Intercontinental Exchange, Inc. Q3 FY2025 Earnings Call

Hello, everyone, and welcome to the ICE Third Quarter 2025 Earnings Conference Call and Webcast. My name is Lydia, and I will be your operator today. I'll now hand you over to Katia Gonzalez, Manager of Investor Relations, to begin. Please go ahead.

Good morning. ICE's third quarter 2025 earnings release and presentation can be found in the Investors section of ice.com. These items will be archived, and our call will be available for replay. Today's call may contain forward-looking statements. These statements, which we undertake no obligation to update, represent our current judgment, and are subject to risks, assumptions and uncertainties. For a description of the risks that could cause our results to differ materially from those described in forward-looking statements, please refer to our 2024 Form 10-K, 2025 third quarter Form 10-Q and other filings with the SEC. In our earnings supplement, we refer to certain non-GAAP measures. We believe our non-GAAP measures are more reflective of our cash operations and core business performance. You'll find a reconciliation to the equivalent GAAP terms in the earnings materials. When used on this call, net revenue refers to revenue net of transaction-based expenses and adjusted earnings refers to adjusted diluted earnings per share. Throughout this presentation, unless otherwise indicated, references to revenue growth are on a constant currency basis. Please see the explanatory notes on the second page of the earnings supplement for additional details regarding the definition of certain items. With us on the call today are Jeff Sprecher, Chair and CEO; Warren Gardiner, Chief Financial Officer; Ben Jackson, President; Lynn Martin, President of the NYSE; and Chris Edmonds, President of Fixed Income and Data Services. I'll now turn the call over to Warren.

Thanks, Katia. Good morning, everyone, and thank you for joining us today. I'll begin on Slide 4 with some of the key highlights from our record third quarter results. Third quarter adjusted earnings per share were $1.71, up 10% year-over-year and the best third quarter in our company's history. Net revenues totaled $2.4 billion and were underpinned by a 5% increase in recurring revenue. This recurring revenue growth was fueled by a 9% rise in exchange data and a 7% uplift in fixed income and data services, both reflecting sustained demand for our high-value proprietary data offerings. Third quarter adjusted operating expenses totaled $981 million. Our disciplined cost management was further supported by approximately $15 million in one-time benefits, about evenly distributed across compensation expense and depreciation and amortization. After adjusting for these benefits, we would have been towards the low end of our guidance range. I also want to provide some color on our third quarter adjusted tax rate of 21%, which benefited from recent prior year tax audit settlements. Excluding this benefit, the adjusted tax rate would have been within the prior 24% to 26% guidance range. And as a result, we expect the fourth quarter tax rate will normalize to between 24% and 26%. Moving to capital allocation. We returned $674 million to our shareholders during the quarter, including approximately $400 million of share repurchases. In addition, we reduced debt outstanding by roughly $175 million, reducing gross leverage to just over 2.9x EBITDA. Next, I will touch on a few fourth quarter guidance items. We expect fourth quarter adjusted operating expenses to be in the range of $1.005 billion to $1.015 billion. The sequential increase is largely driven by the aforementioned one-time expense items not repeating in the fourth quarter. Fourth quarter adjusted non-operating expense is expected to be between $180 million and $185 million, driven by a sequential uptick in interest expense related to our October investment in Polymarket. As a note, we funded $1 billion of that investment with CP issuance in early October and expect to fund up to an additional $1 billion in the future, also utilizing existing capacity on our commercial paper program. Now let's move to Slide 5, where I'll provide an overview of the performance of our Exchange segment. Third quarter net revenues totaled $1.3 billion, building on strong double-digit growth in the prior 2 years. Transaction revenues totaled $876 million. Importantly, towards the end of October, open interest across our futures and options complex surged 16% year-over-year, with energy futures up 14% and interest rate futures climbing 