MSCI Inc. (MSCI) Earnings Call Transcript & Summary

I'm Ashish Sabadra, and I cover info services companies here at RBC. We are excited to host Jorge, Head of Analytics at MSCI thanks again for giving us this opportunity.

Thank you, Ashish. Great to be here.

I'll kick off the conversation with a question on GenAI because that has been the most topical question across our universe and at the tech conference, how do you think about the GenAI? One of the questions that we get is, obviously, the disintermediation risk. But then if you can also talk about the proprietary data, how deeply embedded you are within the workflows? And as well as talk about the monetization opportunity. So we'll first go with the top line and then we may talk about the efficiency part.

Yes. So we're very excited about GenAI in general, for both of those reasons. We think it will make us way more efficient than we are today and then have to talk about the things that we're doing in that regard. But we also think there's opportunities to expand ways in which our clients are using it, develop new products. And so lots and lots of opportunities that we see going forward. So let me talk about internally first, how we're doing it because I think that's something that we've been doing for a very long time. Generally using standard leading products and embedding them in every part of what we do, certainly in product development, our engineers are using it to code, things like Replit and Cursor and Copilot -- GitHub copilot, all of these things, using it for building new code, testing, quality control of the data, sourcing data faster, which is a big part of our business. We run a big data factory at MSCI. And we've seen a lot of gains from doing that, and there's still more to be done in that space. We've been factoring all code using AI, seen 50%, 60% gains in the time that it would take to do that work. So it's quite remarkable what you can achieve. But then we're also doing it across the firm. So in the client coverage organization, for example, we analyze client nodes using AI. We put all this intelligence at the fingertips of our account managers. Hopefully, that improves the client experience and the service that they provide our clients. So we're seeing a lot of those benefits internally. And then in terms of clients using our products, right, we have developed already a handful of products that use AI in very deep ways. We just released the second version of what we call AI insights. The first one was released a while ago, but it's now multi-asset class. And what these does is -- it allows our clients to ask questions about their portfolios, right, using natural language. So a client can, for example, say, "Hey, can you give me a line chart of the tracking error of portfolio XYZ over the last 2 years?" And he will do that or give me the top 10 stocks contributing to the risk of my portfolio today, things of that nature, and it happens in real time rather than having to go into an application and clicking through various screens or trying to find the information. And so this is also creating massive efficiencies on our client side. This is something that our clients could with our current tools. It just takes time, right? And in a world where they are also trying to become more efficient themselves, this is extremely powerful. So things like that have been very well received. And then sort of more on the cutting edge we're thinking a lot about how our clients will want to consume our content going forward, right? So we already put a lot of our content on places like Snowflake. And some of that is even AI-enabled, for example, our factor models are using now or our clients can use Snowflake AI capabilities on top of our factor models. But going forward, even more directly, the possibility of using our APIs and their content through market standard LLMs like cloud, for example, right? So we're experimenting -- internally, we've already done that we're thinking about how to externalize some of the stuff like building MCP. So this is a connector between our data and our APIs and these LLMs, so that clients can interact with us straight in places like cloud and ChatGPT and the like. And so we think there's a lot of possibilities to really increase the usage of our content by making it more accessible through all of these tools. So very exciting future.

That's great. That's very helpful color. And if I can drill down further on the monetization part, so maybe the 2 aspect questions. So you talked about AI inside and the product functionality that you're adding. How do you think about that monetization? Do you think about that as a stable stake or ability for you to continue to charge for it? Or does it help you on the pricing front, retention? Does it help when you're baking off against the competitor. So first on that AI insight? And how do you think about monetizing the investments that you have done on -- or the AI capabilities that you are offering to the clients?

