Hi, good morning. I'm Heather Balsky, B of A's U.S. Business and Information Services Analyst. I'm excited and a little bit voiceless today for this call. We're redoing a deep dive into Moody's Analytics, or MA Business, with President of Moody's Analytics, Stephen Tulenko. Prior to his appointment to President. President for MA, Stephen was Executive Director, Sales, Customer Service, and Marketing for five years. He also worked as Group Managing Director, Global Head of Sales for the Investor Services Group within Moody's Investors Service, a unit dedicated to providing credit research and risk management tools to buy-side and sell-side institutions. As a managing director in the organization since 1998, Stephen has also managed marketing and product development teams within Moody's. Stephen, thank you for joining us. Good morning, Heather. Thanks very much, everybody, for joining. I'm not sure. How many do we have on today? Can we tell? You know what? I don't have. Oh, no. We have a count. Yeah. I see you. Yeah. I see a bunch of black boxes. I hope everybody's. Yeah. Having a nice morning. Yeah. I think it's a good number of people. I also wanna say we're joined by Shivani Kak, Head of Investor Relations at Moody Corp. Shivani, it's, it's great to have you as well. We, we want this call to be an opportunity for investors to better understand the MA Business and its drivers. There will be a replay of this call. We, we at B of A can send to you, and we'll also be available on the Moody's website. And with that, we'll go to the first question. And, and thankfully, I see you will do more of the talking than me, from here. So I thought it would be helpful to kick off with a rehash of Moody's Analytics history. What was the vision for MA back in 2007 at Inception at Inception, and how has, has that evolved over the last 16 years? Okay. Thanks, Heather. Sorry to hear you're not feeling great. Your voice will come back one of these days, right? So, good morning, everybody. It's a pleasure to be here. We had a group at Moody's, which I think you just referred to, which was dedicated to selling products, developing products, and working with investors especially, often bankers as well, prior to the formation of Moody's Analytics. And there were a couple of other businesses that we had developed. There was an economic research business that we bought in the 2005 era, I think. There was a company called KMV that we bought earlier than that. And it was a combination of other capabilities where we were able to help especially financial institutions and firms in the financial services understand credit. In a business that was probably about $550 million or maybe $600 million at the time when we created Moody's Analytics in 2008. Now, the project had begun during 2007, and it was really there to consolidate and really create some synergies across these groups that had been developed over the course of the previous couple of decades. So in 2008, we formed MA, Moody's Analytics, created it as a revenue segment, and reported on it in our financial statements as an independent unit. And you've seen a tremendous growth story since then. We were growing very nicely prior to this as well. But since we started to report out on the results of Moody's Analytics, I think we're at 60+ quarters of I think maybe 64 quarters in a row of revenue growth. And we've generated a CAGR of about 12% over that period of time. 60% of that revenue growth has been generated through organic product development and organic efforts. So we have been pretty acquisitive over that period of time. I think we've done 35 or 40 acquisitions since 2008. And I would say there's a couple that are pretty notable. There was the acquisition of Fermat, which was a small French software development company that really helped us springboard into the software development space to support especially banks in their regulatory capital calculation needs, capital allocation needs, as well as asset liability management, liquidity management products. But the regulatory reporting franchise there was pretty strong and gave us a new entry point into financial institutions. Another large acquisition that many of you might have heard of is the acquisition of Bureau van Dijk in 2017. And I think that was a moment that we should mark here in that it was our effort to expand our capabilities in terms of delivering information, firmographic information, and other tools that can help people understand who they're doing business with. You know, who is this customer I'm working with? What about this supplier? And these kinds of questions are often asked by, you know, literally our 15,000 customers. So we had a tremendous growth story. I would say, maybe just a ballpark, two-thirds of the revenue at Moody's Analytics, which is about $3 billion-ish. Two-thirds of that revenue comes from firms that are in the financial services sector. About a third comes from non-financial corporations and government entities. And I think that'll be important to talk about as we go forward through this conversation. So anyway, a nice growth story. One other thing I'll note is, you know, where we started was originally the strategy here was to leverage the brand to support the brand, I should say, by selling research to the investor community so that the investor community would continue to and rely upon the ratings themselves. So there's a pretty interesting connection there. A lot of what I do is here to support and contribute to the brand equity of