MCO - NYSE

Good day, everyone. Welcome to the Moody's Corporation Fourth Quarter and Full Year 2025 Earnings Call. At this time, I would like to inform you that this conference is being recorded and that all participants are in a listen-only mode. At the request of the company, we will open the conference up for question and answers. The call is scheduled to last approximately one hour. I will now turn the call over to Shivani Kak, Head of Investor Relations. Please go ahead. Thank you. Good morning, and thank you for joining us today. I'm Shivani Kak, Head of Investor Relations.

This morning, Moody's Corporation released its results for the fourth quarter and full year of 2025 as well as our guidance for 2026. The earnings press release and the presentation to accompany this teleconference are both available on our website at ir.moodys.com. During this call, we will also be presenting non-GAAP or adjusted figures. Please refer to the tables at the end of our earnings press release filed this morning for reconciliations between all adjusted measures referenced during this call and U.S. GAAP. I call your attention to the Safe Harbor language which can be found towards the end of our earnings release. Today's remarks may contain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In accordance with the act, I also direct your attention to the Management's Discussion and Analysis section and the Risk Factors discussed in our Annual Report on Form 10-K for the year ended 12/31/2024, and in other SEC filings made by the company, which are available on our website and on the SEC website. These, together with the Safe Harbor statement, set forth important factors that could cause actual results to differ materially from those contained in any such forward-looking statements. I'd also like to point out that members of the media may be on the call this morning in a listen-only mode. Over to you, Robert Scott Fauber.

Thanks, Shivani, and thanks everybody for joining today's call. I'm going to start with the highlights. And 2025 was a record year for Moody's Corporation. It was driven by consistent execution against the long-term demand trends that we have discussed over the last several years. And we finished the year with strong fourth quarter performance across both ratings and analytics, and delivered robust growth and meaningful capital returns to shareholders. Now we are scaling decision-grade contextual intelligence embedded directly into customer workflows. Across our platforms, third-party systems, AI-enabled interfaces. So that we are present where critical decisions get made. As technology and the ways of working continue to evolve, we enter 2026 well positioned and confident in the opportunities ahead. Now we had strong top line performance across the company in 2025. Total revenue exceeded $7,700,000,000. That was up 9% year over year. And 9% in both ratings and analytics. We expanded adjusted operating margin to 51.1%. That was up 300 basis points as we drive further operating leverage into the business. And these results are being driven by sustained customer demand for our decision-grade data, analytics, and insights, amidst very large funding needs, greater market complexity, heightened risk and resilience needs, and compliance requirements. Now adjusted EPS—sorry. Adjusted diluted EPS reached a record $14.94. That was up 20% year over year. And that represents a 70% earnings growth over the past three years. It is something like a 20% CAGR since 2022. Now let me turn to ratings. And issuance and investment cycles came together very powerfully in the fourth quarter. It resulted in the busiest fourth quarter in our history. And the investments that we have made over several years have really positioned us to capitalize on this activity and that drove record revenue this past year. In 2025, we rated $6,600,000,000,000 of debt. That was an all-time high supporting investment across infrastructure, AI-driven data centers, energy finance, energy transition finance, and private credit. And in the fourth quarter alone, we rated more than $70,000,000,000 of issuance for companies including Alphabet, Amazon, and Meta, in part related to their AI investment programs. Moody's Corporation was named Best Credit Rating Agency in the U.S. by Xcel again. That is for the fourteenth consecutive year. And that really reflects our role at the forefront of global debt markets. In December, we issued a request for comment on a cross-sector stablecoin rating methodology. And as the use of tokenized cash continues to accelerate, the total value of issued stablecoins is forecasted to reach $400,000,000,000 by 2026, and $2,000,000,000,000 by 2028. And our methodology, which is the first such framework from a credit rating agency, will position Moody's Corporation to play an important role in the digital finance ecosystem. Now in private credit, demand for ratings continues to accelerate. Private credit revenue increased 60% in 2025. Reflecting both market growth and our expanding role in the sector. And we developed new methodologies and deepened our analytical and commercial engagement to capture rising demand for transparent, independent credit assessment. And that momentum is translating into tangible wins. Last year, we were the sole rating agency on the largest private credit CLO of the year, a $1,500,000,000 issuance by Blackstone. Now pivoting to Moody's Analytics. We finished 2025 on a strong note there as well. We delivered net growth that outpaced 2024. And this performance included meaningful contributions from our highest priority growth areas. That includes our lending and credit decisioning solutions, as well as decision-grade KYC data. We also closed the year with strong momentum in AI-related sales, ranging from specialized workflow agents to AI-ready datasets I am going to talk about that in just a few minutes.