Yes. So AI Insights, we're bundling the agentic capabilities into the product. And the reason we're doing that is because we think that specifically the set of capabilities is something that enable our clients to use what we're already providing much better, right? So -- what are the benefits? Retention, it helps with new sales for new clients, and it definitely helps with pricing as well because we're adding a lot more value, helps our clients cut costs on their side. So it becomes a pretty easy decision to make from that perspective. So there will be other things that we will build over time that might have be stand-alone products that we sell separately. But AI insights specifically, we're bundling it into the current risk offering, right?

That's helpful. And then maybe on the other aspect, as you talked about the fact that your data is AI ready and as it gets available within MCP servers and cloud and other ways of monetizing data, how do you think about the opportunity on that front? And again, this is over the longer-term monetization opportunity on that side?

Yes. So if you -- just in -- and by the way, I think this is true across MSCI but talking about analytics, which is the area that I'm responsible for. If you look at the biggest consumers of our data, there are the firms that typically have an infrastructure to use that data efficiently outside of an application, right? And so -- and then they have these processes on top of that data. So who are they? A lot of the quant hedge funds, a lot of the banks, particularly broker-dealers, market makers, et cetera. And so these are the firms that are also at the forefront of the use of a lot of these tools. So we think it's just going to naturally increase the demand for a lot of our data as the data become more useful when paired with these agentic capabilities.

That's great. That's very helpful. Just drilling down to the quarter we've had like last 2 quarters, very, very strong net new subscription sales, particularly on the analytics side. Can you talk about what's driving that strength? And maybe if you want to go by client segment or by products and then we can drill down further on those growth drivers?

Yes, it's a great question. So why don't we start by segment, right? So just to give you a sense on the analytics product line, about 40% of our run rate is asset managers. Then we have 20% hedge funds, 20% banks and broker-dealers and 15% asset owners and 5% is like a little bit of everything. On the asset manager front, as you know, it's a segment that has had quite a bit of challenges over the last couple of years. A lot of those challenges haven't gone away, but I think the markets helped at least in the immediate term a little bit. But with those firms, what we're trying to do and we're getting good results with them is a couple of things. First, make sure that we have the conversation about consolidating more of what they do with us because we can help them save cost and it's good for us because we do more business with them, right? So some of them are, for example, only risk clients. We want them to be risk and performance clients, right? Some of them only uses in the middle office. We want them to use us in the middle office and the front office. And the more they do with us, the more they can save and the more business we do with those organizations. So that's clearly one of the strategies. The other one is just making sure that we remove any friction in the use of our products with them because people generally think, oh, well, is the spend with the vendor, right? But for every dollar that clients spend with us, they typically spend more than $1 internally in making effective use of our solutions, integrating into their infrastructure, running things, et cetera. So -- and this is linked to the AI question, right? The more we can help them make use of our tools without having to spend money themselves, the more opportunity that we will see in this segment. And that's kind of what we've been doing with asset managers. And I think that playbook will continue for a while. So that's on the asset management side. On the hedge fund side, hedge funds, particularly upon hedge funds, multi-managers, multi-strats in general, not just multi-managers, have been doing very well for the last few years, and they are big consumers of our model data, factor model data. So that has been a big driver for us. Part of the ecosystem as well are the broker-dealers and the market makers in the trading ecosystem, building baskets, hedging, et cetera. So those 2 things kind of go hand in hand, and that's been good for us in the last few quarters in terms of driving growth. And then the last thing is working with asset owners in helping them manage their total portfolio. The story there is as they increase their allocations to private assets, the total portfolio oversight becomes a very different exercise, much more challenging, and so we're very focused on making sure we provide them with the tools and data they need to do that. They're all in different stages of the journey. Some are already very advanced. Certainly, the Canadians and some of the Middle Eastern funds, the some of wealth funds, but many others are moving very rapidly in that direction, and we're focused on helping them.

That's great. We will go into private asset because I think, again, that's a very important area. But before we do that, I don't know if you referenced what's happening on the wealth side. So particularly on the wealth, any color that you can provide on traction that you're seeing for analytics?