the firm so that when investors are thinking about buying a bond, they think about Moody's as a bond rating agency. They want to consult or understand, maybe what their opinion is before they go ahead and do that. So at the original you know, the original point of me joining the firm, which is, by the way, in 1990, our goal was to help the investor community understand ratings better. I would say that's sort of what has created this platform for Moody's Analytics overall. That's helpful. I was gonna say a lot of investors still think of Moody's as a ratings business, but MA is, I think, 62% of your sales and around 36% of your EBITDA. So i t's helpful to understand the industrial logic and synergy between the two businesses, especially as you've moved from your original, you know, effort to share ratings and information to other areas. Yeah. So, I mean, I guess I sort of touched on that a second ago. The original reason was to support the ratings business. And in fact, the group the name of the group I joined was the marketing department of the rating agency, just to give you a sense. And so, you know, if you think about Moody's big picture, roughly $6 billion in revenue, a little bit less than half comes from the rating agency in 2023. A little bit more than half comes from Moody's Analytics. And if you look at the diagram we have on the screen right now, the ratings box is one that most people are familiar with. As you mentioned, Heather, this is the franchise that's been around, I think, for 115 years. John Moody invented the concept of ratings, literally, in 115 years ago. Research and Insights, the box immediately to the right, is where we're monetizing the content and the analytic research that the rating agency produces, among a few other things. We have our credit scoring tools and some other things in that unit. So this is the place where we help people evaluate and understand the risks associated with individual companies with analytic models and analytic insights. That business is roughly oh gosh, it's probably $800 million or so, but the research business is a big portion of that revenue base. The Data and Information Unit, this is we report out on a segment called Data and Information, and that's where all of our data feed products are. The most, probably, notable is a product called Orbis, which is really a crown jewel for the company. Orbis is the database of companies that includes public and private companies. Our coverage is something north of 470 million corporations. Roughly 300-something are actually actively, you know, going concerns. but we have history there for decades, firmographic information, helping you to track how these companies relate to one another and understand the corporate hierarchies and the beneficial ownership associated with these names. And this dataset is extremely helpful when you're thinking about doing credit work, when you're thinking about doing KYC work, and when you're thinking about doing other analytic and risk management work that we offer to our customers. That third box on the right, the Decision Solutions Unit, which I think is the fastest growing unit for the last couple of years, this is where we do a lot of our software development and creation of workflow tools. And we really, the majority of the revenue here is denominated in a SaaS platform, which we think of as dedicated to banks, another one that's dedicated to insurance companies, and a third one that's dedicated to KYC workflows. So you can think of these as software chassis, software, delivered as a service, and a platform that enables you to do analytic work that often is generated by models and supported by models that are developed in the Research and Insights group, fueled by data that comes from the Data and Information group, and then provided in a solution stack that is sold through the Decision Solutions Group. So our lending applications in Decision Solutions, our underwriting applications in Decision Solutions for our insurance companies, or maybe our Know Your Customer applications, incorporate content from Data and Information, analytic models from Research and Insights, as well as software that we develop to bring these things together. And I guess the big thing that we should probably say is, you know, the use cases we support are kind of front office, help people issue, help people originate, help people select or underwrite insurance policies, maybe. Let's measure. Let's manage. Let's think about risk at the portfolio level. Let's understand that risk and find a way to actually dimension the uncertainty associated with our portfolios. And then how do we think about complying with regulations, maybe verifying and, and then reporting, to support financial reporting requirements or regulatory reporting requirements? Now, I guess the other thing at the bottom of the diagram is worth noting, we can talk about multiple areas of expertise. We often talk about this, you know, we're living in an era of exponential risk where, you know, risk curves are not linear and where risk curves are not independent, or I should say, are not mutually exclusive. And understanding the implication of cyber or climate on credit, for example, is a good way for you to maybe understand some of the objectives we have in terms of bringing analytic firepower to the table to help you understand who you're doing business with, what risks are resident in your portfolio, and then also how you might need to tell people about that. That's really helpful. Thank you