37%, underscoring the growing demand for our risk management tools amid shifting macroeconomic conditions. Shifting to recurring revenues, which include our exchange data services and our NYSE listings business, revenues totaled a record $389 million, up 7% year-over-year. Underpinning growth in our record recurring revenues was 9% growth in our broader exchange data and connectivity services, which is once again led by our futures data, while also benefiting from approximately $6 million of auto-related revenue that we don't anticipate will repeat in the fourth quarter. In our listings business, the NYSE helped to raise a market-leading $20 billion in new IPO proceeds through the first 3 quarters of 2025. It is worth noting that only roughly half of new IPOs have met the NYSE's listing standards, and these high standards remain a critical component of our 99% retention rate. As a result of this strong performance within our exchange data business, we now expect full year growth to be towards the high end of our 4% to 5% guidance range. Turning now to Slide 6. I’ll discuss our Fixed Income and Data Services segment. Third quarter revenues totaled a record $618 million, including transaction revenues of $123 million. On a year-over-year basis, ICE Bonds revenues increased 15%, driven by 41% growth in our muni business, which was in part driven by growing institutional adoption. Within our CDS business, results were largely driven by lower member interest, a direct result of the lower Fed funds rate when compared to the year-ago period. Recurring revenue totaled a record $495 million and grew by 7% year-over-year. In our fixed income data and analytics business, record third quarter revenues of $311 million increased 5% year-over-year, driven by growth in pricing and reference data in our index business, which reached a record $754 billion in ETF AUM at the end of the third quarter. Data network technology revenues were record highs and increased by 10% in the quarter, an acceleration from 7% growth in the first half and 5% growth in 2024, driven by heightened demand for our ICE Global Network. Our strategic investments in data center infrastructure are paying off, driven by increasing demand for data and increased capacity, as well as clients preparing to integrate AI into trading workflows. We also continue to drive high single-digit growth across our consolidated feeds business and our desktop solutions as we continue to realize the benefits of investments to enhance our platform. Worth noting that the third quarter included a few million dollars of one-time revenue that we don’t expect will repeat. That said, we still anticipate fourth quarter revenue growth in data and network technology to be in the high single-digit range, and for total segment recurring revenue to be between 5% and 6%, both for the fourth quarter and the full year. Please flip to Slide 7, where I’ll discuss our Mortgage Technology results. Third quarter revenues totaled $528 million, up 4% year-over-year. Recurring revenues totaled $391 million and increased on a year-over-year basis. The year-over-year improvement was largely driven by our data and analytics business and MSP within our servicing business. Shifting to the fourth quarter, we expect revenues to remain at these levels, primarily driven by Mr. Cooper's acquisition of Flagstar, and customers resetting their minimums on Encompass, which I'll note is paired with the benefit of higher transaction fees. We expect these items to largely be offset by revenue from new customers coming online. Transaction revenues totaled $137 million, up 12% year-over-year, driven by double-digit revenue growth related to Encompass closed loans and high single-digit growth from MERS registrations. As you look to the fourth quarter, it's important to remember typical seasonal impacts on purchase volumes, which tend to be lighter in the fourth quarter relative to the second and third quarters. In summary, the third quarter was – once again grew revenues, adjusted operating income, and adjusted earnings per share, building upon our record first half results and representing the best year-to-date performance in our company's history. And as we continue to strategically invest in our future, we have also returned over $1.7 billion to shareholders year-to-date. As we look to the end of the year and into 2026, we remain focused on extending our track record of growth and on creating value for our shareholders. I'll be happy to take your questions during the Q&A. But for now, I'll hand it over to Ben.