Yes, that's a great question. We -- I didn't go there, but that's kind of -- part of that 5% I was -- that I was talking about is still a very small part of our business, but it's something that we're excited about. We continue to invest in this new tool coal MSCI Wealth Manager. But before I go into the specifics, just to back up a little bit and tell you some of the use cases that we serve, right? Because we're very targeting what we do for these organizations. So one is the model of portfolio construction, typically, we serve the CIO or the home office in doing that. So we help them with our factor model data, with our tools, optimization tools, et cetera. Our index data is part of that. Our private asset data will increasingly be part of that as these models start including more and more private assets in them, and that's definitely the direction in which things are going. So that's one use case. The other one is the adviser workflow, right? So specifically in helping the advisers build portfolios for their clients, customized to their needs and their requirements. But at the same time, still complying with the households. So they don't drift too far from what their organizations want them to do. So that workflow reporting back to the client, et cetera, is another thing that we're doing in wealth. And then the third thing, which is not strictly selling to wealth organizations, but it's part of what we think as the wealth opportunity is helping managers of assets, both traditional asset managers, but also increasingly general partners to position their products into the wealth channel, right? So that's a sales enablement play by helping them explain why adding certain products into the advisers' portfolios will leaving them better play somehow. So we do a lot of scenario analysis, a lot of risk and return characteristics and comparisons and so on and so forth. So that's generally what we're doing. I'd say we're still early in that journey where, as I mentioned, I started saying investing in MSCI Wealth Manager, which is fulfilling the adviser part of that workflow and integrating our private asset data, integrating our sustainability and climate data, and we're starting to see some wins in that space, which are part of the results that you reflect, but we still think we're early in that journey.

That's great. And there were 2 tuck-in acquisitions on the wealth side, right? And I was just wondering if you can talk about how that's being integrated into your wealth offering. And I guess that's part of the wealth manager product that you've talked about?

Yes. So the -- I mean that's -- wealth manager is actually rebranded the acquisition was fabric, right, and wealth manager is the branded version of that, but it's the same product. So that has been the main acquisition on the analytics side, as you mentioned, the tuck-in acquisition. This is a buy-to-build type of acquisition. So we still need to invest quite a bit of it to -- get it to a point where everything -- all the IP at MSCI is integrating the service for that adviser workflow, right?

That's great. Shifting gears to private assets, you've talked about private assets a couple of times. Obviously, a big growth opportunity. You recently launched private credit factor model with 1,500 private credit funds. Obviously, with the purchase -- been a few years now, but with the purchase credit data, you have a very strong proprietary data sets. Can you talk about how the opportunity is from an analytics perspective or factor model perspective?

Yes. So a couple of things. The first one is, yes, we just released the private credit model. That was the last sub-asset class within private assets that we have left because we have private equity, real estate, infrastructure, so we're missing private credit. We just released it. This is part of the solutions that I was mentioning earlier to help mainly asset owners manage their portfolio holistically. This model is integrated into our multi-asset class model. So now someone that's investing in public assets, private assets, including private credit, can use it to basically do asset allocation at the entire portfolio and then obviously, risk modeling for the various sleeves or sell the asset classes within that. So we're getting very good feedback from clients. We just released it. So we have a number of clients trialing right now the model again, mostly asset owners, whether it's pension funds, sovereign wealth funds, superannuation funds, those are the typical users of this tool. But then beyond that, we have a robust agenda for things that we want to develop going forward. There's a lot of problems that are -- have been sold for a long time on public assets that are still not quite resolved in private assets. For example, there are private asset benchmarks, but it's not easy to take that benchmark and then do a risk or a performance attribution against it, right? So that's something that we think we are well positioned to solve for clients. So these are -- this type of problem that we're going to resolve. Liquidity issues are super important, both for the institutional asset owner and also even more so for wealth. But what, for example, the large asset owners tell us is, look, if I have 50%, 60% of my portfolio in private, which some do now, and there is a market correction. And I get capital calls I have to liquidate at very distressed levels and that's a very bad scenario for us. So solving for that type of question is not something that anyone has done so far. So we think we have a lot of opportunities for helping clients in private assets specifically manage their portfolio -- and this is -- a lot of these problems are in the intersection of public and private, right? So it's not strictly a private asset problem is where they intersect with the public assets.

