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

All right. We will go ahead and get started. Thank you, everybody, for joining us this afternoon. I am Patrick O'Shaughnessy, capital markets technology analyst at Raymond James. Up next, we have Moody's Corporation. On their behalf, we have Steve Tulenko, who runs the Moody's Analytics business for Moody's. So Steve, thank you for joining us today.

Thanks for having us, Patrick. Nice to see many of you again today. It's been quite a whirlwind in the last couple of days. I don't know how you guys do this, 12 meetings in a day at 30 minutes each, it's pretty rough. Anyway, let's see how we do.

Yes. A little bit of speed dating.

Thank you.

So maybe to kick this off, we have a lot of generalists and portfolio managers attend this conference, and I think that's great. Sometimes they know companies really well and sometimes they're a little bit less familiar. So for the folks in the room who are a little bit less familiar with Moody's, can you maybe just provide a description of the company as it exists today and maybe compare that to what it looked like a few years ago?

Sure. So big picture, I'll give you some rough numbers to start. We've got roughly $6 billion in revenue from Moody's, about 50%, a little bit more than 50% of that comes from the unit known as Moody's Analytics. So the rating agency, many of you would know it as Moody's Investors Service. That's the business that's been around for over 100 years that John Moody really started when he invented ratings back in the early part of the 20th century. It's been generally about $3 billion. If you were to go back 5 years ago, the rating agency would have been about 60% of the revenue at the time and Moody's Analytics would have been about 40%. So the other thing I'd say is that the rating agency is a revenue base that is obviously very global in nature, very related to the bond markets driven by tremendous position in the U.S. and European bond markets, especially Asia as well. Growth there is often driven by flow in the capital markets and somewhat cyclical. And I would say the other thing about the revenue base and the rating agency is that often fees are driven by transactions. There's a high proportion of renewable revenue there, too. But the transaction revenue moves the numbers around a bit as the flow fluctuates. In Moody's Analytics, you've got a recurring revenue base that's 95% and renews at roughly renewal rates in the mid-90s as well. So a very stable base of revenue that is growing. We've been producing ARR numbers around 10% now, sort of ratcheting up through the course of the last 5 years, maybe 7, 8, 9 and 10, over that period of time. And the other thing I'd say is margin expansion for Moody's Analytics has also been a part of the story in the last 5 years. If you were to go back in time, we were driving a lot of our product development effort and a lot of the growth was reliant on software development and projects to install software applications, sort of moving into the SaaS world, really 2016-ish, and you're starting to see the numbers shift; whereby the proportion of revenue has gone from, say, 70% recurring in MA back in 2016 to 95% recurring now. So we're very focused on subscription products and recurring revenue and SaaS capabilities in order to deliver better operating leverage.

Terrific. Appreciate that foundation here. When -- there are investors who are new to your story. What do you think they don't fully appreciate about Moody's in the same way that your most informed shareholders do?

I mean I think an important thing about the rating agency would be the notion that ratings are a part of the language of the bond market and very, very well entrenched. We have some fantastic network effects that Moody's Analytics supports and really is delivering on. The business started with ratings and research. And research was there to help investors understand ratings better. So investors would rely on ratings, and then that would drive the demand for issuers to come in the door and say, "Can you please rate my bond so I can sell them at a better price to these investors?" So there's some very tight synergies in terms of the branding and the way we structure our products. Virtually, everything I do is intended to support that concept. I think the thing about Moody's Analytics is it's -- and maybe it's Moody's in general, we're not just in the credit space. Our fastest-growing business right now is in the KYC or Know Your Customer or Know Your Supplier space, helping people with third-party risk management, really helping them understand, before I go into business with this person, before I do business with this person, is that going to be okay? And I can help them evaluate that discussion or that decision with a risk and an opportunity kind of perspective. I can talk about credit. We can talk about climate. We can talk about cyber. We can talk about financial crime and a whole bunch of other things that you might want to understand as you want to really understand that relationship holistically. Moody's can provide analytic models, databases and software tools to help you make those decisions. And so we're much more than your grandfather's rating agency.

So maybe shifting the conversation to more specifically focused on Moody's Analytics at this point. What has been possible for competitors or -- and maybe just really hard for competitors to replicate about what Moody's Analytics has put together over the years?