for that. I want to dig into the business subsegment, but before we do that, can you talk about how Moody's thinks about portfolio optimization for MA? What do you look at when deciding to invest in a segment? And then on the other side, how do you think about pruning the business? Yeah. So I think this may be a bit what you might expect, right? We're pretty focused on understanding deep currents that represent demand among our customers. You know, what do we expect they're going to need help with? What do we expect they're going to need to do for some extended period of time where Moody's can provide expertise and, and support them, you know, for a long period of time? And especially, you know, where are those currents moving quickly, and where do we expect them to maybe increase as well? So in the financial services sector, where we have decades of experience working with customers, credit is a good example of a demand curve that we, we've invested in, you know, literally, very extensively. Cyber risk is a place where we made some investments in order to support our customers. We've got an affiliate called Bitsight where we made a pretty significant investment, and are leveraging their capabilities now to help our customers understand cyber risk. Climate is a good example of a, I would say, an investment we've made. And I probably should mention an acquisition we did in 2021 that really beefed up our ability to project the impact of weather and climate on property and therefore, the impact it might have on companies. So those are good examples of places where we perceive deep currents of demand. And, you know, optimizing is often making sure that we redeploy the resources that maybe aren't required this year as much as they were required last year into those new areas where we see the investment opportunities. I'll give you an example of how to think about this. I mean, in a sort of prototypical way of a product S curve, you start low, you ramp up, and it kind of levels off, and you think of that as a revenue curve. In the same product lifecycle, there's a kind of a corollary curve which starts high and then kind of levels off lower, which is your expense curve or your investment curve. So as we develop products, we often are investing heavily at the beginning. And then, as things start, as features develop and customers have given us a good sense for feedback over the course of a few years, you start to see that number drop down. So as those cost curves are dropping down, we look at that dynamic and then try to redeploy into the new places where we see growth. I should probably highlight this in case people weren't on our last earnings call. We talked a lot about investments we're making in three key areas this year. We talked a lot about platform engineering and architectural investments to foster faster product assembly and faster product development. Think of some of the work that you might do under the floorboards to make sure that the pipes are connected, to make sure data is very interoperable and verily discoverable across different product sets. We talked about investments in GenAI capabilities. And we may talk some more about that, but this is a very important part of our program, especially in 2024. And then we also acknowledged some really good growth opportunities. And maybe, Heather, another way of exploring your question, you know, in terms of how do we optimize and how do we prune. Right now, we are redeploying toward some really good growth we've seen, not in the financial services sector. We've had good growth there too, but in some new sectors for us, the non-financial corporations and public sector customers where we've seen some outsized growth, in the last couple of years. And I think on the earnings call, we acknowledged that in those new sectors, these, you know, the, these non-financial corporations and government contracts, government customers, we've seen a CAGR of about 14% sales growth there, 14%, AR growth, compared to, you know, our, our AR numbers around 10 right now. So, you know, financial services is contributing nicely. It's the bigger portion of the business, but we're getting some really good growth in working with corporations that aren't necessarily in financial services. And, we're investing to make sure that our products are adapted toward those needs. So those are good examples of us rolling over into new opportunities, redeploying and reinvesting. And, I mean, pruning is a part of that as well, right? So we have done some pruning over the years. You may remember we actually divested the knowledge business, the Moody's Knowledge, gosh, it was called Moody's Analytics Knowledge Services, I think. That was an Indian outsourcing operation that we had for a few years. We divested that. And I would say we've also moved ourselves pretty substantially out of one-time software projects and into SaaS-delivered subscription products. That's another big change we've made over the last five or six years. I think you might be on mute there, Heather. Yep. I am. Thank you. I mean, thank you. Really helpful. I thought, you know, from here, we could dive into each of MA's lines of business, and starting with Research and Insights. You know, when I think of the segment, I think of CreditView. Yep. Can you talk about what CreditView is, how big it is, the composition of that customer base? And this is actually. I'm gonna make it a two-parter just for time, but just, you know, Moody's Research Assistant is the add-in for CreditView, and