All right. Good morning, everyone. We do have a couple seats up front, so those of you who are ushers in the back, make sure people know that when you're a couple minutes late, you get the good fortune of sitting up front with all of us, just like you're in the classroom. Good morning, everyone. Welcome to New York City. This is Moody's headquarters building. We're on the 52nd floor of Seven World Trade Center. This is where we do a lot of our innovation, and we welcome you to our Innovation Open House. This is a location. We renovated this floor recently, and we've done this in a few other places. We have an operation in Seattle, San Francisco, Paris, London, several other offices, but this is one of those where we come to gather and do work together. So this is literally designed for collaboration. The space is built so that we have flexibility, and as you walk around and tour the floor and visit some of the workstations we've set up, you'll get a chance to see how we all do our work. I encourage you very much to take a minute and take a look at the view. You have a fantastic opportunity to see New York City from here. To the south, we have the Statue of Liberty, something we can all be very proud of. To the north, we have things like the Empire State Building and about 500 more buildings that have popped up in the last 20 minutes, and I don't know what they are. There's 100 of them. I really encourage you to take a minute and look down. You have the 9/11 Memorial here, and then take a second and look up, and you'll realize that at the 52nd floor, you're only halfway up the Freedom Tower, which is pretty impressive. So take a second to do that while you're here. We hope you enjoy it. I thought we should just give you a sense for why we've gathered today, and, you know, we spent a lot of time talking about numbers. We've spent a lot of time over the years talking about Moody's Investors Service. Most of you are very familiar with the rating agency. It is a fantastic asset, a crown jewel asset that any company would be proud to be a part of, and anyone would be a proud to be a part of. But we, we often, especially in the last couple of years, have got, well, we've got more and more questions from you about what's driving growth in Moody's Analytics. What's going on here? How is it that you're putting up 10% ARR numbers? Can you give us some sense for what's really happening behind the scenes? So we thought we'd take today and spotlight MA, right? So we don't—we've set ourselves up to really concentrate on, on the Moody's Analytics businesses and, and really give you a sense for some of the crown jewel capabilities we have here. Before we dive into the program, I'll give you a couple logistics. You always have the window washers show up right around now. It's one of the important elements of the program. It's amazing. I literally said, "Please make sure the window washers don't come in during..." Yeah, okay. So number one, we're going to have a couple opening remarks from me and from my colleagues, René Bouw, who's our Chief Architect here at Moody's Analytics, and then Nick Reed, who's our Chief Product Officer. We're going to then move through a series of whistle stops along the way here. We're gonna stop in at a couple of stations that are set up to give you a sense for how we do product development and what our products look like, and how the customers feel when they interact with us. Let's see, we're gonna come back and do a plenary session as well, where Rob will join us on a panel, and we'll be here to ask you, or sorry, answer and address questions you might have after we spend some time on that, on that whistle tour. So that's the plan for the day. So let me just dive in with respect to Moody's Analytics, and I thought it would be helpful to give you a just refresh with respect to a slide that we used in the second quarter earnings call, which Rob, I think, walked you through. This is really an overview of where we produce the money at Moody's. It's intended to give you a sense for where the big revenue drivers are and really the businesses, the biggest businesses that are driving the performance. Then, while we're at it, maybe acknowledge some crown jewel assets, some crown jewel businesses that really are resonant here at Moody's, that, you know, you don't see everywhere else. So, if you think about it, we've got MIS represented on the top left of this screen, and that's the ratings business. That's the, I would argue, the world's preeminent global rating agency, somebody, the place that I've been proud to be a part of, literally for 33 years. I'm really happy to wrap myself in the cape of Moody's brand and go to see customers. It's one of the greatest feelings you can have when you go to see our, see our customers. And then, as we move over and think about the research business, I think it's helpful to think about that as literally the best fixed income, at least credit research business in the world, and maybe the best credit and economic research business in the world. It's really a gem, and we thank very much our colleagues from MIS for helping us with some of the content that we commercialize through that unit. But this is a tremendous juggernaut of recurring revenue growth for us, and we'll spend some time at a station today looking at CreditView, which is the flagship product within the research business, and especially the new AI Research Assistant that we are now in preview with our customers. So we'll spotlight that. If you move a little bit further to the right, the Data and Information unit, we report out with that LOB in mind in our financial statements and our disclosures. This is where we capture all of the information on companies and commercialize the sale of that data through feeds, APIs, and make that data available through our products. But just to remind you, this is literally a database of 470 million companies growing all the time, and we believe it to be the deepest information and the most valuable information you can have, or you can seek, or you can use in order to analyze companies, understand companies. So literally, this is a crown jewel asset like no one else has in the world. And then in the Decision Solutions unit, where we often talk about software and workflow tools, there are three big SaaS businesses that we have. You have seen in previous earnings calls. We've spotlighted the Banking group before. I think we talked about Insurance in Q2. We certainly covered the KYC unit before. Think of these as businesses where we're gathering data that's relevant for the task at hand. We're analyzing that, providing analytical tools to help understand maybe what's happening with respect to that topic you're addressing, and then using software and workflow tools to bring that together, maybe often across departments. So we go to market with respect to those ideas on the top row, and the, the things that we do for customers, the value propositions we bring to the table, are things like help them initiate a new conversation, a new relationship, a new business contact with respect to maybe lending. So originate a loan or underwrite an Insurance policy, or maybe think about whether or not I can do business with this customer? Is that customer, is that firm sanctioned? Is that person politically exposed? Maybe I need to know whether or not I can do business in the first place. And then, as we move through the value chain of working with our customers, we help them monitor, measure risks in their portfolios, understand what risks are changing, actually come up with measurements of uncertainty using frequency, severity, stochastic techniques. You can think about our actuarial models here. You can think about what we do with Cat risk to really understand what's going on in that portfolio and help people adjust and make decisions about that portfolio to improve their business going forward. And then finally, as you move through the value chain, we often are helping them, verify, comply, provide reports to regulators, maybe provide financial statements, and help them do the calculations that are required to do that. The data that we have in our data and information unit, along with the models and the insights, and the conclusions you can gather from research and insights, are often brought together with the software applications that we create to help you do those three things across the value chain. An important thing I want to highlight is, when we talk about this, we're often talking to a couple of different divisions or departments within an organization and helping bring them together. The loan origination group is using the same vernacular, the same scoring concepts, the same software tools as the people who are doing the financial reporting, as the people who are managing and measuring risk in the middle. You can imagine there's some great synergy that we create for our customers when they use our products. If they're built on a SaaS platform, you have an opportunity to cross-sell more easily across those different divisions and bring more of our capabilities in. So that's the product strategy in general. That's the way we go to market in general. But the other thing I'll just highlight is there's a bunch of capabilities that we have that really nobody else has. So you're very familiar with our expertise and our capabilities with respect to credit. Undoubtedly, we've been at this for over 100 years, right? We were Innovators by inventing ratings, right? But credit is just where it sort of begins, right? We can tell you again, "Am I working with somebody who's politically exposed? Is this supplier at risk in a way that maybe I hadn't thought about in light of their physical locations and the climate risk that might be affecting that particular location where their