Yes. Okay. Yes, impossible to adjust, let's go with hard. Thanks. So there's some crown jewels that we should probably cover. I mean the obvious one is in Moody's Analytics, we have a big revenue base coming from the research, which is there to explain ratings and is really a byproduct of the ratings process. So there's a fantastic synergy there. Rating analysts do work. They create content. I monetize that content and help investors understand. Credit professionals generally understand, pretty straightforward, that one. But probably one of the biggest research businesses in the world and certainly in the financial space, people pay money for research, that's got to be one of the biggest. We have, I believe, an unparalleled, the best database on companies in the world. Call that database Orbis, by the way. This came through an acquisition we did back in 2017. It was a company called Bureau van Dijk that we bought. And with that came a database that covers literally, virtually every corporation in the world. So coverage there is above 470 million names. There's probably 300-something million that are actually going concerns today. We have history in there. We've got 40 million financial statements. I can talk about patents. I can even talk about vessels they use to get a sense for what's in their supply chain. This database is uniquely able to link corporate entities and corporate hierarchies together, so you understand who you're exposed to or you understand who you're doing business with literally throughout the corporate hierarchy. And there may be other companies out there that have good coverage. We have what we think is the best understanding of how these corporations relate. That's a crown jewel for the company. I have, in the commercial lending space, a software application many people might have heard of called CreditLens, which is one of these products that's sold to hundreds of banks around the globe, helping them do lending. And we sell research, data and analytic models to support that process. So in the banking space, we probably have, I don't know, 2,500, 3,000 customers. Hundreds and hundreds of them buy CreditLens, for example. And they use that as their initial point of thinking as they're bringing new customers on board and then as they're processing them through their regulatory requirements. In the insurance space, we have a fantastic capability that many of you probably heard of when we bought a company called RMS, which was a cat risk modeling company, maybe $300 million in revenue at the time. That is the world's best set of cat risk models, hundreds of them designed to help you evaluate literally. Some of these are sensitive enough to talk about 3 square meters and give you a sense for flood, fire, wind, all sorts of, call it, catastrophic perils and the impact on the building that might be situated on that particular geolocation. So we're able to leverage that to help insurance companies underwrite their policies, maybe price, maybe work with reinsurers, maybe understand how they may want to lay off some of the risks. And then you can imagine those same models can help bankers just the same and investors just the same. So if we can start to look at portfolios like at a bank, let's talk about the mortgages on the waterfront, on the west side of Florida and start to condition what might happen to my mortgage portfolio if a hurricane was a Category 4 instead of a Category 3. So we could go on. One more I should mention is this KYC franchise. We have what we think is the best mousetrap in this segment. We have a database on companies that's better than anybody. We have a database of politically exposed people, and we have a software application that leverages a bunch of machine learning tools that we've created over the years to help you make decisions more quickly. So that's our fastest growing business, and it's another jewel in the crown.

So as part of your answer to the last question, you talked about banking solutions and you talked about insurance solutions. But I think you guys have disclosed that corporate and government entities now represent 30% to Moody's Analytics ARR. How do you think about the market opportunity and your current penetration across the different client types?

So yes, so 2/3 of the revenue from Moody's Analytics is coming from financial services. And that's really the work of -- it's decades' worth of work in helping customers. You can imagine, we started with credit and have expanded to help people across various forms of risk and helping them across various domains. We get good growth from that sector. I would say we get better than our peers kind of growth from that sector. ARRs that are in the 8s, 9s and 10s, depending on which group you're talking about. I think insurance might have been north of 10% last year. In the last few years, especially in light of this acquisition or the series of acquisitions we've made to support the KYC franchise, we've developed some really strong capabilities that I think are relevant for financial services in helping us to expand those relationships, but also very relevant for nonfinancial companies and public sector accounts as well. So about 30% of the revenue is coming from -- this is sort of a loose collection of corporations and government entities. What I really mean is outside of our core financial services grouping, and that 30% has been growing. I think the -- in the earnings call, it was the last earnings call, we talked about a CAGR of 14% in that grouping. So we see good growth there, better-than-average growth coming from these places where we've been making investments. And we also see some good growth in financial services, of course. But that's an outsized growth that we're investing in.

So you guys have spoken about employing the land-and-expand strategy. On the land side of the equation, are you typically displacing competitors? Are you displacing internal solutions or provide an entirely new solution to your clients?

Yes. On the land side.

On the land side.