your first GenAI product. So kind of after we get through that, you know, if you can talk about why the company started with CreditView in terms of AI. Sure. I'm gonna see if you recall that. Okay. Yeah. So Research and Insights, this is sort of the core business that we all started with around here. Roughly $880 million in revenue, so a big chunk of the $3.1 billion in revenue for MA. This is where we monetize and commercialize the content the rating agency creates through the ratings process. Many of you have probably seen research reports we write on companies. I think we cover probably 10,000 names these days. These are rated issuers. And then we, very importantly, provide services to explain those ratings to institutional investors, especially institutional investors, but also credit professionals. Probably two-thirds of that revenue comes from the research business. So it's north of $500 million. And you can see we've got 2,900 customers of this research service. There are tens of thousands of consumers that come back to our website every month to engage with us. And we get thousands of calls a month to talk to our analysts as well. So that's the big chunk of the revenue there. We also have all of our credit scoring tools in here. So there is a gem here that people may not be aware of, but in a lot of ways, we're sort of the almost a FICO of the commercial lending space. C&I lending is often supported by a product we have called EDF-X. And that is, without a doubt, the scoring model, the credit scoring model that more banks buy than any other. And then there's some economic research that we have here as well. So all of the people who are thinking about doing analysis and providing insights and creating insights, sometimes through models, sometimes through people, are generated through this LOB. Research Assistant is our first foray, as you mentioned, into generative AI. We decided to start here for a few reasons. One, we have a very interesting content set. As you can imagine, the research produced by Moody's is not available through your typical chat tools that might be available through, I don't know, OpenAI or perhaps through Google, you name it, because they're not available to the public internet. Instead, it's a proprietary database of content. We know that many people would like to work with Moody's Research as they're doing their investment analysis or credit analysis. Research Assistant was the first tool we released. All of us here understand that customer base very well. We wanted to do our experimentation here because we have a sense intuitively as to how those customers work with us and what, maybe what we expect the demand curve look like. But it's a great place for us to understand how the pricing dynamics work and what the usage patterns might be so that we have just because we have intuitive benchmarks. So we launched there. We did a lot of work last year to create a very robust generative AI capability. It is endowed with multiple years of content from the rating agency and then a lot of prompt engineering in order to generate content based on your request that is not affected much at all. And we have done everything we can to remove or mitigate the risk of hallucinations. So it's content that's up to date, like literally as of right now. And we've done a lot of work to prevent it from hallucinating on, on topics that we don't really have or haven't had something to say. So what would Moody's say about this is a question you can ask. But if you ask a specific question like, "What was the stock price on Tesla in, you know, 2022?" that's something that we don't necessarily have a database to track and haven't written research about. So we will tell you. We don't actually know what the answer is to that one. If your prompt does work with content that we can support you on, we'll actually provide citations that specifically acknowledge where we got the content we've generated in order to help you, the analyst, or help you, the credit professional, or help you, the person who's doing trade credit or whatever. Maybe it's KYC work; understand where we generated that text. So we've had some pretty good uptake on this. It's early, early days. But we're very excited about the patterns we're seeing and the repeat usage we're seeing thus far. So I think this is gonna be a pretty big deal. One other thing I should say is this is the first launch of what I think will be several other releases of assistant-like capabilities that we do this year. You'll see a couple more over the next quarter or two. That's helpful. So I'm gonna shift to your next line of business. And we're gonna move on to Data and Information. Yeah. If you can give a brief overview, the business helps your customers make decisions across origination and underwriting, portfolio monitoring and management, risk and capital management, and finance and reporting activities. That's a lot. Yeah. Can you just. Yeah. So I would say that. Can you still? Yeah. Sorry. Yeah. Go ahead. Keep taking. Yeah, yeah. So here what's going on here, think of this as the revenue line that represents the data feed activity that we undertake. You can imagine things like ratings feeds that go into back-office systems. You can imagine master data management applications where we're offering a feed of information on companies to support everything from oh gosh, supply chain analysis and third-party risk analysis to KYC analysis. And often, the larger institutions will work with us to pull the data into