All right, our next section is with Moody's. First of all, thank you everyone for joining this section. For those of you who don't know me, my name is Owen Lau. I cover information services, exchanges, and also blockchain at Oppenheimer. Moody's is well known to be a credit rating agency. Over the years, they have been building out a software business in Moody's Analytics. They're also making a big push into AI. In the first half of this year, Moody's Analytics accounted for about 46% of total revenue. Joining us today is Steve Tulenko, President of Moody's Analytics. First of all, thank you for your time, Steve. Thanks, Owen. Thanks very much for having us. Pleasure to be here. Look forward to a good session. Exactly. Good to see you, Steve. We asked this question last year, but we have been hearing quite a bit about the AI adoption in the investment community recently. I'm wondering how you would see your software business in MA today and where you want to be in five years. Yeah, this question we get a lot, right? I think maybe the first thing to note here is software is a part of the program. In a lot of ways, it's a chassis that we use at Moody's to deliver content and deliver analytics and deliver insights. Sometimes the software is there to create the ability to interact in a way where we can bring our expertise to the table. We often use it and have used it for years as a way of delivering value to customers in terms of their lending operation, for example, where we bring the data on the companies that they might lend to the table, maybe to help them do prospecting. We also bring data on maybe the companies and whether or not they might have political exposure or their beneficiaries, owners might have political exposure that we might bring to the table to help them decide whether or not they can do business with that company at all. We use software to deliver that. We also have analytic models to help them evaluate and maybe decide whether this is a good risk for them, what the price might be, how they might structure that deal, and maybe how they might think about doing business with that company in the future. The software is the way in which we record those decisions and record that capability, creating a database often that's a system of record that we can rely on and refer to in the future as we do more analytic work. I think of us really as an analytics business that leverages software as a chassis. It's really a way in which we might package the expertise that we can bring to the table to help the customers do their jobs even better. Got it. Is there any area that you could invest into more and accelerate the growth in MA? Yeah, I mean, there's always investments we can make, especially in light of what's happening here with AI, right? We've been pretty active about investing in those areas where we see the best growth opportunities. Sometimes those in the form of data tools, or sometimes those are software applications or companies that produce software that we thought would be helpful. Sometimes it's just internal development of those kinds of tools, especially leveraging some of the new AI capabilities. The lending space is a place where we are very focused and see some really good growth trends, either because banks continue to digitalize their activity or because we see, and by the way, the enumerated acquisitions are a good example of that, right, where we're bringing onboarding capabilities to our other elements in the value chain that we offer to banks to help them do their lending. That acquisition was a good example of an investment in the lending franchise for us. We do a lot in insurance underwriting. You, of course, know about the RMS capabilities where we have the preeminent CAT models in the world here, helping people understand what the risk might be of a particular climate event, a hurricane, or a wildfire affecting their book from an insurance perspective and maybe in other areas as well. We've made an investment with Cape Analytics, which was the company that does geospatial video AI work to help us understand properties and what's on those properties from an overhead perspective so that I can see, gosh, that roof right there has a patch on it. Maybe there's a branch overhanging that roof. Maybe I need to think about that insurance policy differently than I might have just based on that, which is in the public records. Of course, KYC is another place where we are investing. In general, the AI concept is another place that we think will help us accelerate growth. Those are big examples of areas where we're concentrating our efforts around an ecosystem that we can really add value in, supporting maybe it needs to consume some data from an external benchmark like us, maybe some analytic models from us, maybe some software to help streamline the process. I think of it as we're making bets on big sets of activities or ecosystems of activities that our customers are engaged in. Got it. Is there any area that you could de-emphasize on the other hand? There is always redeployment work that we're doing. We look at our portfolio very carefully, as you can imagine. Where are we on the product life cycle? You can picture an S-curve. Each of our product suites is at some point along that S-curve. At the beginning of an S-curve, your investment cycle is a little different than it might be toward the end of an S-curve. We aim to keep in the portfolio those products that generate good growth profiles, but also good margin profiles when they get towards the end of that S-curve. There is redeployment all the time. Maybe a good example of that would be what we've done with the ESG activities, right? A redeployment of resources internally and a partnership with MSCI is a good example of one where we felt the portfolio, I'll call it a rebalancing in the portfolio, made sense in terms of the resource deployment. Partnering with MSCI is a tremendous