Okay, good morning, and welcome. I'm George Tong. I'm the business services analyst at Goldman, and I'm really pleased to be joined by Steve Tulenko, President of Moody's Analytics.
Hi, everybody.
Steve, thank you for joining us here today.
Hi, everybody. Morning.
So let's start with the high-level evolution of Moody's. Moody's right now is much more than a rating agency. Could you provide an overview of Moody's today versus the pure-play rating agency-
Mm.
that the company was a couple of years ago?
Yeah. Sure. So, fair disclosure here, I started with Moody's in 1990 in a little group at the time, which was called the marketing group within the rating agency. We generated something like $10 million worth of revenue at the time. That group ended up becoming Moody's Analytics in 2008. It was about $500-$600 million worth of revenue at the time. And we formed, before the crisis, actually, we decided in 2007 to create a unit that would be focused on everything but the actual ratings process. So at that time, I would say, revenue for the rating agency was two or three times revenue for Moody's Analytics.
Mm-hmm.
Since then, you know, we've doubled or tripled the revenue base. Last year, I think MA produced more revenue overall, a little bit more than half the revenue overall for the business, for the Moody's Corporation. And this year, with the rating agency delivering the growth that you're seeing, we're not quite keeping up with that.
Right.
with that same dynamic. But, you know, what we've got in MA now is a, you know, a juggernaut of recurring revenue, a juggernaut of ARR growth.
Mm-hmm.
When you throw 10% at a number for long enough, it compounds its way up to north of $3 billion.
Yep.
Roughly half the revenue comes from helping customers, especially in the banking world and the insurance world, the asset management world, so financial services customers understand risks and often the corollary of picking out, you know, which risk is best in terms of the opportunities that they face or they see ahead of them.
Yep.
We've had a lot of really good growth in the corporate and government sectors, too. So our KYC franchise, in particular, helping people know who they're doing business with, has generated some really good growth rates for us. We're really happy about that.
Mm-hmm.
Maybe the other note is that, in 2017, we acquired a company that was a little less known in the U.S. called Bureau van Dijk.
Right.
It was a Belgian company, which was the curator of a tremendous database on corporations.
Mm.
We now have what we think of as the richest and deepest database on corporations in the world. We cover something like five hundred and twenty-five million names, and can tell you, at the very least, that that company is a going concern, that they've registered with the authorities. We can often tell you about the financial performance of the firm, and we cover much more than just public companies. We cover literally millions and hundreds of millions
Mm
Of private companies as well. So that gives us an asset to work with that creates a foundation that really dramatically changes our ability to add value by bringing analytics to the table as you think about analyzing other companies.
Mm-hmm. Yeah. You mentioned the vast real estate of data that came with BVD. For those who are new to the stock, can you talk a little bit more about the details and extent of the variety of data that you have within Moody's Analytics?
Yeah. Okay, so you could sum it up maybe this way, and thanks for that slide. That should just be helpful. So first of all, you can imagine we have a lot of information that we can offer on bonds, right? We've been rating bonds for a hundred years, more than a hundred years. All of the details related to the issuers of those bonds, literally for history of those since we've been in the business, you can capture and understand and use to analyze, especially credit.
Mm.
That's an important part. We rate something over $70 trillion worth of debt, and of course, have a tremendous position in that space. Company information in general, not just the rated tiers, which is about 10,000, maybe 15,000 rated companies. Of course, there's another, say, 45,000 or 50,000 listed names, and then our coverage expands well beyond that to that 525 million company mark. That is extensive. I think we have 6,000 columns that you could actually look at in the database, just to give you a sense.
Mm.
Of course, we don't populate every one of them on every name. The bigger, the more coverage we have. But we have tens of millions of financial statements, for example.
Mm
on corporations. We track patents.
Mm-hmm.
We track shipping containers to help you think about supply chain risk, right? So the entities that you might track-
Mm
... whether they're companies or shipping containers, we help to keep track of. We have a bunch of information on cyber risk.
Mm-hmm.