their systems, rather than maybe use some of our workflow tools on occasion. They often buy the modules or the components that they need the most. And then we'll support multiple use cases through those master data management concepts. So a couple of our largest contracts last year were large non-financial corporations that are really buying a feed of information on companies. Excuse me. And that feed might be firmographic information. It might be trade credit information. It might be hierarchical information so that they can manage things like sanctions and understand who they're exposed to. But there's also all sorts of data on companies, financial statements. We have literally 40 million financial statements in that database. And we have, gosh, we have a database of patents. It's quite impressive. So the information, especially on private companies that's hard to acquire, is something here that we, I think we add a lot of value on. Just to give you a sense for how this works, we have relationships with about 170 information providers. So we have licensed content from literally almost hundreds, you know, 170 different organizations. Often, these are the organizations that are endowed with the affirmative authority from that country to be the supplier of information on companies. Think, certifications of, or certificates of incorporation that you might get from the state you live in, or you might get from the country you live in. We often have relationships with the data providers that are licensed and have the affirmative license to deliver that definitively. So we take that data from all these different sources, and then we bring it together in a unified database so that you can understand definitively what the corporate structure looks like. And you can imagine when you can actually speak with authority, with respect to that. You can help all sorts of different use cases. So that's why you're seeing that proliferation of use cases over time. One other thing I'll note in here, there's an interesting business we bought a couple years ago that we're now incorporating throughout our product array. It was called Acquire Media. They had a product called NewsEdge, many of you may have heard of. NewsEdge is a database of all of the news, basically, that occurs during the day. We process about 1 million news stories a day and use natural language processing techniques in order to understand the sentiment associated with each of those stories. So what is the meaning or the material impact or the message of that news story? And then how might that be useful to, maybe, influence your model or keep you up to date on, you know, topics that you might wanna focus on? You can imagine how valuable that can be in terms of creating new analyses through generative AI. So we're pretty excited about that capability as well. It's one of the things we're happy we have brought into the fold here given the opportunities throughout the product array. That's interesting. Just curious on the GenAI piece. Is that something you're doing already or something you're working on right now? Yeah. We've only commercialized the Research Assistant at this point, but we are doing experiments across the product array. There are literally dozens of product development efforts going on that leverage GenAI capabilities. And, and when I say that, I don't mean kinda search bot capabilities. I mean, generate analyses, highlights. I'll give you an example of one. We could talk about maybe in the context of another business, LOB here, but, just an example, what we're talking about is, in commercial real estate, we are, we're taking the news from that, that natural language processing database that I mentioned before, and then associating the news stories with the names that were in that Orbis database. And then taking those associations and linking them to tenants that are in commercial real estate portfolios so that I can see, you know, if this particular oh, I don't know, retailer is in trouble or there's news that says that they've decided to, you know, move, whatever it might be, I can link that back to my portfolio and understand this lease in my portfolio might be in jeopardy. And I might even be able to see, gosh, it's, that building's 100% leased by that particular retailer. Usually, that's an indication that you've got some trouble coming up, and you've got some vacancy rates that you're gonna need to deal with. So we can condition models and, not just highlight and alert, but actually do math behind the scenes using GenAI on that situation to project the impact on your portfolio. So that's an experiment we're now previewing with customers. We're in the process of evaluating an MVP. We expect to launch sometime in the next couple of quarters. I appreciate that. That's really interesting. We have about 11 minutes left. We have yet to go through decision solutions regarding the subsection. Okay. Yeah. We'll, we'll try to power through. I may consolidate this a little bit. Yeah. Moody's has presented decision solutions as a portfolio of SaaS solutions for insurers, banks. And then there's the KYC offering. Yeah. Can you break it down a little bit for us? And then we can quickly kinda touch on the three pieces of that business. And I think that it's the three. You know, let me know if there's another piece in there that I missed. Yeah. So maybe just to frame it, the easy way to think of this is there's some big picture trends that many of our customers are facing, certainly in financial services. And