Thank you, Warren, and thank you all for joining us this morning. Please turn to Slide 8. Technology and innovation have been foundational to ICE since our inception. Our approach to AI is a natural extension of that legacy. We are using it to accelerate our existing 25-year automation journey by building and implementing tools to drive efficiency and deliver enhanced analytical insights for ICE and our customers. We are now taking the next step by combining our pursuit of workflow automation across our business processes with the solutions we provide to our clients through generative and agentic AI under the name of ICE Aurora. As we continue to expand our AI capabilities, we're leveraging three core strengths: deep operational and complex workflow expertise; highly differentiated proprietary data, which we believe will only grow in value; and the powerful network effects of our platform. We started with a deep understanding of our data, workflows, task and document management, as well as the rules and compliance frameworks of our businesses. We then conducted a risk assessment of how much automation can be applied to executing these workflows based on the impact, technical maturity, accuracy, and model explainability in the AI tools available balanced against the risks of automation. Similar to benchmarks used across industries to measure the scale of automation, we rank our automation within processes on a scale of 0 to 5. At 0, the process is entirely manual. At 5, the process is fully automated, including exception handling without requiring human input. We are applying this model to every workflow across ICE, bottom up, measuring exactly where we are today in terms of the maturity of AI models automating workflows with or without human intervention, and where we can get to based on the current state of the technology. Currently, most generative or agentic AI models at their core are best at pattern recognition, and this recognition continues to evolve. This means there is a stochastic and probabilistic accuracy to them, measuring the reliability and predictability of the outcomes AI models produce. For the highly regulated businesses that we and our customers operate, there has to be an acknowledgment of how much accuracy a probabilistic outcome must have in order to be considered acceptable for full automation versus when some level of human interaction remains necessary, especially in exception handling. Today, we have clear visibility of where we can go and are executing on this in many areas, balanced by the risk I just outlined. That is our strategy and what our ICE Aurora platform is all about, and we're already seeing results across ICE. AI is streamlining and automating workflows across systems, accelerating product development and dramatically accelerating the speed with which we can deliver the modernization of multiple tech stacks within ICE. Importantly, we aim to do this without compromising our adherence to information security, data management, and privacy. In our energy markets, the macro-AI and data center expansion trend is expected to drive significant energy demand over the next decade. We believe our trading and clearing platform, which offers deep liquidity and price transparency across the full energy spectrum, is uniquely positioned to support customers. Despite lower overall market volatility, the third quarter of this year was the second strongest third quarter in our history, following the record quarter of a year ago, led by continued strength in our global gas and power markets, with third quarter volumes up 8% and 18% year-over-year, respectively. As we've consistently said, open interest is a leading indicator of future growth, and we're pleased to see it continue trending higher with record futures Energy OI in October up 14% year-over-year, including 25% and 30% growth in our Brent and TTF benchmarks, respectively. This reflects the value of our diversified energy platform, the depth of our liquidity and the confidence customers place in our benchmarks, which serve as global price reference points across thousands of related contracts providing trusted price transparency across geographies. Across our global gas portfolio, which spans North America, Europe, and Asia, volumes have increased 20% year-to-date. Importantly, this strong year-to-date performance has been underpinned by broad-based strength, including a 16% increase in our North American complex, a 26% increase in our European portfolio, and a 27% increase in our Asian JKM market. In parallel, our power markets have seen continued growth, with volumes up 21% year-to-date and 18% in the quarter. This reinforces the synergy between our gas and power markets and the need for comprehensive risk management tools that offer transparency, flexibility, and choice. In Fixed Income and Data Services, driven by multi-year investments, our comprehensive platform delivered another quarter of record revenues, which grew 5% year-over-year, including 7% growth in recurring revenue and 10% growth in our data and network technology business. Our proprietary data is the cornerstone of our business and a key differentiator in the evolving AI landscape. With over 50 years of experience, our high-quality pricing and reference data serves as the foundation for what is today, one of the largest providers of fixed income indices globally. From benchmark indices and analytics to custom solutions, we support the full ETF ecosystem. As AI becomes embedded in trading strategies across all areas of investing, we expect our proprietary data to grow in strategic importance, with our data sets providing a competitive edge to users of AI models that depend on precision, depth, and large quantities of historical data. Our data is securely managed within ICE's infrastructure, protected by firewalls and entitlements. Our commercial agreements tightly control access and only permit specific use cases through authorized delivery channels. This approach helps ensure our data remains exclusive and strategically deployed, especially as models increasingly rely on high-quality inputs to drive performance. In our reference data business, we're leveraging AI to process and validate documents from hundreds of sources, using AI models that we thoroughly test for fit-for-purpose and high probabilistic outcomes, achieving over 95% accuracy in extracting reference data from fixed income prospectus. This capability is a critical part of the collection process, improving both