Importantly,

our strongest growth came from our largest strategic customers. These customers contributed over 30% of the total MA net growth in the fourth quarter and for the full year grew at twice the rate of the rest of the MA customer base. So this is durable, high quality growth with clear evidence of customer adoption. And I want to emphasize durable because the nature of MA's revenue growth is increasingly recurring and scalable. So recurring revenue grew 11% and represented 97% of fourth quarter revenue. So this, combined with some real execution discipline, enabled us to deliver 190 basis points of margin expansion and an adjusted margin of almost 36% in the fourth quarter. We set our focus on scaling MA's recurring revenue base a few years ago, and now we are making further proactive adjustments to our portfolio to reinforce that strategy. So in December, we closed on the sale of our Learning Solutions business. That was primarily reported as transactional revenue. And it really was no longer core to our strategy. We also announced the sale of our Regulatory Reporting business, which served customers with relatively limited cross-sell opportunities across other banking offerings. And underpinning all of this is our commitment to delivering best-in-class solutions. And that commitment was reinforced by our recognition as the number one provider in the Chartis RiskTech100 for the fourth consecutive year, and that reflects the trust that customers place in Moody's Corporation to support workflows and decisions that matter most. And we see that market recognition reflecting a broader truth that as AI becomes a new interface for decision making, the need for trusted context increases, not decreases. AI systems require verifiable, permissioned, domain-specific data and analytics to produce outputs that are accurate, explainable, and defensible. That is exactly what Moody's Corporation provides and it gives us the opportunity to become even more deeply embedded in customer workflows. So we see this clearly in recent customer behavior. Customers who have purchased or upgraded into at least one standalone GenAI or AgenTix solution are retained at a rate of 97% and are growing at roughly twice the rate of the rest of the customer base. So this is not experimental usage. AI adoption is driving greater consumption of our proprietary data, expanding our share of wallet, and reinforcing long-term customer economics, particularly amongst our largest strategic accounts. And a key reason for adoption is that it is accelerating as customers consume our intelligence. So Moody's Corporation’s solutions are delivered through our own applications. And increasingly, they are embedded directly into customers' existing technology stack and third-party workflow platforms. That includes systems like Salesforce, ServiceNow, Coupa, Intapp,

Databricks,

and we have made our content available through smart APIs and MCPs and spec agents for consumption through our customers' own AI platforms and going forward through AI portals like Claude and OpenAI.

And

Thank you. If you would like to ask a question, please dial 1 on your telephone keypad. If you are on a speakerphone, please pick up your handset and make sure your mute function is turned off so that your signal reaches our equipment. We will ask that you limit yourself to one question. The option to rejoin the queue will be unavailable. Again, that is 1 to ask a question. Our first question comes from Curtis Nagle with Bank of America. Please go ahead.

Terrific. Thanks so much for taking the question. Maybe Rob, just a quick one from you. Just from a portfolio perspective,

for MA, it seems like it is in a pretty good place. But I guess, do you feel like at this point, you have the right assets, the highest growth, you know, the ones you are most confident in terms of investment, or, you know, should we expect more more paring, you know, this year?

Curtis, first of all, welcome to the call. It is great to have you on today. I would say we feel very good about the assets and the capabilities that we have. And you heard me talking about this, Curtis, a bit in my prepared remarks. I mean, I think we all understand that data and trusted data is going to be the fuel for AI. And especially for the big regulated institutions that are, you know, big customers of ours. And so we feel very good about having built out this massive data estate and then as you heard me talk about it, it is about linking that, and it is about the ability to draw insights

across

that network of data. So, you know, I think and, again, I think we also understand that proprietary datasets will be at a premium going forward. And wherever we have an opportunity to add, you know, uniquely valuable data into this giant data estate, putting it into our context layer, helping to build out our network graph, I think you are going to see us do that. In terms of the trimming, you know, I think this just, you know, you hear us talking about, you know, where we are making the more concentrated bets. And I talked about lending and credit decisioning, KYC and compliance, and insurance. And those are the places where we think we bring, you know, the strongest set of capabilities, the deepest customer relationships, that give us the strongest right to win. And so we felt there was just an opportunity to look across the portfolio at things that were not as central to that and had an opportunity to, as you said, kind of, you know, prune the portfolio and allow us to focus even more on the areas of the greatest scalable growth opportunities.