We have an affiliate that we've invested in called BitSight. It's one of the number one or number two cyber scoring companies in the world. So all of the data that they make available to customers, we can also incorporate into our analytics. Think about a name. If you look at a company and you're interested to see more and learn more about them, we have analytic tools we can hang off of that in order to analyze and look at them from different perspectives. So you've got coverage on cyber risk, coverage on climate-
Mm-hmm
... especially what climate might do to a physical property, right? So we cover literally tens of millions of properties where we have varying levels of detail, but basically, we can tell you, for any important commercial real estate building, what the level is in terms of their susceptibility to flood, you know, what's the base flood elevation in that area, what's the minimum floor height on that building. We talk about the roof. We talk about the construction.
Mm-hmm.
We talk about what the leases are and what the operating income is generated in that building. So, tens of millions of properties. Economic statistics, right? We've been in the business of forecasting the macroeconomic statistics and regional economic statistics for decades.
Mm-hmm.
So you can see there's a vast data estate. I would think of it as, what are the things that people who run companies or who are buying and selling securities or making loans, what do they want to know about in order to refine their understanding of who they do business with?
Mm-hmm.
So, in addition to, is that company registered in Austria? I might wanna understand the beneficial ownership that reaches up into a Russian oligarch. I might wanna know who else they might be affiliated with in France, and then I might wanna study that company just to make sure I understand the reputation risk.
Mm.
I can dive in and examine any of these things you see on the screen with respect to any of those names and bring those together as I'm thinking about my investment, my loan, my insurance policy. But also, you know, can I do business with this company at all? Because you never know, they might actually be sanctioned by some entity that I better be aware of?
Mm-hmm.
So we're bringing that together so that people can basically understand who they're doing business with.
Yep.
Yep.
Now, how does your, your vast data estate translate into financial performance?
Yeah, so I would say our product strategy is a combination of good quality data with really good coverage on those classes of information we talked about before. Expertise to help you analyze, usually, often models, right? So you can dimension risks and understand uncertainty with numbers, right? So let's use a stochastic, you know, tool to estimate what the losses might be due to, you know, this actuarial assumption. You've got data with models and then software, workflow tools, to kind of bring these things together to help people do their job, to do lending or to do whatever it is, and what you see on the screen are the numbers we've been putting up recently, so our ARR numbers in the last, I think seven quarters in a row, we've either rounded up or rounded down to 10%.
Mm-hmm.
I think that's, that's quite an accomplishment. That's a big change from where we were maybe three or maybe four or five years ago. We've shifted almost exclusively to recurring revenue-oriented products, so, very little project-oriented work. 95% of the revenue is recurring. Retention rates are in the low- to mid-90s. We've been running a 94% retention rate, you know, 93.8% or 94.2%.
Mm-hmm
... literally, for a couple of years now straight. And our net expansion rate or net dollar retention number is looking like 108, you know, literally several quarters in a row as well. So lots of consistency in that, high single, low teens kinds of numbers.
Right.
Yeah.
You've made a lot of progress with GenAI, and you're often seen as the first mover.
Yeah
In implementing GenAI in the space. Why do you think Moody's is well-positioned to win in GenAI?
Yeah.
Could you share a little bit about your approach and what you've done so far?
Yeah. Yeah. I mean, number one, we believe strongly that the concepts of the LLM, right, the ability to synthesize information and draw inference and really assist people in doing analysis is going to be a pretty big deal, right? So the orientation toward GenAI has been, you know, let's dive in and get into- let's not just dip our toe in this-
Mm.
Let's dive in.
Mm-hmm.
Right? So, we have created a suite of tools that we made avail [audio distortion] in GenAI RAG
Mm.
Orientation. That, I think, is becoming almost table stakes, and it certainly will be soon, and we're incorporating that in virtually every product.
Yep.
Right? Lots of products... anything you all would have heard of, probably. Agentic work, I think, is the next phase. I don't know if it's the next generation, but certainly next phase, and we're investing quite a bit there. We have experiments and tools that we've created, certainly for internal use. I've got one tool where we fire up thirty or forty different agents and let them go after a problem, where we dive in to try to really understand what drives the financial performance behind a firm, or what factors might affect the financial performance behind a firm, and then dive in on what's driving those. Mm. So the nice thing about agents is they don't ask for raises, and they don't get upset when they work too hard.