opportunity for us to bring world-class capabilities in that space to our customers. Maybe we are not investing quite the same way we were in terms of producing that content ourselves. Of course, it's a bilateral concept. We can help them just the same in the private credit space, for example, where we have great expertise in private credit. They, of course, have great tools that support the asset management community. That's a good example of a redeployment where it makes sense given our portfolio. That's good. You mentioned AI multiple times. Could you please talk about the traction of your AI products? I think your Research and Insights revenue was stronger than expected in the second quarter. Part of the reason was Research Assistant, which is embedded within CreditView, I think. I think you have over 100 customers and a healthy pipeline. Could you please give us an update on that? Yeah, I mean, the Research Assistant is a good example of the way I think our product strategy will develop here as GenAI becomes more important to our customers in the way they do their work. It's an important capability that enables us to draw inference across multiple areas of expertise and leverage it in the context of the work that our customers are doing. We're using the same technique. Sometimes we price for it independently as a new module. Sometimes we include it in the product itself. It depends a bit on where we are and which product family we're talking about. I think Noémie talked about this in the most recent earnings call, about 40% of our products, when you measure that by revenue or by ARR, now include some form of GenAI capabilities. For example, the RMS IRP, which is the platform that we offer to help people manage their models across different catastrophic risks and engage those models through that platform, has a set of GenAI tools that help you understand what the models can do, maybe understand how they work, and actually write code for you in order to implement and leverage those models in a way that makes the most sense for you. That's something we didn't actually charge for incrementally. When you look at the products that had these, I call them the product families that had these tools available in the suite, you see a growth rate that's better than the average overall, sometimes maybe even twice the growth rate. That is the other thing that we're pretty excited about, you get a very positive impact on the value that is generated by those products, as well as the potential for growth by cross-selling. Sometimes we charge for that module independently. Sometimes we just include it in the program. We see growth coming through expansion of those relationships. This is something that is, I would say, already at almost half of our product array, it is now making contributions, this capability of these new technologies. We expect this is going to permeate our product array as we move through the next 12 - 18 months. Got it. To follow up on that, I think the Net Promoter Scores, it's much higher with these clients I think you just talked about and much better cross-sell opportunities. Could you please maybe unpack a little bit more why that's the case? Yeah, I mean, the cross-sell opportunities are probably obvious, but let's just connect some dots for people. You know, as we make more content available through our website, right? Historically, CreditView is the flagship product that Moody's has offered to help explain ratings and provide credit analysis on rated companies. We're now expanding that to cover literally thousands and thousands more companies, not just the rated universe, but maybe the names that might be relevant from the private credit perspective. That coverage is expanding literally by thousands. Coverage itself is expanding, but we're also layering in more areas of expertise. For example, the economic scenario analysis that we often do, we've sold separately before. We're now making that available in the same place. You can use these GenAI tools to draw inference across all of it. The question, you know, what would Moody's say about this name? Maybe it's a name in the private credit space where we don't actually formally rate them. We can use one of our rating scorecards in order to give you a sense for what that rating might look like. We also can use economic content, maybe it's interest rate forecasts, in order to say what might happen to this company in light of that interest rate forecast. These tools give you the ability to introduce new content sets and maybe even suggest, have you thought about doing an interest rate forecast in light of this scenario? Or have you thought about applying this scenario to that credit? There's the introduction part. Of course, deliver through especially GenAI tools. In general, we can just make it available on the same website as well. There are some good cross-selling capabilities there that are pretty rich. You mentioned the NPS concept. We look at this. The scores themselves are useful, but much more interesting is the feedback you get. You get a great feedback loop from your customers there. We rely on that quite a bit. We actually have a regular cadence of meetings to look at that and make sure that we're not missing something that's grueling or something that's interesting from a customer perspective. What's really, really cool is the Net Promoter Scores are higher when these GenAI products are in place. What's really interesting is when you dive in and you look at the activity, they are spending more time on our website, which could be good or could be bad, right? Maybe it's harder to find stuff, so they have to spend more. That's not the case. They're spending more time, and they're engaging more. They're tapping more research and using more of our content than they are spending time. The rate of increase in the time they spend is going up, but the rate of engagement is going up faster. There