Okay. Thank you. Appreciate it.

Our next question comes from Alex Kramm with UBS Financial. Please go ahead.

Yes. Good morning, everyone. I want to stay on MA. Thanks

to both of you for all the AI detail. A lot of impressive stats. On the flip side, though, it does not sound like it is really translating into ARR revenue yet. Maybe it is. But, obviously, if we look at the guidance and the results, relative to your medium-term outlook, those have, you know, kind of softened a bit. So I guess the question is, when is AI really going to contribute? And if it is already contributing, are there some other issues elsewhere in the business? So maybe an open question there. Thanks.

Hey, Alex. Thanks. And I think in a way, there is kind of two parts that I want to unpack in that question. The first is kind of your observation around the trajectory of MA. And I would say that our fourth quarter ARR was in line with the third quarter. And, you know, as you would expect, when you have got, I am going to say, kind of a portfolio, you know, we are selling into very different customer bases. There are some puts and takes in terms of what is growing faster and what is growing not as fast. If you look at, you know, kind of the ARR trend across the portfolio in 2025, I think you would see that actually banking, research, and data actually picked up a little bit. And we had some headwinds with insurance and KYC. And as you heard, you know, Noémie mentioned, we have talked about before on the call, some of that with KYC was impacted by DOS. And, you know, you see our guide that is consistent with these growth rates. I talked about the new products and the cross-sell and upgrade pathways that are going to drive that growth. But I think maybe one other point I want to just double click on: everybody wants to understand how much revenue is being generated by AI. And there were two stats that I, again, I want to come back to because I do think they are leading indicators for us. One is the fact that those largest accounts for us are growing at about twice as fast as the rest of the portfolio. That is really important because that is where we have the deepest engagement with the most sophisticated institutions on the planet. And that is where they all want to be able to consume our content and bring it into their own AI workflow orchestration platforms and consume it through AI portals. So there is a lot of AI-oriented engagement with those big institutions. That is what is driving and importantly driving that growth. And then second, you know, we have that stat about the cohort of customers who have bought at least one standalone or packaged or upgraded into an AI solution, that is growing twice as fast. Again, because of the level of engagement. So I think, Alex, you know, I feel good that the most sophisticated institutions are where we have got the most growth and the most engagement around AI. And, you know, our view is that that is going to then trickle through the rest of the customer base over time.

Very helpful. Thank you.

Our next question comes from Manav Patnaik with Barclays. Please go ahead.

Thank you. Good morning. I was just hoping on the ratings side, you could just

help us with the cadence for the year in terms of how you, you know, assume the issuance trajectory there?

Yeah. Manav, hey. Great to have you on the call. So I am going to start with issuance, then maybe I will just go into revenue real quickly for you. Because I know that will be helpful. So we are expecting issuance activity, like we typically do, to be more heavily weighted towards the first half of the year. We have very attractive market conditions and, you know, there is a, I would say, relatively strong start to the year as well.

And

that is also in line with what we have been hearing from the banks who we have been talking to, who think that the issuance, again, will be a little bit front loaded in the first half of the year. To give you a sense, that is probably mid-50s percent of total issuance is going to be in the first half of the year. At least that is what we are modeling. That was pretty consistent with 2023–2024. 2025 was a little more back-end loaded, I think, as you know. And that is also a pretty consistent pattern that we see with frequent issuers. So to put a finer point on it, Manav, we are expecting issuance to grow in 2026 in the kind of high single-digit range versus the first half of last year and to decline mid-single digit in the second half. And in the first quarter in particular, we think we are going to see kind of high-20s percent of issuance in terms of as a percent of the full year. Now when we go to revenue, it is a little less pronounced in terms of being front-end loaded. So I would say from a revenue perspective, we expect it to be, you know, somewhere in the low to mid-50s percent of revenue in the first half of the year. I think importantly, we do expect revenue growth in each quarter of the year. We think that we are going to be somewhere in the mid-teens for revenue growth in the first half of the year. And somewhere in kind of the low single-digit range for the second half of the year. And for the first quarter, probably somewhere in the mid-20s percent.