portions of their supply chain reside?" Let's think about other areas of risk that we can bring to the table, like, cyber risk, right? We have an investment we've made with BitSight. We, of course, work with BitSight very closely and have incorporated some of their cyber scores into our databases and into our products. So you can look at risk and opportunity in a holistic way, understanding different dimensions of risk, understanding different perspectives of getting to know that customer, and then apply that through the value chain and in your day-to-day activities. So we are very deeply entrenched with customers because we're providing the data they use to confirm, analytic models they use to analyze, and the software they use to remember what they did. This is a very powerful combination of vertical integration to help bankers, and Insurance companies, and asset managers, and corporations, and governments around the world. So I suppose that's us in a nutshell. And what we're hoping is that when we go through our tour today, we're gonna stop at a couple of different spots along the way, that we'll be able to show you... And actually, these guys are pretty good, not just at gathering data on companies or preparing credit ratings and explaining them through research services, which I'm sure you're familiar with. We've been at those things for 30 years, but we're also pretty good at software development. We're pretty good at bringing this stuff together to help a customer do their job every day. And I'm hoping when you are at these stations, you'll ask questions about that. Why, why are we doing such a good job, and why are we creating so much value for our customers? So I encourage you to do that, and before we head off into that tour, we have a couple comments here from René, who's our Chief Architect. I should acknowledge René was a part of the RMS acquisition. So in addition to fantastic cat risk modeling capability and a great group of people, we got somebody who could help us think a lot more about architecture in a more strategic way. So René is going to talk about how we can assemble products faster if we do some work at the platform level. And then Nick Reed will, I think, finish us off here with in terms of prepared remarks in terms of talking about our product strategy, some of the ways we do product development, and then a few more comments on GenAI as well. So, René, why don't I hand over to you, and we'll get moving through the program. Thank you. Sounds good. Good morning. I'm going to talk to you about the platforming approach that we are taking at Moody's Analytics. We've created a platform organization this year with the goal of making new and integrated solutions for our customers. This will allow us to better align to Moody's growth goals, and it will also allow us to better take advantage of all the capabilities that we have available across Moody's Analytics. And we think that with an architectural coordination, we can create four key benefits. The first one is to go faster to market and to innovate faster. When you have a platform, and you have all these capabilities around you, as a software developer and a product manager, you are able to work with your customers to figure out what is the workflow that creates most value for your customers. You don't have to focus anymore on things like logging, metering, entitlements, or how to do single sign-on. That is taken care for you by the platform. You can focus on the financial things that this customer wants to achieve. Second, by having logging, metering, knowing the entitlements that the customers have and how they use their solutions, you are able to identify how these solutions better work together, and how customers are actually using them together in real life, not as how people thought they would be used. So this creates cross-sell opportunities across, various business units, but also between customers and comparing how one bank views something versus how an Insurance company uses it. Third, because we are building this platform, we are able to get much more resiliency, much more quality, and much more compliance out of the platform. The platform out of the box is SOC 2, ISO, and NIST compliant, and applications only need to take care of their concerns and their controls that they do. So if they have PII information, yes, they need to know what they need to do with it, but they don't have to worry about the PII or the single sign-on of the metering or the entitlements. And finally, we want to minimize the cost of changing by taking a multi-cloud approach. So we are really going to make it such that we can use the cloud that works best for us and our customers. We do that by using open protocols, open standards, and Platform-as-a-Service solutions that are available on all clouds. That's how we achieve that. So when I talk about platform elements, as you can see here, how do we achieve this? It's not just a greenfield situation. We have many businesses that make hundreds of millions of dollars, so how do we get there? The way we do it is we are looking at the best-of-breed solution that we have within Moody's Analytics, and then we look at how can we apply that to all of MA, not just for banking, not just for KYC, not just for data and solutions, not just for Moody's.com. That is what we call platforming, and then we create these platform elements that can be adopted by itself. So it's not an all or nothing proposition. An application or an operating unit can say, "Oh, I wanna have single sign-on already this quarter. Next quarter, I want to enroll and take advantage of entitlements, then I'll do the metering." And so product can weave in the priorities of new capabilities for our customers with onboarding onto the platform in a gradual manner, but at least we know that we do the right thing and that we're on the right path. So, when we create the platform element, the process of selecting which one it is and what we need to do and how we need to platform, we are actually taking as a whole within Moody's Analytics. And so all our technical leaders across the organization are working together in an architectural council, where we're reviewing, discussing, and brainstorming on what are the best solutions, who has the best thing to deliver, what is best of breed, and how should we expand it. That's how we achieve those goals. Now, let's have a look at the architecture diagram and pick a few of these as an example. So the front door is basically how we deliver our bits to our customers. This is something that Moody's.com is really best of breed in. They have a very efficient and secure way of delivering the data. That's the starting point of the front door platform element. What we add is Infrastructure as Code, and so now with just a little configuration, I can have a new region in either Azure or AWS, and replicate our capabilities over there. Another example is single sign-on. Every customer is able to sign on into the Moody's applications and now being signed on in all applications. What we're adding here is federated identity, which then allows our customers to manage their users appropriately. When they leave, they don't get access to the Moody's applications anymore. The third example is the CI/CD at the bottom. This is really an opinionated view on how to develop, test, operate your solutions in the Moody's infrastructure. We have an intent-based abstraction that allows us to be cross-cloud, and that's really the way how all these applications can move forward in a much more cohesive fashion.... With these examples, I hope you can see how software engineering at Moody's Analytics is transforming and improving, and most importantly, how we're improving the time to market and the customer experience of our solutions. With that, I'm going to hand it over to Nick to talk more about product. Thanks, René. Thank you. It's great to see so many familiar faces, and in New York. I've been on a bit of a global tour, so I'm still awake, and I'm still standing up. Talking about really the most exciting thing that's happening at Moody's at the moment, which is working out how to adopt GenAI and how to embed it in almost everything that we do. And we're gonna talk a little bit about that today. I guess I should welcome everyone to our product development space, because if you didn't know it, you are in it. This is one of the places where we do innovation at Moody's. And to give you a sense of kind of what that is, this physical space that you're in now is where we do innovation, ideation, product development. In fact, it was about 12 weeks ago in this exact room that we were here with engineers and product managers, and salespeople and strategists for a whole day workshop with Microsoft. At the end of that session, among other things, we talked about how we would enable our employees to be able to get access to generative technologies inside the Teams ecosystem, and we ideated on the possibility of being able to give customers access to the same kind of content, to our information, using Gen AI technologies in various channels. And so it's really pleasing to see that kind of really, just a couple of months later, the outworking of the activity that occurred in this room is now available to our customers on preview, and that you're going to be able to see it at one of the demo stations a little later on. To give you a sense of the way that we do product development in some of the other physical spaces that you're going to see, as you walk around, you're gonna do and see some product demonstrations at various sites. Hopefully, you'll get a sense. Those sites are open, they're collaborative. We make it available and accessible to all of our stakeholders, and that's really typical of the way that we do product development. We hold customers