is sort of a velocity to this that we're pretty excited about. I'll call it an engagement velocity that is pretty interesting, right? These GenAI tools are enabling people to, they're spending more time with us, which means they're spending less time with somebody else, which is great. More importantly, they're getting more from us in that same period of time than they ever did before. Yeah, I agree. I think very exciting. Yeah, I agree. I do feel like I spend more time with ChatGPT these days, honestly. Maybe a follow-up on that is, you know, AI can be an opportunity for Moody's Analytics and many other companies. It could also be a threat to your business. There has been a narrative that AI can replace some software products or some software codes. How do you see this risk to Moody's Analytics workflow tools and software solutions such as CreditLens going forward? Yeah, I mean, this is a great kind of existential philosophical question. You know, you could think about the disruption factor, or you could think about the tremendous opportunity that's before us. You know, we definitely land on the opportunity side of this. If you think back to what I mentioned before about software and kind of thinking as a chassis, AI and especially these GenAI tools, the agentic AI modules that we're now developing, and by the way, Owen, we may be able to show you, show your colleagues here a quick video to give you a sense for what we're doing next with respect to these AI tools. GenAI is just another form of software development. It happens to be that the code that we're using is often language rather than your traditional software code. Packaging our expertise either in the form of deterministic software like CreditLens or converting that into agentic software so that you do credit scoring or you do spreading or you do covenant monitoring, those are the kinds of things that we can do agentically just the same as we have done deterministically. We see this as a great opportunity maybe to actually make that capability available to more customers by helping them convert from, I'll call it software tools that were systems of record to leveraging the data that's resident in those systems of record to do more analytics. If you, I can show you a quick demonstration of the work we're doing with our website right now. We've done this on video. In the one-on-one meetings that we have scheduled today, we can do live demonstrations if you'd like. Just to give you a sense, if you're a customer of our website, of our products via our website, moodys.com, we have a module here where we have our agents that we're making available. This is just a demo version we've got in the video right now. We've got agents that help you do these kinds of tasks. We can glean insight from an earnings report. You can dive in on, in this case, we dove in on the D&O insurance work that you can hit pause for a second. The D&O insurance work that one of our insurance customers might do. Each of the boxes here represents an agent that we've already created, right? In order to help them do the underwriting work on the D&O policies. Step 68, sorry, step 22 is get information about the company. Another step two is to search through the annual report on that company to understand things. There's another step here where we review news stories on a name like Boeing. We're pulling in across dozens of agents, the content and capabilities we bring through our data estate, maybe also with content that might be in their systems, and then bring that together, hit run again, and pull that together and let these agents do some work for us. On the right-hand side, you've got those different steps firing away, right? You've got the results generated. On the left-hand side, you can see the agents going to work for us. I can show this to you guys live. This is a production website. Green means that that step's been accomplished. Blue means that step is actually being activated. Each of those boxes that we have organized through that schematic, we can add or delete, and we can reconnect the way we want to in order to deliver in a way that's relevant for our customers. The software opportunity here of deterministic SaaS-based software is actually more constrained than what we can do with this. We're actually pretty excited about the growth opportunity here. What you'll see here is we're generating content. I don't know if you can skip through it at the end. We're generating content on the right-hand side. This will generate a report that might be 20 or 30 pages long in order to help an insurance underwriter think about D&O insurance for this name. Then consider the news that might be relevant, consider the financials that might be relevant, look at the people, and then understand how they might be exposed politically or not. In light of all that, you have a much more holistic understanding of who you're doing business with and what you might want to do in terms of pricing that policy. It gives you a sense for, you can see we're very excited about the opportunity to actually use agents to do the same work that software was doing before. Maybe some of the software applications that we have serve as a system of record to help us bring the customer data in a way that's relevant to really add value to them going forward. Right. That's part of one of your agentic AI modules to hire the agent to write the credit memo and stuff like that. What you just saw there is a button that we will make available. We have it in preview with some customers. Now we've actually sold some of these agents to customers already. We have, you know, people have actually paid us money. The most important thing here is we're going to make that layer of agents available on all of the items in our data estate as we make them available through moodys.com. This is something we're in the process of doing now. We've undoubtedly been aware. We've been scaling up our