Yes. So we still land some customers in financial services, of course. But let's just talk about this new category of work. I would say the majority of new accounts we are landing are related to this KYC and Know Your Supplier kind of work. I'll give you an example. A tax authority that is interested in understanding -- Patrick, I'm sure you've got many LLCs associated with your name somewhere in your past. We want to make sure we understand all the revenue that you're generating from those LLCs rolls up to your personal tax return. Tax authority might want to trace you through or trace through to you all of those corporate relationships. That's a good example of us landing a new account that actually we probably didn't displace anybody. Now there's other examples where there might be an incumbent that has a data product that could be replaceable. Usually, when we win business, it's because we're selling data, an analytic tool of some sort, a model that might help you make a decision. Could be as simple as a credit scoring model, it could be as rich as a KYC tool, and then some software and workflow capability to bring that solution stack to bear and solve a problem for you that's vertically integrated, really adding value because you can transform your business when you're doing it that way. So we're helping them be -- really behave differently and think differently about the decisions they're making and do things more efficiently but actually more effectively as well, make better decisions along with faster decisions. So sometimes we're displacing. Sometimes it's land. I would say that we're excited to land more accounts in these new sectors.

And then as we move on to the expand side of the equation, kind of what's a typical progression that you would work your way through?

Yes.

Is it a typical progression?

So I mean if I drew it up on the board, let's go to an account in Asia, right? I'd want to help the -- let's go to the bank, right? So most of our expansion work -- and by the way, we talked about a net expansion rate on the last earnings call for financial services. I think the number there, we rounded up to 109%. So that net expansion rate, there's a lot like an NRR number or an NDR number, really. This book of accounts is growing at about 108% of what we started the year with. A typical -- if you drew it up on the board, it would be land an account with a training service that helps them learn more about credit because they're developing and growing in their sophistication levels, often move into a lending software application to maybe streamline their lending operation, maybe add some more enterprise risk management software along the way. As they become more sophisticated and maybe are exposed to larger names or a more global footprint, research becomes more relevant because the rated universe is now at play. You can imagine along the way, we're cross-selling in data from Orbis to help provide data on those potential loans they're trying to make or maybe names they're trying to monitor. So we've got our database to feed and then credit scoring models to assess, evaluate and score. And then we've got software to remember why we decided what we did. You approved my loan. We know that I proposed it. We know that you approved it. And then ideally, in about 6 months, maybe even less than that, I'll be selling you my generative AI tool to help you write your credit memo, to help me write the credit memo to explain to you, my boss, that this is the right loan for us to make. So you can see that sort of trajectory. I think that would be the way you draw it up. In real life, people's buying decisions are often made a little bit further along. We need to replace our software today. And then you say, "Oh, gosh, you've got data too, let's bring that in." Sometimes we start with the training, sometimes the training comes in later. The point is that vertically integrated solution stack enables you to traverse that and go to where the customers want you to be.

So you brought up GenAI, so I'll go in that direction. What's your level of conviction in the generative AI opportunity for Moody's? And are you confident that initiatives such as Moody's Research Assistant will be high ROI investments? Or is it more in the category of, I want to make sure I'm not wrong in case AI really does become a big thing?

Cover your bases, yes. I would say I am -- I need an adjective that would be extreme. I am very convicted, you could say wildly convicted, dramatically convicted, something like this. I am very much of the belief that we are dealing with a generational opportunity to enhance the way knowledge workers do their jobs. We, as a knowledge company, are seeing it internally. This is just the beginning of what I think is going to be a very important wave. And I think the people in this room will all be officially users of generative AI tools soon. And I think the adoption curve that we see is going to be compressed compared to other adoption curves that we've seen in the past. We're making investments here. We talked about this on the earnings call. We're making investments in platform engineering to foster more interoperability and especially related to GenAI tools. We are building tools now that I can start to assemble into different product families and reuse the code. We are architecting these things so we can go to various cloud providers or to various model providers. So as the news that comes out on the next model suggests that we should experiment and test, we're doing that. And we're able to see right now, this model is the way to go. It's the best source of the ability to generate new insight and analysis in a way that's relevant for us. So we are investing quite, I think, quite intentionally internally and externally. And the beauty of this is there's synergy in these tools when you're a rating agency and when you're a knowledge company. Everything I do for internal use, I can probably use, right? That same code I can use for my products because you're doing the same kind of thing. Think about it. Everybody assembles data. They analyze data to figure out what's most important. They use scoring and benchmarking tools. And then they remember what they did. I mean that's what the rating agency does. It's what I do all day. That's what you do all day. And we're building tools to make that go faster.