really dear to our product development process, and so we often hold design sprints, or we do feature building, where we actually have end users of our products included as part of that process. It's actually amazing having salespeople or engineers or product managers and customers all together to kind of understand need. To convert that need into features and functions, is a really fast-track way of ensuring that we make our solutions responsive and ultimately make our products sticky with active use by our customers. Our approach to ensuring that we continuously deliver value to customers is really embedded in our product development process. I guess what's interesting, given we price our products on the basis of value, it's kind of incumbent on us to make sure that we understand what drives a valuable experience, and so we orient our product development processes that way, so that we can get value into our products as quickly as possible. That's never really been truer than on the topic of GenAI. So we understood from our own experiences and from the conversations that we'd had with customers, that there was this weird kind of barrier to entry to the really widespread adoption of GenAI tools, particularly like ChatGPT. So while the models were amazing and the technology was magical, to use GenAI in practice in kind of enterprise decision-making processes, people very clearly told us they needed certainty, they needed citations, and they needed an ability to be able to access factual information. And so to derive value from GenAI tools, companies needed to be able to trust the answer. And so, again, sometimes in these rooms, we deployed data scientists and researchers and engineers, and those same salespeople, and included some customers, and we have developed an enterprise platform approach to solving that problem, leveraging some of the elements that René was talking about. That platform is called the Moody's Copilot, and it is currently available to everyone that works in this building, and the outworkings of that platform are available to our customers. It's worth noting that that approach is now baked into our product development life cycle. So leveraging all 14,000 Moody's employees, we talk about it all the time. Everyone takes great pride in being an innovator and a beta tester of our product. It allows us to push that product through its paces. We test out new features almost every day, and so we can see this, the kind of benefits of the speed of deployment that that agile approach has in practice. And the best practice is getting it in the hands of our customers. So we're really proud to announce the public preview of the first commercial product that's an outworking of that development life cycle that created the Moody's Copilot, and that's the Moody's Research Assistant. And so, again, you're gonna be able to see a demonstration of that today. Speaking of demonstrations, we thought the best way for you to understand our approach to innovation is just to show you. And so today is really about, in practice, being able to show you what, what we do and how we do it. And so we have set up some demonstration booths that you're gonna be able to visit today. It's gonna give you insight into the products, what their features are, give you some insight into how we built them, like I said, and how we implement features and how we involve customers as part of that process. I'm holding on to them. You should have received a card when you came in today. It's on the back of the- Oh, it's on the back of your name tag, apparently. And on the back are some words. Shivani is gonna go through all of the logistics. I'm gonna talk about the themes. So those themes are Collaborators, Automators, Integrators, and Innovators, and you're gonna be in one of those groups. These cards are really great thematic descriptors of what we're hoping you'll be able to see today. So when you visit the stations, for example, Collaborators, here's some things that you should look out for. So this is not just about teams across Moody's collaborating with each other or even new acquisitions that we might have made. This is about collaboration with stakeholders that are relevant, including customers. So, as an example, when you go to one of the demonstrations where we're gonna talk about commercial lending, you're gonna see an example of a product that we built by working with the largest commercial real estate bank in the country to develop a best-of-breed solution that helps them manage that asset class. What's the next one? For Automators, look out for examples of process and workflow automation or calculation information, and look for examples where we've embedded that directly in our platforms. And so that's none more obvious than when you go to the Insurance station, where you're gonna be able to see a demonstration of a platform that Insurance professional use to be able to speed up the process of constructing and managing the risks in their portfolio. What have we got next? Integration. So again, you're gonna see this everywhere. This is about primarily the crown jewels that Steve was talking about, those Moody's data assets being directly integrated into all of the decisioning workflows that we have and all of the software products that we have. And again, there's no better example than when you go to the workstation that's about KYC. This is where you're gonna see a demonstration of how we've enabled our customers to be able to undertake KYC processes embedded directly in platforms like Salesforce. And lastly, I've kind of mentioned it already, you're gonna be able to see innovation. You'll see this everywhere. That's never more true than when you go and have a look at the demonstration for the Research Assistant that we've mentioned a couple of times already. It's our GenAI tool that provides natural language interactions with Moody's research and ratings information. It allows you to see sources of that information, the citations, and it allows you to interact with our content using natural language. Some requests before you go to those stations. While you're visiting the stations, you have to make sure that you ask the Moody's people certain questions. I want you to ask them how they think customers get value from our products. I want you to understand and ask them questions about how customers interact with our products. We have some pretty strong retention rates, so we're kind of interested in being able to dig in a little about why exactly it is that our products are so sticky and why customers hold on to them. Secondly, please make sure that you note and ask questions about how we've brought our data and our analytics and our workflow solutions together. 'Cause again, you're gonna see examples at all of these stations where we've brought all of that together in one place. And so dig in, ask them some questions about how to do that. And thirdly, I want you to be able to ask them questions about AI. Again, you're gonna notice at all of these stations that it is resident in all of the products that we have and all of the platforms that we build. We've been leveraging it for over 10 years, and so it's a really great opportunity for you to be able to see how that works in action. So I think you're gonna have an amazing day. I think you're gonna get a sense of how we work and what we're working on, and I'm gonna hand over to Shivani. So, welcome everyone, and we are starting the live stream portion of today's event. So, thank you for joining us online, and thank you for everyone who is here in the room. We want to just kind of say, sadly, a few legal words, which I need to jump into since this portion is Reg FD. So, on behalf of my legal folks, certain statements contained in today's presentation and remarks are forward-looking statements that involve a number of risks and uncertainties, and actual results could differ. So please refer to the disclaimer slide at the front of the presentation that IR has actually posted this morning on our website, and it's up on the screens for those of you in the room now. I do want to mention, for those of us who's joining online, that there were some earlier sessions this morning from Steve Tulenko, Nick Reed, and René Bouw. They have been recorded and will be posted on the IR website shortly. So when we do refer to Steve, Nick, and René's comments, I encourage you to go to the IR website to check those out. And with that, very excited and very happy to hand over to Rob Fauber, the CEO of Moody's Corporation. Thank you, Shivani. And fortunately, Copilot summarized all that, disclaimer very nicely for you. I realize that I am losing the Moody's cool shoe contest very badly, but, ... Nice, some nice kicks here today. First of all, it's great to see everybody. It's really, really wonderful to have you here and, of course, many folks joining us online. And somebody asked me a little bit earlier, they said, "Rob, you seem excited." And I am excited, and I hope you are getting that from me and the rest of the team. So, hopefully, that was a really rich hour or so getting a chance to really see what those products do. Because you hear us talk about it, and sometimes you hear product names, and you think, "What does this stuff really do? And how is it leading to the results that I see and hear the team talking about?" So hopefully, you got a good chance to talk about that, and internalize that, and we're gonna have an hour of Q&A and hopefully lots of good questions. But our goal today is really simple. As Steve talked about earlier, many of you know our ratings business very well, and I always say it is one of the world's great businesses. It really, really is. Today, we're shining a spotlight on Moody's Analytics, and showing you the many different ways that we're integrating our