quite good. And now as we're going to round into -- from Thanksgiving and into the end of the year, some of that deal flow is actually going to get -- also get announced in the beginning of the year, and as you say, then get financed subsequent to that.

Okay. Talk about the 4 deep currents. These are something you've been talking about for a while. Are they coming to fruition in terms of revenue growth the way you would expect them?

Yes. So it's interesting. During COVID, or right after COVID, and we were a beneficiary of COVID. We were a COVID stock, right, with ultra-low interest rates. There was a lot of fretting from investors who said, "Oh, my gosh, interest rates aren't close to 0. It's going to be terrible for your business." Obviously, there was an adjustment period in 2022. We ripped the band-aid off and rates moved up. But I would argue that we're in a much better environment for debt issuance over the medium term than we were then, right? Then it was a monetary bubble. And now we look at what is going to drive financing volumes. The first one is there's just a massive amount of debt that's been issued over the last 5 years. And that debt has got to get refinanced. We've published these. We call them our refinancing walls. Those look quite good, especially for speculative grade debt. So that kind of underpins issuance. And then the deep currents that we talk about, private credit and banks coming off -- assets coming off of bank balance sheets and going into investor markets, capital markets, that's securitization. That's a positive for us because we're providing credit assessment in many cases. The -- both infrastructure -- I've seen a BlackRock report that says something like $68 billion of infrastructure financing needed by 2040. But of course, AI, it's all in the news, these massive AI data center and infrastructure investments also driving that, and we're seeing that. And then I'd say in the earlier days. So that's rolling through the ratings business now, Andrew. And then I would say earlier in its maturity is digital finance. We do feel that, that is an inexorable trend and transition finance. I think maybe a little bit of that slowed down a little bit, but when you look at companies that are going to be decarbonizing and evolving their business models, they're still -- and what we're going to do with energy grids and all of that, there's still a lot of financing that's going to get done for transition finance. So that's still out in front of us.

Okay. That's great. Okay. Well, so when you look at the categories, you just mentioned a moment ago that spec-grade looks good. But when I look over the Moody's categories of issuance projections, both structured finance and the public category was actually tapered in terms of MIS rating issuance outlook. Why is that? And is this an important thing to watch? Obviously, leveraged loans and high-yield bonds are more important. But should I be watching these other tails?

Look, there are always ebbs and flows within the different asset classes. That's one of the great things about the business is sometimes when we see -- we'll see issuance slowdown in 1 area and we'll see it pickup in another, whether it's a region or an asset class. In this case, Andrew, you're right, corporate has been very strong for the reasons I talked about. In a couple place -- parts of structured finance, primarily around consumer finance, we have seen a little bit slower growth than we had thought in the beginning of the year. That's not particularly surprising because I think there are elements of a 2-speed economy in the United States. There's the AI economy and then there is kind of everybody else, and we've seen a little bit of stress in fact as we move down the socioeconomic spectrum, right, with subprime autos and undocumented populations. And so you see a little bit of that in the -- in parts of our structured business. As it relates to project and infrastructure finance, it's an interesting question, right, "Hey, if there's all this infrastructure funding, why did you, again, modestly trim our outlook for the year?" But yes, all -- that stuff is -- it's interesting. Just take data centers for a moment. They're coming to us through all of the different lines of business within ratings. So you've got data center financing that's getting done in our corporate rating segment. I'd call that infrastructure, but that's in corporate. We see it in CMBS. We see it in REITs. So it's rolling through different parts of the rating business. So there's a little bit of a, I'd say, a quarterly downtick just in that particular line. But infrastructure is much broader than that across our rating lines.

Okay. That's fine. When looking at the MA organic revenue growth targets, the medium-term targets that you set earlier high single digits to low double digits, you kind of left that kind of low double digits there as kind of an ambition. What would it take to get there? Like is that really a stretch? Or is that kind of a key part of the range?

So we're not bringing forward any of our guidance estimates today. That's still the -- certainly the medium-term targets in this particular case. I'd say a couple of things. One, this year, we've talked a little bit about a few of the, I have to be careful about this, the idiosyncratic things that we've experienced in terms of whether it was canceling a distribution agreement, whether it was [ Doge ], a little bit of the ESG runoff from when we did the MSCI partnership. I only caution that because I -- every -- we're in a very dynamic world, and there're always things that are happening, but those things did provide a headwind to growth. We had a little bit higher attrition in those particular areas for those reasons. And I would just go back to kind of what is it going to take? It's a -- in particular, we're going to be investing where we see where we have the strongest right to win and the strongest growth tailwinds. And those are going to be in our banking segment. It's around lending. Right now, we feel very good about our lending suite. In fact, that's growing faster than the rest of Moody's Analytics. Underwriting, and particularly building out an insurance and expanding from property into casualty and financial lines and that's another opportunity for us, cyber. KYC continues to be an important opportunity. Certainly now with AI, there's a really interesting opportunity between our data and agents and thinking about providing huge amounts of value to our customers that have very manual, people-based workflows. And then the last thing I'd say, Andrew, is kind of an agentic layer over top of our content estate. In general, I think of AI as a great opportunity for us. It must be a tremendous unlock when you have a massive mostly proprietary data and analytics estate. And I think this offers us at this moment in time so many more ways and channels for us to monetize that content.