So, that's I think the next thing you'll see is us spending a lot of time, and there's lots of opportunities to leverage maybe agents so that copilots become coworkers. I think that might be the way to think of it. And, you know, we are a company of knowledge workers, and in this space, we think we need to be a leading thought leader in this space in order to help our customers, who are all knowledge workers as well. Yep. The Model Garden is a good example of if you can make available, and we have hundreds of models that we sell to customers, if you can make them available in a manner that is discoverable to the agent or to the GenAI tools, right?
If that discoverability is easy, and you can understand the intent behind the prompt, so you can pull the right model at the right time. Mm. You can really help people do work much more quickly. Right. Right? What is the impact of this? Well, gosh, which model should I engage to evaluate that question, right? We have. We're doing a lot of work behind the scenes to engineer what we meant by the question, and then pick the right model from the Model Garden. Right. Yeah. You've formed partnerships with Google and Microsoft. Yeah. In your work with AI. Yeah. Can you talk a little bit more about these partnerships. Yeah. And then what you're doing with them? Yeah, sure. So big portion. A big part of what we're doing here is just diving in, right?
I mean, what better way to dive in, in the AI space than to work with Microsoft and Google? Diving in is important. I would say, in the Microsoft arrangement, we are very focused on demonstrating resiliency and robust controls to customers, and then leveraging their OpenAI Azure service as a foundational part of many of the tools that we use. In the Google space, we're doing a little bit more experimental work. We're testing out some small language model activity, and then that's often in a very applied sense. You can imagine reading technical documents. This is something Moody's has to do a lot, and I'll bet you, you do, too. Reading technical documents and getting to the footnotes, and what do the footnotes really mean?
So leveraging these small language model techniques to really establish a good understanding of the footnotes in financial statements as opposed to the footnotes in a doctor's brief or a lawyer's brief. So that's the sort of thing we're experimenting with. Google, we've talked a lot about grounding the results as well. So these are all, I think partnerships that are setting us up for a combination of innovation and resiliency, right? Because we're working with some of the best players in the business. Right. Right. You've been in the market nine months now with the Gen and GenAI-enabled product. Can you share some of the learnings from being in the market and what some of the customer feedback has been? Yeah. Okay, so, let's see. Learning number one, the usage patterns are really quite interesting.
You know, we can benchmark usage of our CreditView product. You've probably heard of CreditView. That's our flagship research product, where we commercialize the content that comes from the rating agency. As they rate bonds and rates, rate companies, we grab that content, grab those opinions, grab those scorecards, grab those methodologies, apply them in a GenAI model. So you can ask the question, what would Moody's say about this? What we find is, for CreditView users that adopt the Research Assistant, that their usage goes up by about 60%. Right? And what's really interesting is it's not just 60% in terms of interactions in total, but there's an increase in the usage of the actual research that they used to buy before. So we're seeing much more activity.
We're capturing more time and sort of capturing more intellectual shelf space with our customers, which we're happy about. But they're actually reading the research more or they're tapping the methodology more which is really quite comforting. So these tools are enhancing the quality of the interactions quite a bit. Those usage stats are impressive, right? And, and the other thing is it, it's opening up. You know, you've got early adopters in the firm. What's great about these GenAI tools is the cross-selling dynamic can be quite rich. So we've structured, you can imagine it's Moody's, we, we make available anything that we have available through that GenAI facility, you prompt, "What would Moody's say about Goldman Sachs? The news today," right? We can bring in the news today.
We can say, "Here's what Moody's has said about Goldman Sachs in the past. How does it compare to other companies?" And then we might also say, "And in light of interest rates, we think Goldman Sachs is going to do X," right? "Or we expect that would have this kind of impact." And then we have a little citation that says, "Click here for the citation." If you purchased coverage from us on our economic coverage of interest rates, you can see that. We've incorporated it into the result. But if you haven't purchased that, you can click there and maybe introduce yourself to some other content sets you haven't seen before. So we're opening the aperture through the usage and introducing cross-selling dynamics in context.