data, our analytics, to create innovative and valuable and best-in-class solutions for what is a growing customer base. And I think you know, we have over 60 quarters of growth in Moody's Analytics. And the reason this spotlight is important, as Steve said, we get a lot of questions from investors who wanna have a better understanding, but this last year, Moody's Analytics was just a little over half of our revenues. So it's really important that the investor community and the, and the analyst community really understands this business and how our businesses are working together. And I wanna re-underwrite. You know, Steve talked a little bit about, in the morning, how we think about Moody's, and it's really important, I think, to ground ourselves. The business has evolved a lot in the last five or six years. And we have done a lot of things. And so, as Steve said, you know, it's anchored. Our business is anchored by what we believe is the agency of choice for issuers and investors, and it's a business that I worked in for about six and a half years, a wonderful business. But we also have a set of what I think of as crown jewel businesses. We have a wonderful fixed income and economic research business in our Research and Insights segment. Our Data and Information segment is powered by maybe the world's largest database on companies, over 460 million companies, over one billion ownership links, and now you see what the use of those ownership links is. And then we have our Decision Solutions segment, and we have three cloud-based software-as-a-service businesses serving banking, Insurance, and know-your-customer workflows. So, for those of you who have joined us online and didn't get a chance to see the discussions from this morning, we're gonna be posting those, and I would encourage you to take a look at those. You'll get a chance to hear how we are evolving our approach to product development and engineering. So those will be online shortly. So, today, we shared with you four primary solutions, products, that highlight what I hope you, hope you came away with thinking there's some unique capabilities within those solutions. And together, these solutions demonstrate how we deliver value for our customers. And it's important, I think, for you all to understand that because that gives you a sense of what the opportunity is and why these solutions are sticky. And at the core, and hopefully, you saw in a number of these different solutions, at the core of this is truly a vast proprietary data estate, curated data estate. And that is a differentiator because so many things go back and connect back to that. And it allows us to provide an increasingly holistic view of risk. And you may have heard me start using this phrase, exponential risk, and that is something that really resonates with our customers. And I wanna go back to what are our customers dealing with. So let's think about that. They have realized that a siloed approach, which is the traditional approach to thinking about, I've got credit risk, I've got market risk, I've got operational risk, I've got compliance risk, all managed separately across my institution. That's got some real limits. Our customers are seeking a greater breadth and depth of understanding who they're doing business with, who they're making a loan to, who they're investing in, who they're underwriting an Insurance policy for, who their customer is, who their suppliers are. And a major theme with our customers is this idea of developing a 360-degree view of who I'm doing business with, and that presents a huge opportunity for us. So to capitalize on that opportunity, I wanna talk about four things that we are doing that have accelerated the growth over the last couple of years, and that are going to continue to do so. So first, as you've gotten a very good sense for, we've just got a much broader range of solutions to sell to our customers than ever before. I'd like to go back to 2017. I think that was an important point in our history because that was the year that we bought Bureau van Dijk. That's when we got this massive database on companies.... We believe that that was going to be very important to power a wide range of risk assessment use cases over time. A much wider range of solutions, and we're now able to help customers, as you, as you saw, think about climate risk and how to think about the physical risk of a changing climate and the financial quantification of that. How to use cloud-based workflow orchestration to think about onboarding customers much, much more quickly and more effectively. It's not just about managing risk, as you heard Keith talk about. Typically takes a long time to onboard a corporate customer. We're cutting that time for our customers. Since 2017, there has been a very meaningful growth in the kinds of solutions that we can offer to both existing customers and new customers, and that's very, very important. Second, you heard René Bouw talk about this. If you didn't, I encourage you to pull up the module. We are platforming MA, and we have, I have to say, we have already moved in the rating agency in MIS to one CI/CD platform so that we have a more consistent tech stack for all of our new app dev that's going on in ratings. We have a wonderful development hub down in Charlotte. Cat is part of our MIS tech team. She'll talk about it a little bit more. But in MA, you heard from René Bouw, and what we didn't tell you is, René, you have over 20 years of experience at Microsoft. René manages a team in Seattle of over 50 people, and he is responsible for developing our overall technology architecture blueprint and building out the platform engineering layer for Moody's Analytics. And what you heard René talk about is that that application and data layer is moving to more universally available analytical components and datasets so that we can deliver them seamlessly across more and more solutions. And hopefully, you're seeing what the potential for that is. The architecture and platform layer houses these common engineering elements that René was talking about, and those are increasingly being used across our MA solutions. That creates a set of capabilities and services across user experience, application development, entitlements, which is very, very important, APIs and datasets. Again, why is that important? It enables faster application development, a much better user experience for our customers who use multiple products, and it's also going to give us better insight into customer behavior, more pathways for content monetization, and that is going to help us cross-sell and upsell. Third, a move to more solutions-based selling. We have made some very big investments in our sales organization over the last couple of years. You've heard us talk about that on the earnings call to help us to accelerate growth. That has included not only hiring more salespeople because we have more solutions to sell, but also building out the capabilities of the sales organization. Helen is our global co-head of sales. She's here and can talk about that a little bit more in the Q&A. One of the functions that we've built out are called Industry Practice Leads. These are folks who used to be customers, who did exactly the jobs that our customers are doing, and can help us then sit down with a head of Financial Crime Compliance or a supply chain manager and talk to them about their workflows and how they can leverage our solutions in their workflow. That's really, really important as we think about how to then as we think about not only retention, but upsell. We've also built out a Customer Success organization. We had a much smaller Customer Success team. Somebody had said to me earlier, "You know, people are busy. How do they get the most out of our solutions? That's why Customer Success is so important. It helps our customers make sure they're getting the most value out of our solutions, and that drives retention, and that drives upsell. And you are getting a much better sense that there's much more that Moody's can do for our customers. But there's a lot more work for us to do around that, so we are refreshing our approach to branding and messaging. We are going to be broadening our brand umbrella and the public understanding of our capabilities to give us some tailwind. All right. And fourth, as I know you've heard us talk about a lot lately, we're harnessing AI, obviously, to accelerate the digitization and the automation for our customers. That is really, really important. Two things we hear from customers all the time: "I need to be more effective and more efficient," both of those things. And we are building on a very powerful foundation of high-quality, curated, proprietary data and very deep domain expertise and insights. So GenAI allows us to bring together a wide array of risk domains and content sets to really deliver on what Steve was talking about, that multifaceted view of risk. So we see that as a huge opportunity. It's a huge unlock, and we're able to deliver that through an intuitive natural language interface. Really, really exciting. That's going to drive a step change in effectiveness and efficiency for our customers. And you saw Research Assistant…. That's just one glimpse of, I think, what our customers have in store for them. So it's a very exciting moment for the firm. As you heard earlier from Nick, we have deployed our GenAI tools to all 14,000 of our employees. We call them 14,000 Innovators. Somebody told me the other day, "That's the most inclusive thing that we've ever done at Moody's." I never thought of it that way. It's pretty neat. But from every corner of the company, from our engineers, to our analysts, to the folks in our finance team, they have access to these tools. And I have to tell you, every single day, I have somebody tell