efficiency and speed of delivery, enabling us to do more with the same resources. Today, within our reference data business alone, we are processing roughly 40,000 documents on average per month using AI. Documents assessed by AI that meet predefined confidence thresholds go straight into our database for clients to consume, while those falling below the threshold are flagged for manual review and intervention. This capability is a critical part of the collection process, improving both efficiency and speed of delivery, enabling us to do more with the same resources. We're also leveraging machine learning to power key components of our evaluated pricing. Our continuous evaluated pricing blends trade and quote data to predict bond pricing, complementing our deep market expertise and data quality workflows. Additional models use historical data to determine bid-ask spreads across the bond universe, with machine learning capabilities significantly improving evaluation quality when measured against actual trades in the market. Meanwhile, our ICE Global Network continues to set the standard for resiliency, latency, and security, connecting participants to over 750 data sources and more than 150 trading venues, including ICE and the NYSE. The ICE Cloud comprises state-of-the-art data centers owned and operated by ICE that facilitate seamless integration with key third-party cloud providers, all under ICE's cybersecurity and operational resilience framework. This provides our clients flexibility to access AI workloads where it makes the most sense without compromising cyber and operational controls. We continue to invest in our data centers to support business growth needs and to meet growing customer demand, including to support increased adoption of AI strategies. This is to ensure we are accessing the most cost-effective, secure, and reliable infrastructure for ICE's needs and our customers' needs, both now and in the future. Across product development, AI is automating data analysis, pattern recognition, and repetitive processes using tools such as GitHub Copilot, freeing product managers to focus on validation and enhancement. This has already accelerated speed to market for certain products. For example, we've reduced the time to convert code for index qualification, calculation, and reporting by roughly 60%. Demonstrating the new innovation underway across ICE, we're utilizing AI with our new sentiment indicator data sets, including Reddit, Dow Jones, and Polymarket, with Google and Meta AI models helping to process these data sets and identify patterns. While still in the development phase, these data sets are particularly attractive to market participants seeking an edge through differentiated data inputs. This illustrates how our proprietary data set is set to become increasingly vital to a trading community reliant on models to support trading decisions. In our mortgage business, the use of AI is helping our efforts to streamline the homeownership experience, enhancing productivity of lending and servicing operations, improving the borrower experience with self-service workflows, reducing risk via automated compliance and quality checks across the mortgage life cycle, all while improving recapture rates for our customers. All of this contributes to lowering the cost to originate and service the loan for our customers, a foundational part of our mortgage strategy. For example, customers using our industry-standard loan servicing system, MSP, saved roughly 20% to 30% on the cost to service a loan based on a recently conducted customer study, and we expect this number will increase with new innovations that we have come to market or are coming to market, such as our enhanced customer service, loan boarding, ICE Business Intelligence for servicing, and our loss mitigation suite. This execution reinforces our clients' trust in us to enhance and streamline their business workflows through our workflow automation capabilities. In the third quarter, despite a tough macro backdrop, revenues increased 4% year-over-year, while transaction revenue grew 12%. We also continued to win new clients, signing on 2 new clients to MSP, both already on Encompass, and building on the 2 we signed in the second quarter, including UWM. We also signed 16 new Encompass clients, 5 of them already on MSP or an MSP subservicer. We've also made significant progress in re-platforming MSP from the mainframe to ICE's modern tech stack to give us increased agility, cost efficiency, and scale. Here, tools such as GitHub Copilot have helped us achieve a significant improvement in productivity, helping us rewrite the entire user interface by the end of this year and migrate 30 million lines of code, with roughly 1/3 complete, and the remaining targeted to complete within 2 years. The original estimate to complete this project was baseline to take up to 7 years, similar to the move off the mainframe following our acquisition of Interactive Data Corporation. With the assistance of GitHub Copilot and other AI-based code conversion tools, we have reduced the projected window to around half the time originally anticipated, a significant improvement to the speed with which we can now convert old technology processes to ICE's modern tech stack. Another interesting area where we're applying our AI adoption model is in customer service. Here, we have evolved our capabilities to a level of conditional automation, one where there is significant automation but still requires human intervention for exception handling. We are using generative AI to provide predictions for a customer service representative on call intent and then call summarization. We are next applying agentic AI to automate department handoff for issue handling. Then we plan to take this to the next level by adding a chatbot designed to go beyond search capabilities, one that also executes real actions, such as payment scheduling for borrower self-service within our ICE mortgage technology servicing digital application. And we will work to expand even further with an intelligent virtual agent for certain issue resolution where the maturity of the solutions and the quality of the probabilistic outcome is balanced against risk. In summary, as ICE continues to enhance our leading technology, we do so with both the client and end consumer in mind as well as always considering what will make us more operationally efficient and deliver solutions that help automate workflows. With that, I'll hand it over to Jeff.