Okay. Maybe we should jump into AI since it just seems like the conversation is naturally migrating that way. I have this figure that I put together, really was kind of worked from the research we did over the summer of who's most at risk, who's least at risk. The rating agencies are actually, in our opinion, kind of least at risk, but just -- let's just start out with a big picture question about info services. There's been a sell off broadly of info services stocks. It's not just Verisk and Moody's, it's everybody. We've sold off and there's a worry about AI. And of course, I've come to the conclusion there is companies more at risk and least at risk. Just start with the big picture point, do you think this is a group, the whole group that's going to net benefit from AI on average, or be dislocated by AI, the whole group?

So take it with a grain of salt to who it's coming from, right?

I know.

I firmly believe that this must -- for the reason I just touched on, this has got to be a big opportunity for the owners of, I'm going to say, proprietary or heavily derived data and analytics. And I'm happy to kind of dig into that, but...

Please do.

Okay. So why is that? And we've talked about this a lot today. First of all, I think there are many more opportunities for us to monetize that content across new customer segments, new customer personas and new use cases. And in some ways, I think about a utility curve of our content, I'm going to give an example of our catastrophe models, okay? So we have these really sophisticated catastrophe models that the insurance industry uses to assess risk of extreme events. We've done an amazing job of monetizing those models at the very high end of the utility curve with catastrophe modelers through our catastrophe modeling software. But guess what, that IP is very, very valuable to personas and customers well beyond insurance companies. So you're a bank and you want to understand the risk of a piece of real estate that you're taking as collateral, we have an opportunity to leverage that IP and to be able to provide that to a bank during their lending process. I can embed that into my software, I can pull that into an agent. So in general, I just look at this and think, gosh, there's so many more ways to access our content and for me to think about who I'm serving, what use cases and how I price along that utility curve, many ways, I think I'm just getting started. And then the other thing I would say to this is, and this might sound a little trite, but more and more -- we know there's enormous value to the data, right? All of a sudden, we've -- not all of a sudden. We know that our data is valuable in supply chain and supplier risk, for example, right? So now I've got -- I know that I want to have my data and my content and my models where our customers are making decisions, whether that's in SAP or Salesforce or Coupa, any of those third-party platforms, whether it's in a bank's internal AI workflow orchestration layer, take my content with AI rights to it, take my specialized agent, or whether it's in our software and our web platforms with an AI interface or agentic layer over top. I don't care. There's many more ways for me to monetize that content for broader uses and also thinking about, over time, different commercial models for that content.

And if a financial customer discovered Moody's data on a third-party LLM and wanted to subscribe to the data, would you charge them the same as an existing customer? They might not have the broad use cases that existing customer has.

I mean it really depends. We're going to have a variety of different pricing models. I'll tell you, so you -- in that case, I think you're talking about a situational access to our content. So what I'd like to do is I think of that example, Andrew, is somewhere lower on this utility curve, right? So there, I've got to have the capability to be able to do essentially digital fulfillment, right, and enablement for that content at that moment of time. Historically, our company and many companies like us, we have products and we have field sales, right? And so what would happen in that particular scenario is, in theory, up to now, it would kick off -- you have to call a salesperson. That's not a scalable model. So we've built a platform layer underneath of all of our application estate, starting with single sign-on and moving to metering and fulfillment to be able to understand what are our customers doing across our applications, and how can we then start to think about a digital fulfillment model that will allow us to sell the content and monetize somewhere different on that utility curve. The one other thing I'd say about, you gave a bank example. Our content with the big banks is being consumed all over these institutions in different departments in different parts of the world. We'll have many different contracts. There's a really interesting opportunity at this moment to up level the way that our content is consumed at these institutions. In many cases, like at JPMorgan and others, they're building out these AI workflow orchestration platforms. They might be at the enterprise level or more likely at the corporate and investment bank level, the commercial bank, and to have us be able to get core parts of our risk operating system, as I like to call it, make that available to the AI and then be able to have that content consumed much more broadly across the institution to serve many more use cases and then I'm going to price behind that.

Right. I just want to make sure that Moody's is going to lose its pricing power as it does more digital fulfillment for new customers.