me how they're using the tools to become more effective and more efficient. In fact, somebody just told me on the elevator on the way up here this morning. We've had hundreds of ideas, and we have a process for inventorying all of those ideas and then thinking about how we wanna act on those and sponsor those. Over the last two months, I think we've had, Nick, nearly 200 proposals. I mean, it's everywhere. It's fantastic. We're now working with customers in preview mode with Research Assistant that you all saw, and preview mode means that we're ideating, we're testing, we're iterating, we're getting feedback from them. What's valuable? What's less valuable? What do you wanna see? And the feedback from the customers has been very encouraging. How could it not? I mean, you, it's not hard to imagine how much more efficient and effective you could be with what you saw. So I hope that today has helped, those of you who are here this morning to really see and understand Moody's in a new and exciting way. That's my number one goal for today. If you walk out of here and say, "There's something different, and there's something exciting going on at Moody's," is I think our customers think that. So with that, I'm gonna turn it over to Shivani, and we'll start with the panel. Thank you. Thank you, Rob. I'd like to invite up to the stage, we have Nick Reed, MA's Chief Product Officer, Cat Tucker from the MIS Tech team, Helen Rider, who is the Global Co-Head of Sales for MA, Steve Tulenko, President of MA, and of course, Rob. So the format for this Q&A session is, I'm just gonna ask one or two questions before we open it up to the room. Because this is live-streamed, I would ask that you wait for the microphone, so that way, we can ensure that those who are listening online can hear your question as well. But starting, if I may, Helen, with you. Yes. You're the Global Co-Head of Sales, frankly, for the MA juggernaut machine—that is, that is there. We've been talking a lot about some of the AI products that we've been previewing with customers, and I'm curious, from the customer perspective, what are you and your teams hearing? Yeah, I should probably start by saying, as you know, that AI isn't new to us. We have hundreds of customers today using AI incorporated into capabilities such as our screening for financial crime or spreading of financials for companies as part of our lending solution. So it's not new, but generative AI is absolutely a game changer. Like you said, Rob, when you think about the fact that our customers today are navigating increasingly complex, interconnected risks and trying to increase their sort of organizational resilience, and the bringing together through GenAI of our proprietary data assets, and our risk insights, and the tools that we provide, is really, really providing a lot of value and excitement. We had the preview or the demonstration in the examples today of the Research Assistant. And again, Research Assistant, if you think about it, you've got a very powerful LLM taking in, you know, the vast proprietary data assets of Moody's on top of the world's leading credit research platform. And customers are really excited. We've been speaking to many of them through... And inviting them to previews. One customer was on holiday, and actually asked if they could join the preview from their holiday. And one example, we were meeting with a global financial institution, a Tier 1 financial institution, a ratings advisory team, and typically, they are some of the heaviest users of our CreditView platform. And for them, the operational efficiencies were very, very clear. And what they meant by that was, you know, they could see how they would be able to work faster and smarter, in fact, onboard more customers more quickly and increase their value to the organization. And what they also said was that, and very importantly, they could do that in a secure way with data that they can trust, which is really, really important. And it isn't just... And it's not just, Tier 1 financial institutions. There are many different workflows, use cases, types of organizations that are seeing the efficiency and also competitive advantage that can be leveraged, from using these tools. So I can go on many, many examples, but lots of excitement. Surely, lots of excitement. Thank you. Thank you, Helen. Yeah. I know today is about MA, but, you know, MIS is such an important part of the company and our journey. And, Cat, you're at the forefront of seeing the kind of integration of GenAI, and we'd love to hear from the MIS side of things, what's been going on? ... Yeah, so the most exciting part about this journey is within MIS, we're actually using Copilot and AI today. So as part of the 14,000 Innovators, it's deployed to all desktops. So I—you know, we're managing a queue of ideas that are coming in on a regular basis across our analytics, across our technology teams, across all of our operations teams. Everyone is accelerated by the use of gen AI. It's very, very real for us. So that's the exciting part, is it's happening now. The other part is we're thinking about ways that we can evolve how efficient we are in what we deliver to market. So as everyone said earlier, MIS is a business that is long-standing, and we deliver the ratings and research to market. So how do we make that better? How do we get a better customer focus as well? As we think about our workflow, we think about our customer engagement, how can we be more intelligent? How can we get things into the queue and faster to the customer by looking at our workflow and what it takes to produce the products to market? How do we access that vast data, not just in MIS, but corporate-wide, to get put that in the analyst's hands so that they can do more scenarios per minute or scenarios? So something that may take a couple of days to do a couple of scenarios, maybe we can do it in minutes. So what we ultimately deliver to market is the data, how we accelerate insights and modeling, and so what... You know, how we employ that technology to do that, and then ultimately, how we deliver that to market. So when it comes to the ratings and the research, how do we think about research differently? So if we produce a lot of valuable research. The customer can maybe think about ways that they want to query us in their own research. So how do we think about our, those types of products ultimately delivering to the customer, higher, you know, higher cycle time, but also comprehensiveness and scope? And so that's how we're thinking about in MIS. And again, just having it on our desktops and using it today is creating such an accelerant in ideation within the organization that it is, it's just so exciting. Thank you for that, and I'm happy now to open it up to the audience. My team is floating around with microphones, so please raise your hands and someone will come to grab you. So I think it's between Heather and Toni for the first question. So if you... Thank you very much. Heather Balsky at Bank of America. Can you talk a little bit more about the customer education process and, and the branding efforts that you're planning to execute on? Just what do you think customer awaRenéss is today, and, and how do you get them to the... How do you build on that? Thank you. You take a shot? Yeah, go ahead. Yeah. Yeah. I would say, customer awareness in the financial services sectors is very high. We have a tremendous brand, one could argue, one of the most important and most valuable brands in the world of financial services. If you talk to a bond market participant or a credit and lending participant or perhaps an Insurance professional, they tend to know something about us, so I think it's very high. I think the other part of the story that's also really interesting is that we're finding increasingly important and really good growth opportunities from other sectors. If I say the corporate sector, that's sort of a rough way. Maybe I should say all of the corporate sectors, and then all of the different ways in which we have users in the government entities that we work with as well. We're seeing some really good growth rates coming from the corporations and the government customers that we've been acquiring over the last couple of years. So there may be some more brand work and awareness work for us to do among those people that might not have financial services as their initial vocation. You know, the reason they you know, they came out of college, they started in financial services. I think a lot of people understand us there, and I think there's some great opportunity for us in the other sectors as well. I don't know if anybody else has anything to add. Only because we spoke about it earlier, Heather, but if you're talking specifically about GenAI and education around GenAI, what we're noticing is the conversation we're having with customers follows a pretty similar path, which is: "Our company banned the use of ChatGPT, so how exactly is it that you're doing this? Why are you allowed to? Aren't you regulated like we are? Yeah. And so partly what we're explaining is the journey that we've been on in our ability to be able to build a safe and secure environment and to be able to explore these kinds of technologies. So as much as the actual output and the product, they're also talking about the process that we've been through. And so we're kind of educating people on the way that we think about it, educating people on the way that we undertake kind of innovation activity, particularly around gen AI, and that's driving a much deeper conversation, which then invariably leads to: "Okay, so how do we get specific exactly about that? What tools, what technologies, what processes, what companies did you partner with, and how can we explore this together? I think it was