Absolutely. That's very helpful color. As you mentioned, private asset -- these analytics has been integrated with our multi-asset class as well. I believe multi-asset class is the biggest piece from a product perspective. Can you talk about the growth drivers there? Where you're seeing the growth? How do you think about the innovation there?

Yes. So the -- yes, multi-asset class is many, many use cases, right? So you need to break them down. I think we already talked a lot about what we're doing with asset owners. So that's a big part of it. It's also a big part of what we do with asset managers. So basically, all of these AI insights that I talked about are built on top of the of the MAC model and the MAC analytics that we provide. So we've already talked a little bit about that. But we also use these analytics in other client segments. So we're actually starting to see quite a bit of demand from banks. Now I'm talking about banks as principles, not the broker-dealer side of the bank to use analytics for the investment book on the treasury to solve some ALM problems, some regulatory requirements. And so again, it's opening up opportunities in other segments. And it's basically the same toolkit, a little bit modified for the needs of the segment, right? And then we also have a very healthy client base on the hedge fund side that uses our multi-asset class analytics for risk management purposes. This is different from the models, but yes, but we think there's opportunities to grow that piece as well.

That's great. Fixed income. Fixed Income analytics has been another big growth driver for you relatively smaller, I guess, but bigger growth opportunity. Can you talk about the fixed income analytics?

Yes. So on fixed income, where we're seeing a lot of the -- we've seen demand in a couple of places. One is integrating our fixed income analytics into the order management system so that there is consistency between the middle office and the front office on the number. So -- and that's mostly asset managers. The other place where we're seeing demand is in securitized products. We've spent a lot of time investing in securitized products across different types of collateral and structures. And so -- and regions, right, from certainly the U.S., which is the lion's share of that market, but places like Japan and others. And we're seeing demand from asset managers just to use the models directly, but also increasingly for banks, right, that want to improve some of their analytics in securitized products.

That's great. And maybe just honing on this question on regions. Can you talk about like from a regional perspective, have you seen stronger opportunities or greater inflection in growth in certain region versus another, like...

Yes. So it's a little bit -- I think the client segments behave similarly across regions. So a lot of it is how big is the client segment in a specific region, right? There's other dynamics, but that's a big one. So for example, when you look at hedge funds and hedge funds doing well and helping our growth, the vast majority of them are in the U.S., right? So there's going to be a geographic vendor. Obviously, there's a number of them in London, but not nearly as many as there are in the U.S. And there's a few in some spots in Hong Kong and Singapore. But it's, by and large, a U.S. phenomenon. So obviously, that biases things to the U.S. Broker-dealers and market makers are mostly in the U.S. and in Europe in a few places, right? So obviously, that's going to be biased there. Asset owners. Asia is a huge market for asset owners. There is generally a handful of very large ones, in some cases, 1 or 2, but in some cases, many like Japan or Australia, right? And so that region will have a bent towards asset owners and solving the private asset opportunity and so on and so forth. So I think when we talk about regions, a good way to understand that is through the lens of client segments, right?

I was just going to ask about the MSCI ONE product that was an initiative that has been going on for several years now. Can you talk about how that products have evolved, the MSCI ONE products?