Alright. Super helpful. Thank you.

Our next question comes from Toni Michele Kaplan with Morgan Stanley. Please go ahead. Thank you so much. I have been getting an increasing number of questions recently around how much of your data is proprietary, the sources of your data, and which parts and how much of MA is based on proprietary data. I was just hoping that you could dimensionalize this in a way that you think is most helpful for investors. Thank you.

Yeah. Toni,

rather than me sitting here and trying to convince you of some statistic,

let me

help you think about it in slightly a different way. And this is about why we think we are well positioned in an AI world. And first, as you said, like, we all understand we have a massive proprietary data estate. And you heard me talk about we are in the process of unifying all of the data, the models, the ratings, the research, the risk assessments into a really a single normalized record for each entity. And that is going to be able to give us the ability to create a very, very powerful knowledge graph.

Alright? And then we are going to keep adding to that.

And that is going to enable

the agents to be able to access a

comprehensive, interconnected view of any entity. And, as I said, give unique insights and allow for richer decision making. But the second thing, I think this is important, is we are assembling all of that into what we call—and you might have heard me use this term—a trusted context layer. So that context layer sits between the raw data assets and the AI reasoning engines. So it makes the data usable for reasoning. And what that is is a structured, governed representation of, you know, what the data means, how it relates across entities and time and scenarios, when and why the data should be applied, and much, much more. Right? It is a deep contextual understanding of the data. Orbis, obviously, being a very important part of this massive data estate, is a great example. It is not just company data. It is years of entity resolution, ownership mapping, expert judgment, and, of course, a complex ecosystem of licenses and IP rights. And we have built all of that context directly into our analytics, our methodologies, and our models so that then the outputs are accurate. They are explainable. And they are defensible. As you have heard me say, and I love this term,

they are

decision grade. So hopefully that gives you a sense. It is all of that together that makes our data, I think, uniquely valuable.

Thank you.

Our next question comes from Ashish Sabadra with RBC.

I wanted to ask a follow-up question on AI. Thanks for highlighting the AI resilience and strong demand for the agentic solution. One of the investor concerns lately have focused on the adoption of white coding and multiplatform LLM offerings such as Claude for financial services, and those potentially impacting vertical software or workflow solutions. Can you talk about the moat around the software or vertical solution within MA? Thanks. Yeah. Ashish,

hey. Great to have you on the call. Again, I think the way to think about this—and it is interesting if you think about, you know, you heard me talk about CreditLens and our lending solution, and that has an AI-enabled layer to all of it, from the ingestion of financials to credit decisioning and covenant monitoring and much more. You have got different adoption curves with different customer segments. So, you know, you heard me say at the high end, almost all of the banks, the big tier one sophisticated banks want to be able to consume our content in a variety of different ways and it is typically not through software.

Right? But

what they want is, you know, we had a bank that is working on AgenTix—I mentioned it in my remarks. They are building an agentic workflow for lending. So while they do not need to adopt CreditLens, what they do want is they want our specialized agents around credit memo generation and early warning that are populated with all of our data. And access to our models. So they are consuming it either through smart APIs and MCPs or specialized agents that are going right into the workflow that they are building.

So

for me, again, it comes back to, you know, we talk about we are going to be wherever our customers want us to be. If you are a tier three bank, and you want a lending software platform that is enabled with AI, and has access to a lot of our content, we are going to sell that to you. If you want our content through, as I said, different ways to consume the data or specialized agents, we will do that. If you want to consume it in an enterprise software system, we will do that. So in a way, Ashish, I am actually less worried about it because at the end of the day, and we have always talked about this, the software that we have built is simply a delivery chassis for the content. It is not just some business logic that we have sold to a customer. It is a delivery channel for the content. We will deliver it through software. We will deliver it into your AI platform. It does not matter.

Our next question comes from the line of Andrew Steinerman with JPMorgan. Please go ahead.

Hi. I have a simple one.

I just want to know how much revenues these two MA divest—

Thank you, Noémie.

Our next question comes from Owen Lau with Clear Street. Please go ahead.