So as you're building these tools in KYC and generative AI, Moody's capital intensity has increased in the most recent few years. Is that a short-term cyclical, hey, we've really go after these investments and then we can probably pull back? Or is there something more structural about the capital intensity needs of Moody's Analytics?

Yes. I mean capital intensive, I think that phrase usually implies a different kind of industry in some respects. I mean the capitalization dynamics, I think, are a little different when you're investing in and building hosted software applications. I think that's the key insight here that you're asking me about. When we were building products or enhancing products that were established in, I'll call it, previous technology eras, your capitalization requirement were not the same. Your GAAP requirements are not the same. So when you build and invest in a capability that is hosted software application that you own and then you lease, you're really required to account for your expenses with that capitalization strategy or capitalization approach, I should say. So that is what's happening here. You're seeing more and more of our spend on the operating -- basically in terms of product development and product build occurring in places where you're required to do the capitalization work from a GAAP perspective. So I think that's right. So it is a bit structural rather than cyclical because I don't expect us -- I do expect, I should say, that we will be doing more investments in that space where I'm building the code using a code base that I can build once and then leveraging it through leasing it out through SaaS platforms, right, that can be reused. So I expect the capitalization dynamics to be consistent or consistent with this approach in the future.

So I suppose the conversation about capital intensity for folks in this room, I think a lot of it comes down to free cash flow. What is the free cash flow that when is this able to generate and then deploy for additional investments or return to shareholders, et cetera? So the increased capital intensity as we kind of defined it from Moody's Analytics going forward, does that change the free cash flow profile of the company?

I mean I would say in light of the capitalization treatment, your free cash flow profile adjusts. But I wouldn't expect that, that profile would change dramatically over time going forward, right? You would expect these ratios to be about the same. Virtually, all of our investment these days is being made in that space where that kind of treatment is relevant, at least all the big investments. From a free cash flow perspective, at the MCO level, MIS, I think, is where -- the rating agency is where you see the big moves because of the way the flows and the variability in the bond issuance dynamics were has such great operating leverage coming from the rating agency. When things go, operating leverage really kicks up, and you see big moves in free cash flow.

Yes. It's a good point.

Yes. Yes.

So maybe taking a step back, just as you guys think about product development and innovation, what's the process of Moody's? Working on gen AI, for example, is this something where your customers came to you and said, this technology is out there. Here's some really great tools we think you could build? Is it more internally sourced? How does that work?

Yes. A little bit of both. I mean I think the key to our product development process is we sort of require that we have 4 parties involved with a product development project. So I have a salesperson or a sales representative who understands how we might commercialize these things, a product person who can help think about resourcing and think about that commercialization. Engineering person is actually helping me with the actual product itself, actually writing the code kind of customer. So the quartet there keeps me honest, right? So I often sort of move with the flow of customer sentiment and latch on to something that looks like it's getting some traction or there's a need there that we can serve that maybe nobody else can. And then I get my quadrangle of talent in the room, and we iterate around the MVP and then iterate around the enhancements and then we adjust along the way. Sometimes we start here, we end up over here. But we end up over here because there's momentum pulling us or there's, I don't know, what is it? It's gravity pulling us in that direction. We had several examples of this. But the gen AI tool is a great -- Research Assistant is a great example of that. And we took a prototype and went out as fast as we could. We had 40 people in preview. They promised they'd give us feedback. We met with them multiple times, literally every week got feedback, adjusted, added. I can tell you what the next 3 features are that are required in order to deliver value on that thing. And we've got them road mapped, and we'll continue to do this as we go through this. And that's, I think, a common way we build products. So I'm not doing too much. Somebody asked me a question earlier today about build it and they will come. It's not really that. It's more start with a prototype. I haven't made a giant investment yet, iterate around that, get feedback, go, and then I'll adjust along the way. And it feels like 40 people are actually giving me feedback and they're actually engaging, I'm probably on to something, right?

So speaking of people giving you feedback and, I guess, at a different level, you've done a lot of investor meetings today and in recent months. What do investors say they want for Moody's at this point? What do they want Moody's to really maximize for?

You're talking about MCO, right? That's a product development, we're talking about MCO.

Yes.