Thank you, Ben. Please turn to Slide 9. Given ICE's recently announced investment and business relationship with Polymarket, I thought it might be helpful to explain our thinking on the evolution of markets. ICE was an early investor in the crypto space, having been an early-stage funder of Bakkt and Coinbase. We made these investments to stay close to the evolution of the market's use of blockchain. In the case of Bakkt, we thought that there could be an acceptance of a system of tokens that adhered to a high level of then existing securities and commodities regulation. We found; however, that traditional regulated financial firms were slow or unwilling to adopt tokens during a period of regulatory uncertainty, particularly where events of default would move unwanted tokens onto a financial guarantor's balance sheet. Current U.S. administration and Congress have been attempting to address these uncertainties, which has caused ICE to more actively lean into the knowledge that we've accumulated over the past decade. One of the significant macro trends of the past decade of blockchain investment is a rewiring of the rails of the banking system. ICE, for example, operates six clearing houses around the world, all of which are highly regulated, and which are required to operate within the limitations of local banking hours, customs, and preferences. On-chain banking now operates globally with 24/7 availability, allowing for instantaneous margin calls and trade liquidations. This facilitates increasing margining and lending against assets, which some cohorts of asset holders are clearly taking advantage of with increased risk management tolerances, and which places excess trade financing collateral into an omnibus stablecoin collateral pool. This excess collateral pool is funded by traders via the forfeiture of earnings on their collateral. Features that were previously unavailable to regulated clearing houses. ICE decided to invest in Polymarket as we're impressed with the design of its underlying architecture of smart contracts that take advantage of this new banking infrastructure. Alongside our investment, we've also announced a strategic data agreement under which ICE will become a global distributor of Polymarket's highly differentiated event-driven data. As the leader in non-sports prediction markets, Polymarket provides real-time probabilities on events like elections, economic indicators, and cultural trends, offering a powerful new layer of insight, supporting more informed decision-making. We believe that we can accelerate Polymarket's acceptance into the traditional financial system by virtue of our distribution, understanding, and long-time customer relationships. And we believe Polymarket's engineering team can help ICE's engineers better understand our own adoption of evolving banking technology, a relationship that is already paying dividends for both of us. ICE is in the process of rolling out an advanced clearing model for our global clearing houses, one that we've very elegantly named ICE Risk Model 2. Our new clearing system was built on the existing local banking and regulatory infrastructure for funds movement and collateral management. However, the current regulatory environment is being confronted by collateral management using tokens, which I believe will help evolve regulatory oversight to take advantage of 24/7 capital movement. And ICE intends to be at the forefront of driving this evolution, given our own use case of operating six global clearing houses with differing collateral and regulatory environments. Such an evolution can make global clearing and trade settlement more efficient. And we've seen that the efficient use of collateral typically results in increased trading volumes and transaction revenues. One does not have to look too far to see that trading volumes in the U.S. equities markets have dramatically increased since the industry freed-up collateral by moving from T-plus-2-day to T-plus-1-day settlement times. Beyond the rewiring of funds movement, Polymarket has pioneered the rapid listing of new markets, driven by real-time consumer demand. Traditional exchanges have been subject to government approvals of our new product launches, which, at best, take 30 days, and in many countries, substantially longer. Polymarket is forcing a dialogue in the U.S. on how to minimize government regulatory burdens, so as to not impede innovators. We think this dialogue will ultimately benefit new product innovation for all markets, and certainly for ICE. Now augmenting on Ben's comments on the adoption of artificial intelligence. We see the jagged intelligence phenomenon at play for both our own AI adoption and for that of our customers. Internally at ICE, we have our engineers using copilots to help them write code more effectively, particularly where the projects involve modernizing our legacy code. However, to fully deploy production code at scale and at the latency precision which ICE operates, we still require unique skill sets that are not now available in AI. So our current experience is that AI has become a good assistant for our teams but not a replacement. Ben also highlighted our use of AI in improving our customer service. Artificial intelligence has made our help desk more efficient at diagnosing real-time issues as well as cataloging and summarizing customer inputs to create more efficient feedback loops. The third area where we deployed AI is in our data gathering and data organization, such as cataloging bond and equity prospectuses, cleansing our data sets, and organizing unstructured data for our vast financial data offerings. And lastly, much of the regulation that ICE is required to oversee is surveillance in the form of pattern recognition. Here, again, AI tools are making our colleagues more efficient at our oversight. So in summary, our internal use cases for AI have made our colleagues better at what they do. In terms of our customer adoption of AI, we see that same jag in intelligence, where AI is very helpful in some areas yet unreliable in others. Where our customers interface with ICE products for pattern recognition or language organization, we're seeing positive uptake. For example, we've seen healthy uptake of our structured and unstructured financial data offerings. Similarly, the AI tools that we've built into our mortgage network, such as our data and document automation and our customer engagement suite, have strong interest, with customers adopting these tools to more efficiently target new business and minimize the cost of mortgage onboarding, but not to replace underwriting decisions that are subject to regulatory oversight or to replicate the vast ICE mortgage network that links the industry together, including the U.S. federal housing regulators' supervisory efforts in validating GSE and Federal Home Loan Bank mortgage holdings and providing it with monthly mortgage service information. Finally, a number of people have speculated to me that the overall volumes of trading must have increased due to AI adoption. While that's possible, I believe that a significantly larger volume impact has come from capital being freed up when moving equity settlement times 1-day forward and with the expansion of retail trading leverage that's inherent in popular 1-day options. So all in all, we think the current state of AI is helping to control costs and control new hiring and is for us at the margin driving sales and transaction growth. Our record third quarter results on top of our extraordinary third quarter results of last year are another example of strong execution across our all-weather platform. We very intentionally positioned the company to provide customer solutions in numerous geographies and economic conditions to facilitate these all-weather results. I'd like to end our prepared remarks by thanking our customers for their continued business and thank you for your trust. And I'd also like to thank my colleagues at ICE for their contribution to the very best third quarter in our company's history, following on our unsurpassed first half results, and yielding the best year-to-date performance in the company's history. I'll now turn the call back to our moderator, Lydia, and we'll conduct a question-and-answer session until 9:30 Eastern Time.