Toni. Toni, next, thank you. Thanks so much. Toni Kaplan from Morgan Stanley. I was hoping you could talk about attracting the right talent from the point of view of you're trying to do a lot of innovation here, just managing and also attracting. Is it you're now going after different types of employees that maybe in the past, it might have been a little bit more skewed towards financial services, maybe now more towards technology? And, and just how do you get them to say, "Hey, I really want to go to Moody's, you know, not Google," and things like that? First of all, you get them in this room. ... Our Chief People Officer is here. So Maral, if you would like to comment, I'll hand it over to you in a minute. It's a great question, Toni, and I think in some ways, we're like a hidden gem. You know, as Steve said, a lot of people know us, but they think they know us for certain things. And when people really get to know us, they find that this is a place that is really intellectually rigorous and curious and is about integrity, and... But also, people are starting to realize, there's an energy here, and we have a very, you know, we still have leaned into a flexible work environment, and we're dealing with now cutting-edge technologies. And I think a lot of folks are saying, "I need to take a deeper look at what's going on at Moody's." Look, that's really important, right? Because René's got to be able to attract world-class talent to be able to do the platform engineering that we need, you know, we need to do. So this - I would say we are on a cultural evolution of retaining what's great about this place, but modernizing it and freshening it up so that we can get that talent. Maral, is there anything you want to add? I don't want to put you on the spot, but you're here. Yeah, I'm here. So happy to. Maral Kazanjian, nice to meet you. I think Heather asked a question about our customers knowing us more deeply. I think the same thing is true for talent in the market. When you come to Moody's, we have a tagline: We want you to come and stay, and that actually turns out to be true. We have very long tenure. We have a lot of people that go and boomerang back. But I think our secret sauce or our challenge and our opportunity is people in the market knowing about us and knowing about us as a place for developers and a place for financial analysts. And that's the mission that we are very aggressively on. As the business is modernizing, we are working alongside them to tell the story of who we are to the talent in the market, and we're excited about it. Thanks. I think there was a question over there, Matt, just in front of you. Thank you. Ryan Griffin from BMO. Just on the competitive front, I know you pride yourself on having a more competitive, comprehensive offering compared to some other vendors. I was just curious who you would say your biggest competitors are and whether they differ, and you're coming up against the same people across banking, Insurance, and KYC. We could have three of us answer this question. All of us can answer this question. So, this is a tricky question in that most of the people that we compete with don't do all of the things that we do and have trouble offering the same vertically integrated solutions. So there are competitors out there that provide data, and there are competitors out there that provide analytic tools and maybe some that provide software capabilities, but very few bring those together with a domain of expertise in mind in order to solve a problem for a customer in the same way. That said, if you look at the Risk Tech 100, that's published by Chartis, which we've certainly mentioned over these meetings before, that's a good indication of names that we often see out there, and you'll note that we're number one on that list, by the way. At least that's worth knowing. But, you know, from time to time, we'll see names on that list and compete with them. If you go into the commercial real estate lending space, there's a couple names that you probably have heard of. They might even be issuers of equity out there that we might see from time to time. If you go into the commercial lending space on companies, the same thing might happen. They might not be the same names. So I could give you a laundry list of names, but I think if I were to try and tackle each of the solutions, we'd end up with five on each, and it would start to not overlap much. Not overlap much. I don't know if you have any other comments you want to add there to respond to that, Nick? I guess it's kind of reinforcing of why it's so important to platform the business, because we like, and our customers appreciate the kind of best-of-breed approach that we have. And, and so what we're attempting to do is build a best-of-breed solution in lots of different vertical markets. And so our ability to do that efficiently and effectively is kind of based on our ability to platform the underlying capabilities. So I don't think of us as being a generic software house or a generic data house. We're a very specific best-of-breed solution provider in each of the vertical spaces that we operate in, which is why we have so many competitors, and that's really underpinned by our ability to have a platform that drives that efficiently. Yes, sir. Yeah, thank you. Jeff Meuler from Baird. If you had a large customer come to you in a couple of years or within the next couple of years and say, "We're investing a lot on our own in terms of GenAI, GenAI capabilities, we want to apply that to Moody's content," what is the answer versus protecting your IP and trying to sell your own GenAI capabilities as a value add? Yeah. Or at what point do you bring in customer data and/or other third-party data sets into your GenAI capabilities? Thanks. Yeah, well, I mean, we're actually tackling that today. We're working with customers to be able to build shared platforms and solutions that leverage the best of large language models, the best of Moody's proprietary data, and the best of the data that's owned by the underlying counterparty. And so part of the reason why we're so advanced in this conversation is because it was necessary for us to build those components inside the organization, because we have a regulatory requirement to do so, to be able to differentiate the ratings agency from the rest of the company. And so our approach is to try and build a flexible enough platform that has an ability to be able to onboard different data sets from different components, keep them physically separate, allow entitlements across those data assets, and provide answers that are conscious of those different data assets. So we can get into the technicalities. We have a pretty sophisticated model, garden, and library. We have a sophisticated RAG engine that allows us to be able to retrieve documents from different places and to be conscious of those different places and provide an entitlements infrastructure that allows us to do that. And that is intriguing to customers. The level of interest that we've seen, not just in buying our solutions, but potentially having access to the platform itself, is a new revenue stream that we're really interested in. We're working very actively with customers to work out how we might bring that to market. Just one more comment there, Nick. The array of content that we have available here is extraordinary, right? Within, we talk about coverage of 470 million companies. We're not talking about one line for each company. We're talking about thousands of data tables on those companies, right? You have extensive data we can talk about with respect to climate models that generate new results on top of that data to help you understand what the implications might be. We can do this in each of the disciplines that we've been exploring. Economic data, we have time series data around macroeconomic statistics that literally go back for decades. We have historically structured these data sets so that you can use them in ways through using these GenAI tools that just are not available if you don't have them structured. So when we go to see people with a Research Assistant, and they say something like, "Well, what would the impact of interest rates be?" We could use the LLM to answer that question, or we could train and bring in all of the economic data we have related to interest rates for that particular economy or that microeconomy over the last 50 years, and start to inform with that as well. So you can imagine the cross-selling opportunity that gets created here. You sort of very, smoothly, seamlessly introducing other capabilities that Moody's brings to the table and bringing them, bringing those to the fore so that the LLM can leverage them as well. You can imagine, and I would imagine almost everybody in this room might like to use the research that you've written, the documents you've prepared, and your firm has prepared before, in order to help inform and leverage those same LLMs, maybe with the, "What would Moody's say about this part?" Join together. So that's the kind of thing we're talking about with customers. It's our vast array of capabilities, sometimes our content, sometimes engaging our models, and then maybe let's bring in their array of capabilities or maybe another one, a third party, so that they can really start to do their work even better. And we think we're on, we're on the forefront of helping people think that through. I think we had a question from you, Faiza. Thank you. Faiza Alwy from Deutsche Bank. So, Steve, you started to address this a little bit, but I wanted to talk more about the platform strategy. Like, where—what's the vision in terms of where do you see yourself over the next three to five years? Not in terms of, you know, financial targets or anything, but what, what do you think Moody's Analytics