Yes. I mean I think you've got a pretty interesting dynamic in that the people who bought Moody's 10 and 15 years ago bought it because of the rating agency. And we have [ spun out ], it's all one today. Somebody bought our stock in 2009, and they're the happiest people who ever lived. And it's a nice run. So the idea of being involved with a business as rich with network effects as the rating agency is something that's very attractive. And I think people believe in the thesis that the bond market and debt financing will probably continue and probably expand because it's just more efficient for everybody. Capital markets is a way in which capital flows most efficiently, and so they buy. I think that, especially recently, people see the renewable and the ARR growth numbers that MA is putting up there with expanding margins over the last few years. We've been generating margin expansion I think the last 5, we mentioned at the beginning. So they like that story, this Rule of 40 kind of story. And I've got investors that are maybe the older school, very aware of free cash flow, really like the idea of dividends really. And then you've got some investors that want this compounding machine to keep going with these organic investments because, gosh, the compounding is good. I mean the beauty is you have a business that has fantastic operating leverage that can feed some of these organic investments in a way that I think is quite valuable in terms of generating value for shareholders. So I think you got an interesting combo here that I think more and more people are beginning to appreciate.

Let me pause there and see if there are any questions in the room at this point. All right. I don't see any.

It's amazing how bashful are they.

It's afternoon, everybody is still enjoying their lunch. Maybe I'll just finish with another question or 2. So you spoke about how margins in Moody's Analytics have moved up a lot higher over the last several years, and then you have your medium-term goal of mid-30s. What is the path to get to that mid-30s? And I know I heard Shivani say this is not linear. So kind of how do you think about the trajectory of margins going forward?

Yes. I mean we're making investments now in places where we think we're going to make the strategy come true, right? So when you have products that deliver, think of it as like a juggernaut of renewable growth, right, especially those that are delivered in high-margin or higher-margin platforms like SaaS platforms. You have the opportunity to generate high-quality growth that gives you a little bit more room to invest in your business and continue with this product development machine that we've built up over the last couple of years, continue to invest in the sales force like we have over the last couple of years and still have some room for margin expansion. Now in 2024, we see 3 time-sensitive opportunities to invest in the business that we think will drive more of that high-quality, high operating leverage kind of product sales. And we're making those investments now. So I talked about platform engineering, I talked about generative AI. Beside the landing and expanding these new customer sets, we think all of those give us growth rates that afford investments in the business and still the ability to generate some good margins, margin expansion.

Shivani?

Shivani?

[indiscernible]

Most excited about. That's like a planned question. It's pretty good. What am I most excited about? We got this question a couple of times in London. We did an innovation open house in London last week. I mean right now, I am extremely interested and excited about this GenAI opportunity. And in particular, the ability to comingle and create a corpus of content that is not just Moody's content, not just the models that Moody's brings to the table or the datasets that Moody's brings to the table or the research, again, on all these different perspective, credit climate, cyber, but also yours. So I'll have to show this to Patrick so you can see this and you can believe me. We're right now rolling out a capability internally to build your own GPT tool, right? Just leveraging classic kind of generic Copilot capabilities. I've got Moody's content available, and I can entitle the GenAI prompt with that. I could pull in, like put it on your machine or make it available on your machine. We can pull in the last 150 research documents you wrote. And I can say, in the voice of Patrick, write an investment research report on Moody's. And while you're at it, evaluate that in light of Moody's research on those -- on that company, right? So we're at the point now where this isn't just a computer trick, right? And we're able to engage by understanding the intent behind your prompt and then direct into places where we can pull in calculation engines. We're actually doing experiments now where we're doing calculations behind the scenes. We're generating research reports. We're generating credit memos for people, but I'm also able to condition portfolios or condition models to actually assess impact on portfolios. So we're at a next inflection point here where the ability to combine and pull agents in is getting very rich and very real. So I think that this -- we're -- this curve, this product life cycle for GenAI is starting to ramp up, and I am very excited about that. You can imagine what that can do for Moody's internally, right? If I'm a steel analyst, and I monitor these 5 publications and I look at these other 6 indicators, synthesize that for me every morning and let me know what's flashing, right? This is a pretty cool concept.

Okay. Cool. Well, before I get supplanted by AI, I think we should wrap it up. But thank you guys for all joining us this afternoon. Thank you, Steve.

Thanks, Patrick. Appreciate it.