Our first question today comes from Ken Worthington with JPMorgan.

Believe it or not, my question is on the impact of AI in the mortgage origination and servicing business, then really following up in your prepared remarks. So maybe first, how easy is it to incorporate the benefits of AI in MSP and Encompass given what their tech stacks look like today? You gave some examples, but can you get AI into all the areas you need to maximize your competitiveness? And then maybe secondly, do you think AI can make it easier for perspective, ICE Mortgage Technology clients to pursue efficiency on their own? And does the hope of new technology extend the time it's taking for ICE to sign up new Encompass and MSP customers, particularly when thinking about large customers.

Thanks, Ken. It's Ben. I'll take this. In my opinion, the best way to summarize the impact of AI on our mortgage origination and servicing platforms is that it has allowed us to evolve these platforms from systems of record to a system of intelligence. This means that we are managing extremely complex and highly regulated business processes and workflows within these core platforms. As mentioned in previous comments by both Jeff and me, we have an extensive network connected to us, including thousands of customers, hundreds of network service providers, 35,000 settlement agents, and tens of thousands of notaries, among others. We facilitate communication not only between clients connected to us, but also, and perhaps more importantly, between our clients themselves. We hold proprietary information on how to effectively manage that workflow and enhance its robustness. Additionally, we possess and maintain the most comprehensive compliance and underwriting guideline databases in the industry, which are essential for fully automating underwriting workflows through our DDA platform. Our extensive collection of closing guidelines and rules for every county across the country allows us to enable electronic closings and the e-reporting of loan transactions, particularly in the business we acquired with Simplifile. We also have significant proprietary data derived from our platforms that inform our business intelligence models, helping our clients achieve greater operational and business efficiencies. When we consider all of this and how we are implementing AI throughout each business process from a foundational perspective, using the Aurora process that I mentioned, we analyze each business process, deciphering the probabilistic accuracy of the AI's pattern recognition model and weighing the business tolerances concerning regulatory rules and compliance to determine the extent of automation versus the need for human intervention. We are exceptionally well positioned to capitalize on this, and it is reflected in our results. We experienced our strongest sales quarter of the year in the third quarter. Within our ICE Mortgage Technology segment, we signed two MSP clients, both of whom are already using Encompass, in addition to two we secured last quarter, including one of the largest lenders in the U.S., United Wholesale Mortgage. Furthermore, we achieved 16 Encompass wins, five of which involve MSP or MSP subservicers that are embracing our vision for a cohesive front-to-back workflow. Therefore, we feel very well positioned, and we're optimistic about the opportunities ahead, seeing that we are in a strong position.

Our next question comes from Dan Fannon with Jefferies.

Another question here on Mortgage. Warren, you gave some near-term comments around the fourth quarter given Flagstar, but could you elaborate a bit more on the shorter-term dynamics and also PennyMac, which announced in the quarter that they would also be leaving your platform over time, what that contribution is today?

Sure. Thanks for the question, Dan. Regarding the third quarter, we were slightly lower by a few million dollars. There are three main reasons for this. First, there was a higher-than-expected roll-off of inactive loans on MSP, which we previously mentioned. However, active loans on MSP did see an increase for the first time in a few quarters, which is a positive sign. Second, some customers renewed at slightly lower minimums than we anticipated, though we are noticing a narrowing discount to last year's minimums and an improvement in the percentage of loans above those minimums, which helps our transaction fees. Third, we had some implementations planned for the fourth quarter and the first quarter of next year based on customer needs. While not all of our best sales from the year will contribute to this quarter or the next, they do provide a good outlook for the business moving forward into next year. Overall, while none of these issues are overly significant on their own, they resulted in a couple of revenue streams being lighter than expected. This impacts our fourth quarter run rate and the noted implementations. Additionally, Flagstar will roll off in the fourth quarter, which we had discussed previously. Regarding PennyMac, consider it will contribute about 0.5 points of growth, but that won’t affect us until 2028, specifically impacting recurring revenue at that time.

Our next question comes from Ben Budish with Barclays.

I would like to follow up on Jeff's comments regarding Polymarket. Could you provide more details about the data licensing or redistribution arrangements? What potential impact could that have on profit and loss? Also, you've made a significant investment in the company; could you share your long-term plans? Are there any intentions to list event contracts, especially since we've heard competitors discussing that? Or is this more focused on the partnership aspect? Additionally, could you clarify how much of the appeal lies in the blockchain technology itself as opposed to it merely being a means to access new trading market data points?