is going to be? Like, bring to life for us some- Yeah ... Examples of, you know, where do you see- Yeah -Yourself? And, you know, are there things that you're considering, what-- is there something else that, you know, Moody's might need, whether it's, you know, a different distribution platform- Mm. Or, you know, incremental data? Mm-hmm. Like, is there more that might be needed to- Yeah. Okay. Yeah. So let's use this word platform, and then let's add, let's make it the infinitive, right? There's a concept of platforming that I think is relevant here, and it extends across technology and data, especially. Because if you have interoperability so that you can bring things together more effectively, you can assemble value propositions and assemble products more quickly for our customers. So we're platforming, in many ways, our technology, so that the engineering teams that really know their customer sets. Let's say it's the engineering team that sits in the Banking group. They know and understand the workflows that are most relevant to those financial institutions, and we organize our engineering team so that they're close to the customers, often in the meetings with the customers. As you move away from the customer and toward the center or toward the foundation, we invest in elements that we think will be highly leverageable across multiple groups. So the investments we're making, these are, depending on who you talk to, extremely nerdy or really cool, right? But they're, they're investments that often happen below the floorboards to help make these connections really easy and help us track and understand what the customer dynamics are. So a great example would be, and René talked about this earlier, you know, the idea of logging usage patterns in a central way so that you can see what banking customers are using when they're working with a credit memo that Avi showed you earlier, or maybe when they're looking at the credit memo capabilities we're bringing in with the Research Assistant. Let's log those together so we can see how we might be able to develop that product together using all those capabilities at once. So that's the sort of thing we're spending time on right now, is investing in this, I'll call it, infrastructure from a technology perspective, to make the lives of the engineers easier, enable them to think more about what the customers want, and then not worry about some of the stuff that you-- all, all of them have to do, so that we can get some efficiencies there. Importantly, we get out-of-the-box compliance, out-of-the-box reliability, out-of-the-box redundancy in the systems as well. So that, that's the technology side. For the rest of it, we're also platforming the use of the data. How can you make sure that all that content we have on all those companies in the world is available through all and any of the products? Maybe you could think of the sales group as another thing we've platformed. Our sales group is organized centrally in order to line up around customer segments. ... And make sure that we're responsive to them, and we coordinate it centrally so that we have a singular set of approaches, and we have strategies that we can apply. In a lot of ways, we've platformed that activity also. Hopefully, that's helpful, give you a sense. I think we have another question, Christian. We have a question from you. Morning or afternoon, not sure now. Christian Bolu, Autonomous Research. So two questions, I guess, are related. I guess most of your competitors and most folks in financial services are still in the early stages of thinking about how to use GenAI. You guys are already potentially bringing something live. So just curious about the journey to get here. How are you able to be so fast in terms of, is it the tech platform, decision-making in the organizations to be able to push this through quite quickly? I'd love to just hear the broad journey. And then the second question is on monetization. Obviously, super early here. This is super exciting. We get, you know, we get the long term, but just any early thoughts on how you're thinking about monetization? Is it just giving you more pricing power or something that you explicitly charge for over time? Mm-hmm. Yep. Let me take the first, Christian. So, you know, I was in a room full of investors months ago when GenAI just kind of came onto the scene, and I got a lot of questions about margin. And I kept saying: Well, yes, we understand we can be more efficient, but we're really focused on the unique power of this technology for our customers and how it can create a real differentiation for us in how we serve our customers. I mean, isn't that... That's much more compelling than just thinking about grinding away on costs. I don't want to minimize that, but you've got to start with the customer and what the customer opportunity is. So we all aligned around that pretty quickly. And then we said: How are we going to rally this organization, right? Because as Steve or Nick said, you know, lots of folks, not just in financial services, but the first approach was we're going to get a team of lawyers, and we're going to look at this for the next nine months, right, and think about all the risks. We had three town halls. We put the whole firm through a set of training. We had the town halls and said, "There are going to be three principles that this firm adopts around generative AI. The first is we're going to have 14,000 Innovators," and that's critical because some group of 10 people in a lab could not possibly keep pace with what's going on with GenAI. It's not possible. So we wanted to rally the entire organization. That was first. Second, we said, "We're going to take a yes, and mentality. Not no, because, yes, and." And third, we're going to deliver impact. It's not just about hobbies and neat stuff. At the end of the day, we have to deliver impact and value for our customers. And we said to the firm, "This genie is not going back in the bottle. It's a transformative technology. It's exciting." And then we announced the partnership with Microsoft, and I have to tell you, Christian, it has unleashed a torrent of engagement, enthusiasm, and innovation around the firm. And we just decided that, you know, we were gonna, we were gonna. I actually said to our board recently, I said, "This is a bit of a cultural watershed moment for us. Mm-hmm. It really is. Mm-hmm. Right? We decided we were going to be early, and we were going to figure out a way. And one wonderful thing about Moody's, and I love about working here, it's a lot of really smart people. And when a lot of really smart people realize that the mandate is to figure this out and deliver it for our customers, guess what? It happened very quickly. Can I make a joke? If it's funny, you can. Hopefully. I've been working here a little while. I worked here long enough to remember the day when I came back from a meeting in Chicago, and I said to my boss, "This thing called the World Wide Web is pretty amazing." And my boss said, "Ah, it's just a fad." And when Rob and I talked about this for the first time, fad did not come up, right? No. It was immediate acknowledgment that this is generational. This is a major, major innovation that is going to affect us all in a way that we don't even understand. The pace of change was so amazing that we have just said, "Let's get in." I think that's the biggest difference. The leadership has just said: Let's jump in. You want to talk about monetization? Yeah. Before that, I was just going to jump in on one point. We have been working with AI for decades. Yeah. We have some really phenomenal people. So Sergio Gago Huerta is a quantum computing specialist, so we're not just thinking about technology of today, we've been thinking about technology of the future, and I think that kind of forward-looking- Mm-hmm kind of approach across the organization positions as well. But I just wanted to- Yeah, and I, I'll, Let me come back on monetization, and, and feel free to, to, chime in, but I'll start with Research Assistant, right? So we get the feedback from the customers that they think it's valuable. Mm-hmm. Then, as you've heard me talk about on earnings calls, we talk about how do we price behind value when we're delivering additional value? And so there's different ways to do that. We could do it à la carte, as a separate offering. We could do that as a price increase. You know, there's a variety of different things, and so we're engaging with our customers to understand, what do you think about it? How are you going to use it? And that informs how we're going to price it. That's one part, but then, two, you saw the questions. So let's say one of the questions is, I want to understand, which auto manufacturer has the most exposure to climate change with their production footprint and supply chain? You might be able to answer that with Research Assistant, but you might not be entitled for the content. Perhaps you're just taking our credit research. Maybe you need to be entitled to some of the climate content from, you know, the RMS platform. That's why what René was talking about with building out those entitlements across the platform is so important, because that is going to speed the monetization- Yeah. of those additional content sets, as our customers say, "I wanna bring together credit and climate." Right? And then the other part, Christian said, "That's Research Assistant," but then we're gonna do the same thing in the Insurance Solutions and the same thing in the banking solutions. Different content sets, different ways of integrating it into the workflow, and that will give us an opportunity, just like Research Assistant, either a la carte or pricing increases. Yeah. Just an interesting observation from the customer engagement side is that, you