Yes. Again, when you're talking about pricing power, we're going to price for the utility and the use case. I think that's going to be very important.

So you saw my risk continuum a little bit here. And I put the ratings as rating agencies least at risk. If you were going to talk about MA, where on the risk continuum for AI do you feel like MA is? And I know, obviously, MA is a mix of businesses.

It is. And if you think about our content estate, the largest part of the content in Moody's Analytics is the exhaust from the rating agency. It's the research and the data. It's all proprietary. We're creating it. On average, we're issuing a rating every 20 minutes, 24 hours a day, 7 days a week. It's all proprietary. You can only get it from Moody's. We then have built out a -- what I think of as one of the world's -- I think it's the world's best commercial credit franchise. So we have credit models and we have a giant contributory proprietary credit default database contributed by banks that helps us to calibrate models for public companies and private companies. And then we've gone all the way down to credit workflow, right? We have loan origination, a lending suite. We have asset liability management software, portfolio analytics, all because we have such deep domain expertise and proprietary content in credit, right? So that anchors our research business, for the most part, anchors a lot of the banking business. I'm now going to move to insurance, and I get asked questions about, well, you sell software. Our software -- we're only in the software business as a delivery chassis for the content. Yes, we have something called the Intelligent Risk Platform, which has I think industrial strength cloud compute to run models for the insurance industry, but really what the insurance industry is buying are the cat models. And I got asked earlier about, well, could AI just recreate the cat models? It's much more than that. Our cat models, first of all, are the currency of risk across the global insurance industry. It's how they manage and price risk. And our cat models are then calibrated with claims data from the insurers. The insurers want and need these models to be accurate. So they work with us to help us with the calibration of the models. An interesting example, the insurance industry wants to grow cyber insurance underwriting. So we work together with the industry, biggest broker, biggest reinsurer, biggest insurers of cyber to form a cyber industry working group where they contribute claims data and content to us to help build models and solutions for the industry to help the industry grow and write more cyber policies. So that's how to maybe think about insurance. And the last part because I get asked this question a lot, and I think this is important, is around our massive company database, right? And this powers a whole range of use cases across banks and insurers and corporations. So we have the world's largest database on companies, 600 million, 2 billion ownership links. We have really rich data on politically exposed people and adverse news and all of that, we link it all together. The biggest use case for that is KYC. But that is assembled through a relatively complex ecosystem of information providers. So we have to have the rights to use the content. We have commercial arrangements with them. We then normalize and cleanse the data and make the data available. So it's not as easy as you can just go out and scrape all this data. Is there data available on private companies that can be scraped? Yes, there is, but not what we're doing through these company bureaus where we curate an ecosystem of information providers where we have the rights to use the data.

You're not going to believe that I don't know the answer to this question. My question is, you already have an incredible database of private companies in your credit research, like could you combine your BvD database with your credit research database? Or do you have to keep those separate?

So if you were to go on to moodys.com today, you can type in any company that you want. And Andrew, you're going to find rich information on companies, whether it's public or private. You may find model-derived ratings on private companies, right, where we're leveraging our credit models, where we have financial statements on private companies, and we say that the financial profile of this private company not rated is a BA1 model implied, right? And guess what, that gives us -- that's also what we're bringing to the private credit opportunity. If I could just touch on that for just a second. Private credit is a super interesting opportunity when you have arguably the world's best commercial credit scoring franchise, right? It starts with ratings on public companies, but we have the ability to put a model-derived score with high fidelity and high confidence on virtually any company on the planet. Now as it goes to smaller and smaller and less information, the range of confidence around that is wider, but, gosh, you want to have -- understand the credit profile of a private company, we can do that. And we've been doing it.

I think what our answer was, if it's model-derived ratings, yes, we can combine it with our other database. But if it's a ratings that is by an issuer, you can't combine it?

The one thing we're going to do is make sure that if you're using our rating, you will know if it came from the rating agency or it was model derived.

That's a fact.

Other than that, it's all going to be available to the same investor group because, guess what, our investors tell us all the time, "Hey, look, in my portfolio, I've got 90% public and 10% private. I need to help on the private." We now -- we offer that. So what do we do? When we layered in all of those hundreds of thousands of private companies, we went back to our CreditView customers and said, "Hey, are you interested in the private company package, right?" There's an upsell.

Just to make sure I got the question right. There are some pieces that you can't combine together, right?

We're not sharing information that we get from the rating agency with any other part of the institution. Yes.

Okay great. Let's open it up for questions for Rob.

Maybe if you could expand on that MSCI partnership. I guess where is that market at in terms of is this being demanded by kind of the investor groups and the LPs? Or is this something that build it and they will come? And maybe if you could just also expand on what exactly you guys are doing together as well?