This is Chris Edmonds. I'll take the first part of that and let Jeff pick up on some of the other parts of your questions. But on the sentiment analysis itself of the data, it's become an interesting feedback loop for our clients. We've seen a tremendous demand from our clients based on our experience with the Reddit data, the Dow Jones data that Ben referenced in his prepared comments. So now the ability to take those signals and actually create a market around that and then get the feedback loop from that activity that's happening on Polymarket really gives us an opportunity for a complete ecosystem around that, and that's driving the customer interest in that. And really what led us to the idea that we wanted to be a distributor of that data to make sure we had in our ecosystems for our clients to use.

Yes. I believe what I conveyed in my prepared remarks is that we see Polymarket as having created something notably innovative and unique in their approach to contract settlements. They utilize a blockchain, enabling direct transactions between participants through token transfers on a second layer, which enhances their performance capabilities. We aim to understand this better and involve our engineering team because the trends in traditional finance indicate a growing number of assets likely to be tokenized, including potentially bank deposits. We anticipate that this will eventually integrate into the clearing infrastructure and allow us to operate continuously. Currently, our six clearing houses lead clients to maintain excess collateral at each one due to the banking hours for moving capital when those specific houses are active. By implementing 24/7 collateral management, we expect to reduce overall collateral requirements for our clients, which will likely lead to increased trading volumes, a trend we've already observed. Thus, it's in our best interest to enhance the efficiency of our customers' trading. I would just say separately, we built ICE over 20 years by really leaning into commercial users and the workflows that they have and the supply chains that exist around the globe, and helping to manage risk of commercials. We've never been particularly potent in the retail space or even the high-frequency space. Others have focused on that, and we've been very, very commercial. So it's good to have a relationship with Polymarket because they're really educating us about how they have gone to market with retail customers, how they did essentially tremendous ground game marketing without money assets at their disposal and really created a brand and brand awareness with a small balance sheet. And so again, we admire what they've done. We're trying to educate them on traditional finance while they educate us on consumer finance. And hopefully, that will pay dividends for both of us down the road, but it just made sense that the teams work together to really educate one another, and the hope that one and one makes three.

Our next question comes from Patrick Moley with Piper Sandler.

Yes. To elaborate on Ben's question regarding Polymarket at the contract level, much of the trading activity we've observed in prediction markets has centered around sports contracts. There have been numerous lawsuits and uncertainties about whether regulators will permit this to expand, but if they do allow it in the coming years, we could see a significant shift of sports betting volumes onto the Exchange. I am curious about your perspective on this and what opportunities you believe could arise for Polymarket and ICE. Additionally, could you discuss how you view the evolution of sports contracts compared to non-sports contracts and their commercial applicability moving forward?

Sure. This is Jeff again. I contacted Shayne, the founder of Polymarket, early in the summer when it became clear that the Trump administration and Congress were likely to validate much of what was happening with stablecoins and on the blockchain. It was in that context that we started our discussion, prior to the NFL football season. We were particularly interested in their non-sports activities, where they really excel globally. We believe that data and information, such as supply chain data, natural events, weather, and corporate actions, will be very appealing to traditional finance. In fact, we are aware of several institutional investors who are already seeking out data to inform their traditional decision-making. Sports were not our main focus. While it's beneficial for Polymarket to develop a business around that and generate revenue, it won't significantly benefit us as we are not a venture firm. Our primary motivation lies in integrating the underlying technologies into our processes to boost our sales revenue and manage our costs effectively.

Next question comes from Brian Bedell with Deutsche Bank.

Great. Maybe just back to mortgage. I just wanted to clarify, Warren, on the 4Q outlook, that the guide of, I think, flat revenue, 3Q to 4Q, was that the whole segment? Or was that just for recurring? I know you did mention the seasonality in transaction fees. So if you could just clarify that. And then just longer term, outlook on that build of the revenue synergy, what's been actioned so far? And are you sticking to the same time line on the integration? And then maybe just longer term, just comments around competition in the mortgage space from the blockchain and from blockchain providers. I know that's more futuristic, but just your thoughts on that.

Sure, Brian, I'll address the first two questions and then pass it to Ben. Thank you for your clarification. The comments in the script referred to recurring revenue remaining at a similar level as in the third quarter. I mentioned that we typically experience a seasonal impact due to lower purchase volume during the winter months, noticeable in the fourth and first quarters of the year. I’m not providing specific guidance on this, as we cannot predict exact volumes during any given period. This is intended as a helpful overview for you to consider as you update your models. Regarding your second question about longer-term guidance, we will offer insights during the fourth-quarter call. The MBA is currently predicting loan growth to be in the high single-digit range, and industry originations are expected to be slightly below $6 million next year based on current data. I’m not confirming or denying that, but that's the information available. Referring back to the scenarios we've shared before, following the Black Knight transaction, we would likely end up in the lower mid-single-digit growth range in that context. However, this can change swiftly as interest rates and mortgage rates fluctuate. We'll have to evaluate this as we approach next year's guidance.