know, we've reached the point in conversations with clients, and they're looking at the tool, and they're previewing the tool. They're getting to the point where they're asking: "How much does it cost?" Right? So getting to that point indicates the value that they see, and they're ready to move to the next stage. Mm-hmm. You know, it's there. Sorry, we had Andrew at the back and then Alex at the front, so. Thank you. Thank you. Andrew Nicholas with William Blair. I wanted to build off some of your responses there. I think from going around the tour and just kind of reading and speaking with you on GenAI over the past couple quarters, it does seem like maybe two of the bigger opportunities here are cross-sell and up-sell through the natural language models themselves, but also the ability to kinda cross-germinate products with all your different data assets. And so with all that, kind of as a precursor to my question, I'm just wondering, does that change how many different assets are attractive to you for an M&A perspective? Does it become easier to monetize, you know, different firms that you might want to acquire and uplift their own revenue growth because of GenAI? Just curious- Yeah. if it changes the M&A strategy at all. I'll just do a quick start with that. And by the way, our ability to cross-sell and up-sell is not going to be just dependent on GenAI. I mean, what you saw here, like with CreditLens, you could see all the different content coming through. It gives us all sorts of options to think about, "Is this content gonna be included in the overall package?" And gives us the opportunity to increase price, or, "Is some of this content going to be a la carte because certain customers want it and others don't?" So I think the opportunity to sell into the existing customer base is a meaningful one and at the heart of our strategy, right? We've added a lot of capabilities, and so it's this land and expand concept at work here. GenAI is going to be, I think, a very valuable tool for us, but it's not the only tool. In thinking about M&A, you know, as we think about AI, it's got to run on data, and so I think it likely means that proprietary data sets will be at a premium. In a way, they always have been, but it's even more valuable here, and you asked a—you brought up a great point. Likely, what we are—when we're thinking about the most attractive content to us, we're thinking about what can we monetize through the four different platforms you just saw? How can we sell it four different times, six different times? Yeah. So, you know, I think that will be very much how we think about it. There's a related topic here that's not specifically about M&A, which is really about strategic partnerships and distribution patterns, I guess, as much as anything else, because what we're noticing with the use of GenAI is people are interested in being able to ask and answer questions wherever they are. And so invariably, that might sit inside applications that are outside of our world, and so we're building our products and platforming our products to be able to make that happen. And we're really in the market, working out where is the best place for our stuff to be distributed, and where are our customers most interested in being able to consume it. And so, that also might drive differences in the types of people that are interacting with our products. So, GenAI lowers the barrier to accessing sophisticated content. Yeah. That's the way that I think about it. So where you might historically have been a deep analyst, logging into a platform, undertaking a really specific activity, you now have a broader set of people that are able to interact with our content that might never have interacted with it before. And so, as well as being able to cross-sell and up-sell, we also think there's just a huge opportunity to say: Actually, are there more people at the organizations that we currently sell to that can use our products, or are there different kinds of companies that we've never sold to before that now have a lower barrier to be able to access our content? I wanna come back to one thing. This is a great question. I wanna go back to the call we had with investors when we announced the RMS acquisition. I got a bunch of questions: "Why are you guys getting into the cat modeling business? Mm-hmm. And I said- Mm If it was just about cat modeling, we wouldn't be on this call." There are two reasons that we bought RMS and spent $2 billion of the shareholders' money. First, we wanted to move at scale into the global P&C industry because we felt there was a lot of our content, a lot of things that we could do to better serve that industry. You saw some of it today 'cause you saw ESG for underwriting. I was with the global Insurance industry. I probably did 30 customer meetings over the weekend. It was a big event in Europe. We announced a consortium with Munich Re, and Gallagher, and BitSight to develop tools to support cyber Insurance. We similarly announced something today around helping the industry around greenhouse gas emissions. There's a lot that we can do together with RMS and our capabilities at Moody's to really grow and better serve the Insurance industry. So that was a cross-sell concept. And then in reverse, there's a lot of customer demand to better understand the impact of a changing climate and the financial consequences of that, the physical risk relating to extreme weather events. Mm-hmm. So banks have realized that when they're underwriting a loan, it's a 10-year loan and a one-year Insurance policy. They may wanna understand weather risk, climate risk. The same is true for our corporate customers who wanna think about supply chain. The same is true for our government customers, who are spending billions of dollars on climate adaptation and investment in climate resilience, and they wanna understand the impact of that. So we thought that the climate and the weather and the catastrophe modeling capabilities and deep, deep expertise was really valuable then to be able to monetize across a whole range of Moody's solutions. That's why we did that deal. So, Alex, at the front here. It's time. Hi, Alex Hess, on Andrew Steinerman's team at JP Morgan. Quick question on platforming MA. Can you maybe give us a sense of where you are in that process? Our understanding is there, there is a tail of customers in banking and Insurance that are sort of on legacy, on-prem solutions, and it feels like a lot of this, you know, unlock here, that you guys are... And a, a lot of this innovation really rests on customers moving from on-prem solutions to on the cloud. And then, yeah, maybe I'll leave it there. We have been offering hosted or SaaS-based solutions almost. I wouldn't say exclusively, but anytime we've launched a product since 2015 or 2016, every one of those products was made available in a platform that was hosted or SaaS. And I would say, as we became more and more sophisticated with cloud-native architecture, we have essentially moved to that 100%. There are still some customers, especially in places like the Middle East and in Asia, where we're just now breaking through some of the cloud apprehension, so we had to deliver some of our capabilities in an on-prem way. So they're sort of the exception to the rule. But it's really a relatively small number these days. Most virtually all of our growth comes from a SaaS-based revenue source in every case. Mm-hmm. So platforming, when I'm talking about it a little earlier, I guess what I'm saying is, we have a tremendous SaaS platform built where a unified data mart is available to support bankers across multiple workflows in order to bring that value chain together that I talked about earlier. Originate a loan, measure, manage, and understand the risk in your portfolio, and then maybe report out to the regulators. That's a great example of that value chain. The same data gets shared across all three. You can imagine how sticky it is if the same data gets shared across all three. The same credit scoring tool gets shared across all three. The same workflow operation gets shared across all three. There's a platform concept. The stuff René was talking about is actually below that, even more foundational, so that we can share some of the componentry that is available and necessary to run a banking software as a service platform, and an Insurance software as a service platform, and then maybe another one. So that, that's really what we've been, that's what René was talking about earlier, this concept of there's some foundational elements that if we invest in them, a lot of this stuff is abstracted away, so the end user... Sorry, the endpoint, the customer-facing engineering teams, right? The ones that really need to understand what the bankers need, they can concentrate on UI and user interface and analytics that really make sense to them and skip the part where you do the logging. That's, I think, what we meant mostly by the platforming earlier. Just conscious of time and want to kind of wrap up and thank the panelists and thank everyone for joining us today. I hope you found it informative. And, Rob, I don't know if you just want to say a few closing remarks. No, I'm gonna keep it very, very short. These were some great questions, so thank you very much for this. Thank you for the time today. Thank you for actually coming here, and those of you online, thank you for spending the time to dial in. I hope it was useful, and please give us feedback about the experience here. And again, I hope that we achieved our primary goal, which is that you understand that something a little different is going on here, and that we're pretty excited about it. So thank you very, very much for coming today, and we'll be around for a little while to ask any further questions. Thank you.