So GenAI is a great cross-selling tool in that respect for us, and that's one of the things we're pretty excited about. The other big lesson I'd say is, I'd love to get a show of hands here to see who has access to GenAI tools, but I'll wait. Adoption is driven by individuals. I think this is a lot like any technology curve. You've got early adopters who kind of dive in and pull their companies toward this new technology. Then the larger the institution, the more important the compliance and resiliency and risk management dynamics become.
Mm-hmm.
So it's interesting, we were talking about a story this morning, relatively large company. They want to buy our product, but in order to process this through their compliance regime, they need to establish a GenAI governance authority inside their company to bless it. So this is a classic which comes first kind of dynamic, right? Why would I have a GenAI authority to bless the use of a product if I haven't bought the product before? I need to buy the product, but I have to get blessed. So this is a pretty rich, I would say, interesting dynamic in the early part of the adoption curve.
Mm-hmm.
The bigger the institution, the more important that is. The smaller the institution, the more the people in charge can make the decision on the spot, and we're working through that right now. We in fact are helping people set up those governance authorities inside their firms because we as a rating agency need to do that, right? So we have experience with this and can offer some suggestions on how they might jumpstart their own GenAI programs internally.
Right.
That's an interesting lesson as well.
Mm-hmm.
Yeah.
What is the increased engagement from customers and the increased opportunity for a cross-sell mean for future growth and-
Yeah
-these analytics?
Yeah. So what we're doing is, we've got a series of products we're offering commercially. So, you know, think of this as a premium on top of what you already buy. If you spend $100, you might spend $130 if you want to have access to the GenAI capabilities, right? And that's a typical kind of pricing model at the moment.
Mm-hmm.
We expect we'll be exploring some more consumption-based pricing systems in the future. But these are not common at this point, and we don't really have... I'd say we're focused on the biggest consumers of our content right now, so they would prefer a subscription that gives them kind of an all-you-can-eat approach.
Mm-hmm.
So we're sort of working with them first in that respect. But, I think this will be additive to our growth rates in the future.
Mm.
I think you'll see as we move through the sales cycles and sales motions that we normally experience, you'll see more and more contributions to the numbers like the ARR number.
Yep.
Yeah.
Let's talk a little bit more about some of the recent trends, including ARR.
Yeah.
With your 2Q results, Moody's widened its ARR guidance-
Right
-range because of uncertainty in the second half of the year that that's expected. Can you talk about what you're seeing in the marketplace that's prompting this updated view to the guidance?
Yeah. Yeah, thank you for using the word widen. Lots of people ask me, "Why did you lower the guidance?" Okay, we widened this range. It's a wider range. That's usually code for, "Okay, let's ask some questions," right?
Mm.
There's three things, three, four things that are driving our acknowledgment, right, through the guidance, right?
Mm.
And I would say any one of these things individually would be immaterial and wouldn't have been enough to trigger this, but they're all sort of happening at the same time.
Mm.
So you've got one, this deal we did with MSCI-
Mm-hmm
... which is a tremendous opportunity for both us and MSCI. And I can talk more about that, but let me just-
Yeah
... hit your question. We are essentially using the scores and the datasets from MSCI to replace what we were offering in the ESG space. By the way, it's not all of our climate and physical risk work. I'm just talking about ESG scores.
Mm.
So we're no longer reproducing those directly. We're licensing that content from MSCI and making it available to our customers so that they can incorporate it into their lending analysis, their insurance policy analysis, their asset, whatever it might be. So that in itself replaces a revenue stream, right?
Mm.
And, you know, there are some customers that were buying both, right? And they just aren't going to need both-
Mm
... well, they won't be able to get both because we aren't offering ours anymore.
Right.
That's an impact.
Right.
Second, second thing we've talked about is, and I would imagine there's a lot of people here in the asset management space. I would say there's been pressure, especially on margins there, and we are seeing a little bit of that, right? So some of the cost controls in asset management are. We're noticing.
Mm.
And then in the banking sector, I would say that the... You know, coming out of what, what should we call it? Stress in the banking sector last year, coming out of that stress has taken a little longer than we expected.
Mm.