Brian, to address your question about the competitive landscape, customers remain focused on partnering with an independent, financially strong technology provider that can enhance their market infrastructure without competing against them. This focus is a key reason for our continued sales success, which we noted in this call as our highest sales quarter of the year. Regarding PennyMac, there has been a longstanding dispute with Black Knight, in which it was determined that PennyMac used our confidential information for their servicing system. After they acquired Black Knight, it was expected that they would take an ownership interest in a platform and develop a loan origination system. This does not align with our view of a neutral independent platform. In the case of Rocket, they are migrating their loans to a legacy mainframe system called LSAMS rather than to Sagent, as they prefer to develop their proprietary system. In contrast, platforms like MERS offer a comprehensive solution for first and second loans, with established legal standing in mortgage processes and expertise in bankruptcy foreclosure. MERS operates under an independent Board comprised of industry participants, making it a valuable asset for us. Overall, we believe our position as a well-capitalized, neutral technology provider that does not compete with our customers places us in a strong position.

Our next question comes from Alex Blostein with Goldman Sachs.

I was hoping to go back to one of the earlier points you made in prepared remarks around AI initiatives when it comes to the workflow automation, and you spent quite a bit of time talking through various processes. When you zoom out, I guess, what's the goal here? What in terms of actual savings do you guys think this can produce for the firm? What's the time frame on that? And how are you thinking about either reinvesting some of these savings or letting them sort of drop down to the bottom line? And maybe sort of help us frame what that means for the firm's sort of profitability over time?

Alex, it's Ben. We have developed a strategy and process that we are implementing across ICE, specifically the ICE Aurora platform. Our focus is on breaking down each business process internally and assessing the solutions we provide to our customers. We are determining the extent of automation that can be utilized and identifying when human intervention is necessary. Given the highly compliant and regulated nature of our operations, this is crucial. The AI models we use are pattern recognition software, offering various probabilistic outcomes. Some processes are well-suited for nearly full automation, while others require human involvement, particularly in exception handling, such as compliance checks in areas like mortgages where tolerance levels are very low. We are seeing efficiency gains from this approach. Currently, we believe we can accomplish more with the same number of people, allowing us to accelerate our market offerings. There is a growing demand for us to increase our output, and we anticipate being able to meet that demand while maintaining our historical headcount.

Our next question comes from Ashish Sabadra with RBC Capital Markets.

This is Bill Qi on for Ashish Sabadra. Just with the continued strength we've seen on your data services and solutions businesses across ICE, could you maybe give a little bit of commentary on the drivers there? Where maybe the appetite is coming from a customer perspective, either kind of the quantity of data consumed versus pricing? And also with the developments of the new kind of high-value data sets like the sentiment indicators, is that kind of another leg up, you'd say, kind of for driving growth in those segments?

It's Chris. I appreciate the question. I would suggest to you that it's more comprehensive than that. It's a complete playbook that you're getting to take advantage of on the client side, and that is what is resonating. Certainly, the high-value assets that you made reference to are one. But if you look at the mission-critical data that we have across all of our Exchange space, going there, that's a foundation that people come to know and trust, our ability to deliver that into their systems, given the delivery channel that they deem most appropriate at a given time. And the ability to add additional content, whether it's the new pieces we talked about or where they can get additional pieces of data from other sources. As we said in the prepared remarks, we have 750 different data sources that can come across those different delivery mechanisms. We made investments, as Warren and Ben both said in the prepared remarks, in these capabilities. Those investments are paying off, and you're seeing the clients' ability to make those changes and incorporate these opportunities into their operational workflows.

We have no further questions. So I'd like to turn the call back over to Jeff Sprecher, Chair and CEO, for any closing comments.

Well, thank you, Lydia. I appreciate the way you managed the call today and thank you all for joining us this morning. I'd like to again thank all of my colleagues for delivering the best third quarter in our company's history and again, thank our customers for their continued business and for the trust they have in the way we manage our business. We'll be back soon to continue to update you. But meanwhile, we're going to be working to innovate for our customers and continue to build our all-weather business model. Thanks, and have a great day.

Thank you. This now concludes our call. Thank you very much for joining. You may now disconnect your lines.