I think, in the corporation, the non-financial corporations and government customers as well, this idea of digital digitalization and transformation work. This is amazing. I saw a stat the other day. I need to dig this up. It is, you know, 40-some-odd% of banks in the United States are still using mainframe technologies, right? So there's an example of technology that might be 30 or 40 or maybe even 50 years old. You know, relational database technologies that might have been purchased and developed upon maybe through the 1990s are still in effect today. So there's tons of work going on with banks to try and streamline their operations, improve their efficiency of their systems, but to really bring their front office, middle office, and back office together so that they can see the synergies across the departments. So this concept of digitalization is a trend that's really relevant to virtually all of the solutions you see in this decision solutions category. And the recipe is basically gather information on a borrower or an insurance customer or maybe a potential customer you're gonna do business with in terms of KYC, gather the information, digitize it, analyze it with models like the models that we bring to the table, and then capture decisions and help people make decisions on that stack of information and that stack of analytic capability and then remember those decisions so that you can report on them downstream. So this process, this recipe of curating the data that's relevant to the decision at hand, using models that really can help you make better decisions, not just more efficiently but actually higher quality decisions, along with software to bring that all together vertically in a solution stack is what's going on with the decision solutions. So we're heavily investing in the workflow tools in order to make it possible to bring and cross-sell data and analytic tools so that you can have and make a better decision. So that theme is, I think, resonant. So you see that if you think about the three big SaaS platforms. You've got a platform in banking that captures data upfront at origination on a loan, helps you credit score it. We can often prepopulate on that loan using Orbis. And then we can look at portfolios either to monitor them, like we were talking about before, or to do economic capital analysis or regulatory capital analysis. Think RWA calculations for regulatory reporting and under the Basel regime. This kind of work is something where if you get your data straight upfront and can leverage it through the process, that value chain is something that Moody's can help you with all the way through. So having that in a single database, a single data mart or data lake - it really doesn't matter what words we use here - but having that data accessible through the three or four different points along the value chain so you're talking about the same thing is wildly useful. Same thing goes with insurance. If we're talking about underwriting a policy or if we're talking about pricing that policy, right, having the actuarial models or the CAT risk models to project what the losses might be and understand the actual risk associated with those opportunities and then pricing it well is something that we can then help that insurance company with that same kind of value chain. So the sales motion's very similar. Whether it's a bank or an insurance company, the vernacular's often different. And the calculations might be a little different, but ultimately, it's similar math. But, and then, KYC's the same kind of thing. Gather data upfront, benchmark it with databases that have been curated very carefully, use AI models to, maybe help you make that decision more quickly, and then create a workflow tool to make that happen even more effectively. So if you think about decision solutions, the banking group is probably, oh, roughly $500 million in revenues. The insurance group's about $500 million in revenues. And the KYC business is north of $300 million in revenue. That gives you a sense. But they're all following very similar patterns, similar sales motions, very sophisticated analysis being done here. And we're able to integrate and cross-sell through that stack, sometimes selling software first, sometimes selling data first. But the synergies across the stack vertically form. And we end up having good, sticky customer relationships that grow over time. That's a really helpful overview. One of the questions we get sometimes - and I'm gonna kind of combine things here a little bit - but, is that for some of the solutions you're providing, I guess the problem you're solving, you know, your customers, you know, have you know, these solutions have or these needs have been around for a while. So, for example, KYC and some of your banks' tools. So can you help us think through kind of when you're going to market, are you replacing an internally designed system? Are you replacing, Yeah. Party tools? Just kind of where. Sure. You know, where do you come into that? Yeah. I mean, if you think of decision solutions as a whole, I think it's probably fair to say that most of the time, our biggest competitor is an internal build, right? So in the banking world, an asset liability management or, or balance sheet management system often has been homegrown. It could be that there's another provider that's in there. And there might be a sort of classic software replacement cycle. So, you know, we do replace we do go head-to-head with some firms from time to time, in, in