Now, we did better growth in the first half in banking than we did in the prior first half.
Mm-hmm.
And we see much better pipelines in the second half than we did last year, but it's still a little slower than we'd like or than we expected. And then the third thing, there's some currency stuff going on.
Mm.
You just have to keep track of that. The fourth thing is... Gosh, what's the fourth thing? Ah, of course. Just the election. Yeah. The election usually doesn't dramatically affect us, right? It doesn't really matter what parties and what administration it is. But there are a few contracts out there that are owned by people in the U.S. government who I think are a little bit more politically driven.
Mm.
If the wind blows in a certain direction, those contracts will go well. If it blows in a different direction, those contracts might not... and if you take all of those things combined, we thought, "You know what? We better widen the range.
Right. Right.
Right? It's those things altogether.
Right. In the second quarter, if you look at within MA, the R esearch and Insights growth of ARR-
Sorry, one more note, just for everybody here in case they haven't heard.
Please, go ahead. Yep.
We widened the range, but we did say our midpoint on our best guess on the estimate was the high end of high single.
Mm-hmm.
We did say that.
Mm-hmm.
That's just worth noting. So we're widening the range, but we've maybe adjusted the midpoint-
Right
... just a little bit.
Right. Good call.
Sorry about that.
Yep. No, no, no worries. So sticking with the recent trends, if you look at Research and Insights, as of 2Q, the growth was around 6%, and that's below the double-digit growth that you're seeing in Decision Solutions-
Yeah
... and Data and Information.
Yeah.
Can you talk a little bit about the trend that you're seeing within just R&I, and when you would expect ARR growth there to improve?
Yeah
... towards that double-digit range?
Yeah, so, a big chunk of the... You can imagine, Research and Insights is mostly asset management and mostly banking.
Mm-hmm. Mm-hmm.
Right? Virtually any wholesale lender is buying here, and any asset manager that buys bonds is buying here. So that's the place where that segment-oriented pressure is being felt.
Mm-hmm.
That's number one. Number two, we do have two pretty innovative products in market here in the research business especially.
Mm-hmm.
Right? Which is the majority of the money in that segment. So the Research Assistant tool is in that space, and then-
Mm-hmm
... the coverage expansion, which is really a private credit-oriented release, has happened there as well. So we're expecting to see some good take-up with those through the second half of this year. You should see something in the ARR number, when I think we've talked about this. You'll see some movement in the ARR number as we move through the second half of this year, especially because of those product launches.
Got it.
Yeah.
That's helpful.
Yep.
Now, Steve, in addition to running analytics, you're very well-versed with the MIS business.
Right
... the ratings business.
Yeah.
Can you talk a little bit about your latest expectations for global debt issuance trends?
Hmm. Okay, so probably not as well-versed as some of my colleagues. I think that I think the most insightful thing to say about this is to look at the sentiment related to issuance from the perspective of a treasurer or a CFO.
Mm-hmm.
I think that's the thing that if you're trying to project what's going to happen with issuance, you need to get in the mind of the CFO and the treasurer and try to sit with them and decide, "When is the time in which I would want best to tap the market?", and sometimes that's an environment when rates are a little higher, but they're stable and predictable.
Mm-hmm.
Sometimes it's because it's opportunistic that they've hit lower points, right? So I think that's a really useful way to kind of think about it.
Mm-hmm.
You know, capital expenditure expectations, when do I need to borrow, when is it opportunistic to borrow, right, are all important considerations there. We're gonna publish our Refinancing Wall study, I think. Gosh, when does that come out?
October?
Yeah. So that's another big tool to help project what those trends look like.
Mm-hmm.
That's probably, I think, an important refresh on expectations and guidance for the future. I think the first half was very strong. Our second half, I think, is expected to be... Yeah? We got it at high teens.
Right.
Yeah, high teens revenue in the second.
Yep, yep. Now,
That's after a thirty-something in the first or the second quarter, I think.
Very significant.
Yeah.
Yeah. The first half of twenty twenty-four benefited from some pull forward of debt issuance activity. Are you concerned at all that this could create a void in growth or a pothole in growth, either in the second half of this year-
Yeah
... for debt issuance or in 2025 for debt issuance?