Yes. So we are putting more and more of the MSCI content on -- MSCI ONE is basically becoming that single pane of glass through which our clients can access content, not just for analytics but across all product lines. It hosted AI insights is -- hosted inside MSCI ONE as an example. And so you can get all of the bar analytics, the risk metrics analytics with the AI. It's now all embedded there. We have our sustainability and climate data there, real estate data, our index data. We're working on putting more of our private asset data, and so it's going to become a hub for clients to access all of the content across product line, which will help us with telemetry, understand what clients are doing. There's more self-servicing, some of it AI-aided in that platform. It's a lot easy to discover new capabilities for clients and hopefully, that helps with the sales effort is -- clients can self-discover things that they might be interested in. Now that said, one important thing to understand is that we -- we'll never be a company that distributes only through our own applications in our own channels. We have an open architecture philosophy. We distribute our content where our client wants it, right, generally. We want to meet our clients where they are. And many times, that means that we distribute through competitors, and we're quite okay with that because we need to reduce friction for our clients, right?

That's true. Absolutely. Talking about pricing, I was just wondering if you can talk -- provide some qualitative or quantitative color about how you think about pricing as you're adding more and more value for your customers, how do you think about pricing?

Yes. So sometimes it's straightforward in that it's a new product and we price that product. And obviously, when we sell it to an existing client, we have a price list that we go by, et cetera. In terms of price increases, we're always very careful to make sure that we are thinking about the value that -- the incremental value that we're providing for those clients. And that's why we don't have a one-size-fits-all policy because sometimes we spend a lot of our road map and agenda is to help one type of client. And so obviously, they're getting a disproportionate amount of incremental value versus some others, and we take all of those things into account to make sure that clients feel that they're paying for the increased value, right?

Okay. That's great. Actually, when we spoke about the different products, multi-asset class and fixed income, we didn't really delve into equity factor models, and I was just wondering anything in particular that you would flag on the equity side as you're seeing more adoption on that front?

Yes. We have a very robust road map on the equity side. Some of it is client-driven demand that clients sometimes ask us for new versions of our models. That can have a geographic bend to it. Can we have a model for this specific country? Or can you modify a model to have different factor structure. And what we find out is that even though the demand might come from 1 or 2 clients, generally, these models end up having much broader adoption across the board because the idea of one is the idea of many, right? But beyond that, which is something that we've been doing for the last couple of years, we're starting -- this is an area where we're starting to use AI focus a lot more on factors that might not be permanent by transient type of factors that are more thematic in nature, specifically because our clients want to understand performance related to certain events. So for example, COVID or the Ukraine war or tariffs or any of these things? And how are those short-term effects are impacting their portfolio. Now these aren't things that are going to persist for 10 years, the way like momentum or value or growth could, right? Of course, those things revert as well, but these are more permanent factors. But linking returns to those themes, those events is something that we're very much focused on.

That's great. And that was going to be my next question as well. As you think about your product road map, you talked about a lot of things on AI front getting your data AI ready. You talked about some of the innovations that coming on the equity factor model? Are there other things that you would flag? Obviously, you also talked about bringing more and more of your product suites on MSCI ONE. But as we think about your product road map, things that to look out for?

Yes, a lot of integration also and cross-pollination across product lines, that's something we haven't talked about. One thing that we just released is our factor risk analytics were embedded in what we call Total Plan Manager, which used to be called Caissa, that was part of Burgiss. That's a product that helps basically endowment foundations, family offices basically given a complete view of their portfolio. So there's a very heavy private asset bend to that product given how those organizations invest. But we're starting to see very positive feedback from clients that are basically able to access the bar risk analytics inside that product without having to use a different front end for that. So that's an example. We're doing a lot of work across index analytics and basket creation. The ability for clients to define baskets that have very specific characteristics that can turn into an index. A lot of work with -- again, with private assets trying to take more and more of the data and turn it into risk factors. So there's a plethora of opportunities to cross-pollinate across the organization that we're very, very excited about. And in the future, we think our strength, biggest competitive differentiation of the firm is our ability to bring all of the capabilities of the firm in an integrated fashion to solve client portfolios, that's something we're very focused on.

That's great. We'll end it there. Thank you again. Thank you, Jorge for joining us. Thank you, everyone.

Thank you.