Good morning. Thank you for taking my question. I want to go back to your MIS margin guide, which is better than expected, and I think it is even higher than your medium-term guidance which is around low 60%. Could you please talk about the driver of these strengths, and how should we think about your medium-term guide from here? Thanks a lot.

Yeah.

three, four years on technology

enablement.

improving and getting those margins level. We are investing in analytical staff to support, obviously, the volume, but also areas like private credit. We are looking to also invest in our commercial efforts, as well as methodology groups, technology more broadly. So we are still investing in Moody's Ratings and at the same time expanding margin through those investments in technology.

Our next question comes from Craig Huber with Huber Research Partners. Please go ahead.

Oh, great. Thank you. Rob, I thought you did a really good job talking about your

AI moats that you have. But, just a little further on that. Within Moody's Analytics, there is obviously concern out there with investors. You can see it in your stock price and all in your peers as well that AI firms or firms that pop up or exist that have AI tools over time could replicate what you guys do in parts of your MA operation. Can you just talk a little bit further about the moats? Where do you think—just to talk on the other side of this—where do you think maybe you are vulnerable to a third-party AI initiative to take some share away from you there on a meaningful basis? Then on the second way to look at this is there is a lot of concern out there, people talking about that AI is going to ravage the white-collar workforces out there in the U.S. and around the world. Talk to us, if you would, about MA, how you price your product here. It is not really on a per-seat basis. But if white-collar headcount out there goes down 25% plus, just say hypothetically, at a lot of your institutions, how will that impact how you get paid, how much you get paid when contracts come up for renewal? Not existing contracts, but when they come up for renewal, how may that impact your discussions there? Thank you.

Yeah, Craig. Some good stuff there. Thanks for the questions. Let me just talk a little bit. I am going to go back to Orbis for a moment because it is one of our biggest parts of our data estate. And, you know, we get questions about this. And, you know, I would say a few things in terms of what make it very hard to replicate that I do not think are understood. First of all, a lot of the data just simply is not available to the public. We have a, you know, a complex ecosystem of commercial agreements and IP rights. I mean, that has taken us decades to build and we are constantly curating that. Second, you know, there are legal and regulatory issues, you know, privacy laws and export controls and all sorts of things that our customers need to know that we are abiding by, right, if they are going to use the data. There is semantic complexity. This gets into, you know, things in different jurisdictions mean different things. And models have a lot of challenges with semantic drift. So that is where we have been curating all this and our local experts over decades understand what different things mean in different locations, and then they are cleansing and normalizing that data to make it valuable. There is entity resolution and ownership inference. And, by the way, you know, the models are not simply doing entity resolution. That is a really important thing to be able to resolve against the right entity. And we have combined probabilistic models, a human-in-the-loop validation, and proprietary logic. And we have been doing this over years and years and years. Then we have got all this historical depth. Right? So we have a lot of historical depth and in some cases, the data has either been archived or it does not exist in digital forms. It is not easy to get some of that history. And then finally, governance. I have got to tell you, Craig, you know, every bank I talk to tells me good enough is not good enough for our institution. What they want from us—they want to move in many cases to fewer trusted providers. So they want us to be able to meet their needs. And look, I will, you know, I will acknowledge, Craig, that things like, you know, automated data ingest and things like that will be done by AI. But it is those things that I talked about—it is not just Orbis. You could go across a number of other datasets that we have, and the same is true. So hopefully that gives you a sense. Now let me talk about how do we price the product. And we have never had seat-based licenses. That is not the way we have operated. We have always tried to, you know, kind of think about value in our pricing schedules.

But

look, we are starting to trial in parts of the business different pricing models. Right? And thinking about elements, bringing in elements of consumption-based pricing that I think will be more closely aligned to outcomes. Because at the end of the day, Craig, what you are talking about—if there is a substantial labor replacement—somebody and some companies are going to capture some of that opportunity. Maybe not all of it, but they are going to capture it. Right? And that is going to be, in my opinion, a combination of the model providers and the data providers who are making that efficiency possible. And so we are thinking as we speak and trialing different pricing models to be able to capture some of that—frankly, some of that upside.

Great. Thank you, Rob.

That concludes our question and answer session. I will now turn the call back over to Rob for closing remarks.

Hey, thanks everybody for joining today. And for my colleagues at Moody's Corporation,

let’s go.

Talk to you next time.

Bye.