coverage there, adding in more and more content there. As the AI capabilities are available across all of the things in our data estate, we expose it through this set of agents and really hone in on what's important to a banker, an insurance company, an asset manager, or whatever might be relevant. Got it. We have around maybe 20 minutes left. I do want to touch on the growth potential for MA because there are lots of questions from investors about how can you guys accelerate the ARR or the potential. For me, I think in the last quarter, you have mentioned four products. Let me let us walk through these one by one. I think the first one is the so-called enhanced capabilities in CreditLens. We know it's a very embedded product in commercial lending, but could you please unpack what that upgrades are? The upgrades related to CreditLens? Yeah, upgrades related to CreditLens and how that drives ARR. Maybe the way to think of this is, again, this ecosystem concept, right? CreditLens is the software application we use to bring customer data, bring our external data in our data estate together along with scoring models, spreading tools, right? There is an ecosystem here that we help people do lending. What's really good news is we have lots and lots of customers in the lending space. That's good. Not all of them have bought all of our capabilities. As you apply, as you become relevant for one piece of the value chain, we are offering other elements of the value chain that are quite synergistic for you to leverage right then and there. Some of those could be delivered through software. Some of those might be delivered through these agents that we just talked about. At the beginning of the value chain might be a clarification. Am I able to do business with this person by doing a KYC check? You might do your evaluation from a credit perspective and score it with one of our models. You might decide that you want to lend to that company. You might also need to project your impairments related to that company downstream in the finance and accounting department. These things all link together and create a great cross-selling opportunity that I think we think is really quite rich. We see a good pipeline in the banking space. I think partially because banks are now through some of the crisis moments they had a couple of years ago and are now also, I think, investing in this AI capability and learning more about it. I think they're looking forward to actually leveraging it to really make themselves more productive and maybe save some money. Got it. The second one is the new model launch. I think in the insurance space in the second half of 2025. Steve, you mentioned RMS. We actually haven't talked about RMS for a long, long time. Why don't you give us more color and give us an update? Yeah, I mean, the platform that RMS brings to the table here, think of this as industrial strength modeling for the largest insurance companies and reinsurers and brokers in the world. I should say for those who are interested in those exposures and can help anyone because the scale here can handle, I'll call it an industrial strength application. This thing's built for resiliency and the ability to handle a lot of activity. What happens here when you do these CAT models is you run scenarios, often thousands and thousands of years of scenarios in order to project what your losses might be. It's not terribly unlike a lot of other work we do. You have frequency and severity, and you do math to get expected losses. You do it with projections of what might happen to weather and what might happen to weather patterns for a particular location on a particular building over the course of maybe literally 50,000 years that are simulated. You need a lot of compute to do that well. This platform's built for that. Maybe more interesting, we have a whole host of models that we're releasing through that platform that we call high-definition models. The one that's most famous probably or the most interesting to, I would say, the largest number of insurers in the U.S. at least would be the severe convective storm capability where you look at thunderstorms and large, you know, wind events. What do they do to your house and my house, right? These happen everywhere in the country, happen everywhere in the world. They're relevant for, you know, the most number of insurance policies in the world. Hurricanes tend to be a little bit more important on the coast. Fires tend to be more important in sort of arid regions. The severe convective storm model applies almost anywhere in the world. We have applied it and done forecasting and simulations with it in mind. That, I guess, is why we're excited about the launch that you're mentioning there, right? Our platform enables you to really do some really good work. The high-definition models are more effective than anybody else's. We've got data increasingly available for things like the acquisition of Cape Analytics to even inform the raw data and the raw material that comes into those models to inform them and make them even better. This is a pretty rich set of capabilities, very relevant for insurance. It's also relevant, I think, for other places that are, you know, concerned about what weather might do to their assets, banks, public sector entities, et cetera, corporations too. Got it. Where should I put my warehouse? Right, exactly. Yeah. Exactly. Steve, you mentioned Cape Analytics a number of times already. I think there's an integration process going on. I think Cape Analytics is not in your ARR number yet. Correct. Not organic. Not organic, yeah. How should we think about the timeline of this integration and how much and when it will show up in your MA ARR? Yeah, the acquisition of Cape Analytics was when was that? In January. Yeah. I think after a year of being part of the company, it will be part of the ARR. Yeah, so Cape Analytics, I think we're really excited