Yeah. I mean, again, I think the sentiment talk that we were just covering there-
Mm-hmm
... is the way to try to address that. You know, pothole, air pocket, are dynamics I think that, you know, can often happen and are temporary.
Mm.
Right? In the long run, do you expect that people will be leveraging debt financing as a way of financing their, their company? Is it still going to be... Do you expect it to be the best way for them to do that? Is it a, an efficient way for them to actually tap capital markets? I think the, the bet on that in the long run is yes. Disintermediation, I think, continues-
Mm
... and, global, especially global disintermediation continues over the course of the long run, right? So the investment thesis, I think, is intact here, no matter what happens in any one quarter.
Mm-hmm.
We've had a nice run in the last few, and, despite a rising interest rate environment, maybe you have expectations that might be reversing, that should usually put upward pressure on issuance, you know, in general.
Mm-hmm.
But I think the themes around sentiment for the issuer, for the treasurer, is really the way to think about how that you should be thinking about as you think about how you might project what those markets look.
Yep, makes sense. Let's talk about margins.
Yeah.
So if we start with MA margins, what's your strategy in balancing investments you're making into GenAI, into products, with delivering margin expansion, and, and what would you say is your medium-term target for MA margins?
Yeah. So thank you. So just a little bit of history, in the last five years, we've gone, you know, moved ARR numbers up from, say, sevens up to tens.
Hmm.
Margins have come up by 400 basis points or so over that same period of time. So there's been a history of ratcheting these things up. We've bought a couple companies along the way. They tend to be a little lower in the margin category, so they've been dilutive, and we managed to generate, you know, a reported number that's still increasing the margin. I think the GenAI investments, and some of those are data and data interoperability investments, some of those are innovation investments in terms of how do you create these agents, for example, some platform engineering investments as well have been, you know, things that we've talked about in the past, and I think we continue to invest in earnest.
All three of those often require a surge, and then you sort of level off-
Mm.
We think, we hope. Certainly, certainly the GenAI thing, we'll have to see how that goes. There may be continued investment there, but you've got a surge and then a leveling off. So we expect a ramp-up in margins over the medium term. We've talked about getting to mid-thirties.
Mm-hmm.
We've said that's probably not gonna be a linear path in light of some of these big surges of investment that we're making now.
Mm.
Now, we're redeploying all the time. You know, let's turn down the activity on this thing that maybe doesn't have the same kind of growth prospects and turn up this thing that does. KYC is a great example of that.
Mm.
where we've had really good track record of growth, and we're continuing to invest, but yeah, the mid-thirties number is where we're aiming for over the medium term.
Got it, and then I guess similarly within the MIS segment, from a margin perspective, how much further upside would you say there is with operating margins beyond 60%, perhaps, with benefits coming in from GenAI and other productivity-
Yeah
-initiatives?
Okay. So I think last quarter we were north of 60% in MIS, in terms of margins. You had some really strong operating leverage. I'm sure everyone's, you know, more familiar with maybe the ratings business, but you have tremendous operating leverage here. So when issuance goes, right, you see margins going up at a very significant rate. Issuance slows down, you might see the margins come in a little bit.
Mm.
But you know, the goal and the expectation over the medium term is to be in the low sixties sustainably.
Mm.
Right? And that's, I think really due to two major programs. There's a big technology program that we have undertaken in the rating agency to streamline the processing of ratings, streamline the way in which we make rating decisions so that the workflow can be done more effectively, and so that we can save some time and some money there. And then GenAI tools also help you do homework on companies.
Mm.
So we are starting to incorporate some of these models into the ratings business, and we expect to see productivity gains there as well. Both of those things are in effect. I think the technology projects are substantial gonna make a substantial difference for us. GenAI, you sort of hope this works out well, and I think just like we see our customers hoping that they can get some benefit, we're hoping we can get some benefit internally also.
Right. Makes sense. Well, Steve, we're just about out of time. Thank you for the incredible insights and time.
Thank you, George.
Please join me in thanking Steve.
Thanks very much. Thanks, everybody. All right, George, thanks very much.