Yes. So it was interesting. I was on the road for most of the last 2 months. And at the beginning of that trip, when I would sit down with various folks in the investment community, and I was -- I spent most of the time outside the United States, and I would ask questions about how are you understanding the risk of your investments in private credit? It was interesting. And I would get, well, it's a higher-yielding asset class, lower defaults. That's interesting. But towards the end of that trip, I had a very different -- started to have a very different level of interest and engagement. Why are you asking? Tell me more. Yes, I've been wondering more about the credit quality of my private credit funds. And so what we did with MSCI, they had a data set. It's hard to get access to information on these companies. We have the credit models and they had some data. And so we went together to their customers that are on their -- one of their GPLP platforms and said, "Hey, if we could provide you a Moody's modeled credit rating," so we take a probability of default and map it to a rating, "Would you be interested in understanding what the credit profile is of your investments in your private credit funds? Would that be interesting to you?" And in many cases, we had very good feedback and investors said, "Yes, well, it would be interesting." So we had to think about how much are they going to pay and what's that going to look like and all of that. And so it's not going to be a game changer from a financial standpoint, right? What's really interesting, I think, is that we're introducing the language of credit ratings and credit risk to these investors to help them have a third-party rigorous independent understanding of what the credit risk is in the funds they're invested in, and to allow them to have a dialogue then with the GPs who are -- today, how do they understand the credit risk? It's informed by the GP, right? They've -- they're telling the investors what the level of credit risk is. So if you think about the way we built this business over decades, it was by building investor demand. The investors found the ratings useful. And so once again, what I want to do is have the investor community in private credit start to use our ratings to say, "Hey, I need to know more. I want to ask why you guys have marked it like this and why Moody's is market like this? Help me understand this." And over time, you could imagine more and more of the GPs -- because this is really about direct lending, right? More and more of the GP saying, look, rather than having Moody's effectively providing a model-based score on our funds, why don't I just go to Moody's and have them provide an assessment with my engagement, whether I'm APOLLO, Blackstone, whoever it is, right? That's the way our business works is we have issuers come to us. And so we look at this and think we have a very important role to play in the private credit market just like we did in the public credit markets. We created the language of credit risk and then we developed the scorecards and the data and the benchmarks and the research to help investors understand risk and scale the market. And that's what private credit. When I talk to all of the big GPs, say, look, if you're going to go from $2 trillion to $10 trillion, you're going to need this, and we can play a very important role here.

Let me ask you a question about clients that you have that are very AI forward. Do you find that they consume more data and content from Moody's? And I also -- sort of an add-on question, are these forward-looking, forward-leaning AI clients more in the regulated industries? Like is that who's moving quickly? Or are they moving in a more measured way?

So the first part, we gave some interesting data about looking at the cohort of customers who take some form of AI solution from us from those who don't. And this was back a quarter or so ago. And we talked about, it was like almost twice the growth rate of that cohort, meaning we're varying -- think of them as maybe early adopters. So we have a different engagement model with the early adopters. So to your point, Andrew, yes, they're actually taking more things from us. We're engaging with them differently at different parts of the institution. That's what's particularly exciting. The second part of your question was around...

Regulated versus non-regulated...

Regulated. So it's interesting because it took the banks a while, right? They had to get through the risk governance and all that stuff. Every single big bank that we're talking to, we're actively engaged with them. It's -- and it's actually when we look at the growth of the tiers of our bank customers at the moment, the fastest growth is coming from the largest banks, where we have -- where that engagement is really about the content and pulling the content into their environment.

And ultimately, they're trying to measure risk better, right?

That's right.

Okay. Last questions for Rob. Go ahead.

I don't want to read too much into the answer to the question about MSCI, but just curious, you mentioned this inflection point while you were on the road where you were seeing more interest. Is that just an organic conversation that evolved between LPs and GPs? Or is there potentially some sense that these actors are concerned about greater scrutiny post first brands and if there's not some type of self-regulation?

Yes. And I'm specifically referring here to investors, right? So investors who are just saying, Hey, look, I've -- and it may be insurance companies. I've invested a lot in private credit. I'm watching what's going on in the market. And by the way, I, the investor, am now getting questions about the investments I've made in private credit, and how do I understand the credit profile of what I've invested in. And that's a place where Moody's has a great opportunity to help those investors by saying, "Hey, we can give you a third-party independent battle-tested view of credit risk. We've been doing it for 115 years." And so I just -- I mentioned that there's more in the news. There's more interest from investors because of what's going on in the news and the awareness now of -- we're not -- maybe the market felt frothier over the summer and now it feels like I think people are starting to focus more on credit risk, that's always good for our business.

Okay. Rob, I think that's the time for us.

Sounds like it.

Thank you very much.

Thank you.