about from a cross-sell opportunities perspective. Very excited because, you know, the idea of this, I think this is a very helpful way to think of this. We can tell you about virtually any roof in the United States and any plot of land in the United States from an overhead visual perspective. Literally, I have a garage that I insured through one of our larger carriers in the United States. They happen to be a Cape Analytics customer. They insured my building for a couple of months and then decided they didn't want to cover me anymore and cited that there were some issues with my roof. This was a building I was refurbishing and rehabilitating. It gives you a sense for how accurate this kind of tooling can be, right? I had to go seek insurance from a different provider because the visuals they were able to glean from the overhead shot gave them a good sense for what they were dealing with. It's true. I had patched my roof and it was a different color. I have first-person experience with this, right? You can imagine what this does when you can actually see that at scale for any roof in America, right? Any property in America that has a branch overhanging the roof. Anywhere you see a few cars in the backyard, right? Maybe you wonder what those barrels are next to the building, right? These are the kinds of things that you can now see and you don't have to do a site inspection. It gives you a sense for the opportunity. It will move into the organic ARR number next year, probably. I guess we'll probably report that way in the first quarter. Got it. I guess the other potential support for ARR is KYC. You mentioned that maybe some new sales to corporates. Could you please talk about your existing customer mix for KYC and why corporates become a new opportunity for MA? Yeah, I mean, the history of that business, you know, we had Orbis, which was very useful in the KYC sector, right? Because Orbis brought all of the connections between the corporate entities, which was very useful. We also bought Regulatory Data Corporation a few years ago, right? That brought a database of politically exposed people and sanctions data. The combo of the two is really attractive. Orbis, of course, was very important to the banking world, but also to the corporate world. RDC had their history. They started in the banking space. We have a very good franchise among banks that are doing KYC work and supporting their regulatory compliance, as well as their efforts to be more productive and more efficient. In the corporate space, I think that this concept of resiliency has become more and more important. People are more aware of this notion of, gosh, I wonder how resilient my supply chain is. I wonder, you know, who it is that's walking into my building, right? We literally have customers that use our tools to consider who it is that's literally walking into their building at the reception desk. We have immigration authorities that are considering who it is that's flying into their country, right? It's the combination of all this data. The same concept applies whether you're in the banking sector where there are heavy regulations that require you to check and see who it is you do business with. Those same concepts, I think, apply to corporates and corporations that might not have the same regulatory situation. They might have increasing regulatory obligations, but it's early days in that respect. I think the resiliency driver is the thing that's really driving them, right? They want to make sure they understand, is there something out there that I should be aware of before I rely on this company to deliver for me? That's really the nature of the demand. By the way, it's the same as financial strength from an analogy perspective. Will they pay me back? It's the same as, are they exposed to, you know, weather? Is their headquarters, you know, in a floodplain? We might also be able to tell them about whether or not that company or those individuals have any kind of sanctions risks associated with them. Got it. Another hot topic is related to private credit. I mean, we talk a lot about the private credit on the rating side. Is there a role MA can play here in terms of offering tools to private credit firms and stuff like that? Yeah, I'm pretty sure. I think of private credit as another area where Moody's can offer a lot of value. Think about the continuum of credit, maybe going from the top of the house or the largest credit exposures or the biggest companies that borrow are often rated. Moody's has credit scoring capabilities and data on every other company in the world as well. Orbis, for example, along with financial statements that we gather through Orbis, or maybe we might use AI to spread for our customers. We have financial statements on literally 20 million or 30 million companies. We have lots of other data you might use to proxy what that company might look like compared to their peers and then use our credit models to help generate a quantitative score on basically any name that is incorporated, right? Our models reach down into sole proprietorships and provide value. At the top of the house, we talk about the rated names. Private credit is just the next slice. It's the mezzanine level just before you start doing your kind of traditional lending. Often, private credit is a substitute for some of the lending, right? Our biggest customer base is the banking customer base because of lending. Private credit is literally in our wheelhouse. We have data on those names. We have financial statements on those names. We can confirm who the people are. We can tell you what business they're in. We know and can project what those data might imply in terms of credit risk. In the private credit space, that's the one thing that you would expect Moody's would be able to do, right? We help with the risk premia associated with a debt instrument, especially when it comes to the risk associated with credit, which