banking or insurance or in, in KYC and, and Know Your Supplier kind of applications. I would say that the thing that's probably most interesting is we, we believe strongly in this, this combination of data analytic models that help you evaluate the risk, dimension it, and understand it, and then software applications together. When you can offer all three, you can offer a seamless integration that often requires three firms to deliver if you think about our competitors. Some of our competitors are software providers. Some are data providers. Some are. Very few are capable analytic shops. Very, very few, if any, I would argue in most of these cases where we're winning, it's because no one else can offer three. And we represent an opportunity to be a strategic partner for them. From time to time, we're displacing a database or we're displacing a software application. But it's because of that vertical stack that we win. Yeah. On mute again, Heather. Oh. Mute. This, of course, is the way things work, right? It's almost as though we're doing a customer demonstration, right, just when you think everything's perfect, somebody forgets and pulls a plug. I think while we're waiting for Heather to join. Yeah. Can I just ask, Steve, what are you most excited about? There's a lot going on at Moody's with GenAI and. Hi, Steve. Can you hear me now? Oh, there you are. Yeah. Yep. I hit a wrong button on my phone, so I appreciate that. So thank you for that. Gave you a little break, so you know, you kind of touched on it. But you've talked, you know, the company's talked a fair amount about cross-selling the business. Can you help us understand what's different today than two years ago or even one year ago and how you think your efforts are tracking with regards to cross-selling? Yeah. I mean, the strategy, I think, is very much the same, right, have develop a strong product development program that is fed vigorously with customer feedback, make sure that you are developing a series of these product life cycles to drive growth over time, invest in the sales group and the distribution force in order to make sure you can reach out to lots of customers. So this, you know, the formula, I think, is a formula we've been following now for a few years. The biggest change maybe in the last if I go back three or four years is we're very focused on investing in integration points, making assets that we have here, these crown jewel assets like that database on companies or our research, or maybe some of these calculation engines, maybe some of these software capabilities, make them, you know, seamlessly integrated so that customers can avail themselves of these capabilities out of the box. And that's not something that we would have been able to do quite as well five years ago. We have more investment to make. And we're continuing to reinforce that as a value creator for us. That's a bit of a change. I'd say in the last couple of years, the biggest movement here is in the generative AI space. We are strongly convicted in the idea that this is a generational opportunity. I have, you know, I think this is as big a deal to knowledge workers as the advent of the browser and the accessibility through, you know, the internet. This is a very powerful tool. We're seeing customer feedback and internal feedback that suggests that people can be 30% more productive, maybe save 30% of their time. And I think that's just the beginning. The speed of change here is tremendous. And we're very, I think, excited to be a part of that. And we're diving in to make sure that we are there to adapt as that set of technology capabilities becomes available. I don't think we're a technology company per se. But we need, and I think we aim to deliver technology to our customers so that they can leverage our content. We have a vast data estate here, often proprietary, and if not proprietary, unique in its historical gravitas. And that, I think, will be tremendous as we move into a new era for knowledge workers, whether it's for us internally at Moody's where we can save a lot of time to do the work we do or whether it's to help our customers, you know, generate maybe even better analysis and save some time but maybe also generate some better analysis. So, you know, we're very excited about this. And that's one of the things you see us investing in pretty heavily right now. So that's, I think, probably the biggest change. and, you know, we're looking forward to 2024 to learn more how this is gonna work and what the adoption curves look like and, and how we can help customers even more than before. I appreciate that. I think that's a great note to end on. I think it's interesting what you said about, you know, becoming a technology company, you know, in some ways. I think, you know, technology's just becoming a bigger part of everyone's day-to-day. Yeah. Especially for a lot of people on the call. Yeah. That makes a lot of sense. You know. Yeah. I don't think we're gonna win the game of Leapfrog with the big technology firms. But, I do think it's great to leverage their capabilities and make sure that we can connect them to the analytic capabilities we bring to the table here. That makes a lot of sense. Well, thank you so much for the time. We really appreciate it. This was really informative. Thank you so much. Thanks, Heather. You're welcome. Appreciate it as well. Goodbye, everybody. Thanks very much for joining. Hope you feel better, Heather. Oh, thank you very much. Take care. And, Shivani, thank you to you. Bye.