is a big portion of it. It's the one thing you can probably do a good job of predicting. We are excited about the opportunity here. We're working with many of the largest players in the private credit space. Maybe one way to sum it up is, what would Moody's say about this name if it were rated, right? That question, what would Moody's say about this one is something that MA can answer, even if the rating agent hasn't yet rated that name, right? There are tens of thousands of companies that might potentially tap the private credit segment of the markets that we can actually address that question. Is it like you can even provide a pre-rating to customers when they subscribe to MA or kind of? In Moody's CreditView, for example, we have all of the rated names and all the research that explains that. We have all of our industry research, but we also have all the scorecards that the analysts use. These are, we make them available to explain, here are the most important quantitative criteria that we review in the rating process. Here are the most important qualitative criteria that we review in the rating process. We can help you use those scorecards. We often, this is one of the things we might do agentically, for example. We can help you pull the data together that's relevant for the scorecard on that name in that industry. What would Moody's say about this name that isn't rated yet? It might be rated someday, depending on whether they tap the public market or the private market. Maybe it's a name you've seen in the syndicated loan space. What would Moody's say about that one? We have economic content. We have benchmark content. We have scorecards that you can use and analytic models to populate and maybe come up with something that would be very helpful in addressing the credit premium in the bond price or in the instrument price. Got it. We only have like maybe four or five minutes left. Maybe my final questions are, I want to touch on two things. Number one is the KYC basis, and then the other one is the expense program. Maybe on the KYC basis, the growth rate has been very strong. I think ARR was about 15% in the second quarter. Could you please talk about the driver of this growth and how sustainable it is? Yeah, I mean, I think we're continuing to be very excited about this. This is a place we are investing. We mentioned it at the top of the program here. There is great opportunity for us to provide tools, and whether they take the form of databases or data feeds or analytic models or software applications to streamline the operations in these efforts. These efforts I would actually think of as KYC and third-party risk management because when we help people evaluate suppliers, we put this in the same, we think of it as in the same business unit. That's, I think, an important note. There's good growth in the supply chain space as well. Maybe more importantly, the opportunity to address the labor required to investigate a name that showed up on the list, right? Think of this, a bank, for example, they're processing thousands of these a day. And 90-something percent of them get taken out and addressed. We got a match. We're good. I know this is Owen Lau. He's a guy who works at Alpha. We're in good shape, right? That's confirmed. Then there's another one named Owen Lau who works at another company, and we're not sure if it's the same guy. That requires an investigation. The work required to do the investigation is multiple. It's just hours and hours of work. If we can find a way to streamline that, maybe agentically, we've actually sold an agent to help people do the screening, right? That's a place where we can really access another tab because we earn economic rent by replacing the labor, right? Five investigators can do the work of maybe even 50 investigators before by leveraging these tools. It's not just Google searching. It's all of our tools at once leveraged for you agentically to really save you time and money. That's, I think, why we're so excited about this business. Yeah, that's why there are lots of opportunities in AI, combining AI to many different areas. Yeah. I guess my final question is about your expense. You had, I think you gave us an update on the efficiency program back in the fourth quarter of 2024. When would you complete this program, and how much expense runway do you expect to save? Yeah, I think the restructuring window is open. I actually think it's declared in the statements. I think it's open for more than a year, right? We are continuing to do work here and acknowledge that work through that restructuring process. If you ask me how long will this go on, I would say we are actively engaging and continue to engage in redeployment efforts, taking the good people who are doing the work that we want to do and maybe applying it to a new activity, maybe a new activity that we consider to be worthy of us doubling down on some of these bets. The lending space is a good example. We're doing a lot of that. We're doing a lot of work to drive productivity, especially in the engineering space, leveraging AI, for example. Some of these new tools that have come through are really quite valuable, and they enable us to do a lot more work in a shorter period of time. The same thing goes for product development. The same thing goes for sales, right? We're leveraging some of these AI tools to generate more productivity per head. That's an affirmative objective, and it's one that we are continuing to do, and you'll see that reflected through that restructuring window. Got it. I think we're about time. Steve, again, thank you for your time and Kiera as well. Thank you all for joining us today. Thanks, Owen. Hopefully, that was helpful. We look forward to talking with you next time. If anybody has any questions or follow-up, you'll let us know. Sounds great. Thanks a lot. Thanks, Owen. Thank you. Thanks a lot. All right. Have a good day. Bye-bye.