洲際交易所 (ICE) 2025 Q3 法說會逐字稿

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. (Operator Instructions)

大家好，歡迎參加ICE 2025年第三季財報電話會議和網路直播。我叫莉迪亞，今天由我來為您接聽電話。（操作說明）

I'll now hand over to Katia Gonzalez, Manager of Investor Relations, to begin. Please go ahead.

現在我將把發言權交給投資人關係經理卡蒂亞·岡薩雷斯，由她開始發言。請繼續。

Good morning. ICE's third quarter 2025 earnings release and presentation can be found in the Investors section of www.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.

早安.ICE 2025年第三季財報及簡報可在www.ice.com網站的投資人關係欄位中找到。這些資料將被存檔，我們的電話會議錄音也將提供回放。今天的電話會議可能包含前瞻性陳述。這些聲明代表了我們目前的判斷，我們不承擔更新的義務，並且存在風險、假設和不確定性。有關可能導致我們的業績與前瞻性聲明中所述業績存在重大差異的風險的描述，請參閱我們 2024 年 10-K 表格、2025 年第三季 10-Q 表格以及向美國證券交易委員會提交的其他文件。

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.

在我們的獲利補充文件中，我們提到了一些非GAAP指標。我們認為，我們的非GAAP指標更能反映我們的現金營運和核心業務表現。您可以在收益資料中找到與對應 GAAP 條款的調整表。

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.

在本電話會議中，「淨收入」是指扣除交易費用後的收入，「調整後收益」是指調整後的稀釋每股收益。除非另有說明，本簡報中提及的收入成長均以固定匯率為基礎。有關某些項目的定義詳情，請參閱收入補充說明第二頁的解釋性註釋。

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.

今天參加電話會議的有：董事長兼執行長 Jeff Sprecher；財務長 Warren Gardiner；總裁 Ben Jackson；紐約證券交易所總裁 Lynn Martin；以及固定收益和數據服務總裁 Chris Edmonds。

I'll now turn the call over to Warren.

現在我將把電話交給沃倫。

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.

謝謝你，卡蒂婭。各位早安，感謝大家今天收看我們的節目。我將從第 4 張投影片開始，介紹我們創紀錄的第三季業績的一些關鍵亮點。

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.

第三季調整後每股收益為 1.71 美元，年增 10%，是該公司歷史上最好的第三季。淨收入總計 24 億美元，其中經常性收入成長 5%。經常性收入的成長主要得益於交易所數據成長 9% 以及固定收益和數據服務成長 7%，這兩項成長均反映出市場對我們高價值專有數據產品的持續需求。

Third quarter adjusted operating expenses totaled $981 million. Our disciplined cost management was further supported by approximately $15 million in onetime benefits, without 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.

第三季調整後營運支出總計9.81億美元。我們嚴格的成本管理也得到了約 1500 萬美元的一次性收益的支持，這筆收益並未平均分配到薪資支出、折舊和攤提。考慮到這些因素，我們的預期值將接近指導範圍的下限。

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%.

我還想就我們第三季調整後的稅率 21% 做一些說明，這得益於最近一年稅務審計的和解。如果排除此優惠，調整後的稅率將落在先前 24% 至 26% 的指導範圍內。因此，我們預計第四季度稅率將恢復正常，介於 24% 至 26% 之間。

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 2.9 times EBITDA.

接下來討論資本配置。本季我們向股東返還了 6.74 億美元，其中包括約 4 億美元的股票回購。此外，我們減少了約 1.75 億美元的未償債務，使總槓桿率降至 EBITDA 的 2.9 倍以上。

Next, I will touch on a few fourth quarter guidance items. We expect fourth quarter adjusted operating expenses to be in the range of $1.05 billion to $1.15 billion. Sequential increase is largely driven by the aforementioned onetime expense items not repeating in the fourth quarter.

接下來，我將談談第四季業績指引方面的一些事項。我們預計第四季度調整後營運支出將在 10.5 億美元至 11.5 億美元之間。環比成長主要是由於上述一次性支出項目在第四季不再重複出現。

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.

第四季調整後的非經營性支出預計在 1.8 億美元至 1.85 億美元之間，主要原因是與我們 10 月對 Polymarket 的投資相關的利息支出環比上升。需要說明的是，我們在 10 月初透過發行商業票據為該項投資提供了 10 億美元的資金，並預計未來還將利用我們商業票據計劃的現有能力，再提供至多 10 億美元的資金。

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 two years. Transaction revenues sold $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.

現在讓我們來看第 5 張投影片，我將在此概述我們交易所部門的表現。第三季淨收入總計 13 億美元，延續了前兩年兩位數的強勁成長動能。交易收入達 8.76 億美元。值得注意的是，10 月底，我們期貨和選擇權組合的未平倉合約量年增 16%，其中能源期貨上漲 14%，利率期貨上漲 37%，這凸顯了在宏觀經濟情勢變化的情況下，市場對我們風險管理工具的需求不斷增長。

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 with 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 or will repeat in the fourth quarter.

轉向經常性收入，包括我們的交易所數據服務和紐約證券交易所上市業務，收入總額達到創紀錄的 3.89 億美元，同比增長 7%，這支撐了我們創紀錄的經常性收入的增長，其中更廣泛的交易所數據和連接服務增長了 9%，這再次由我們的期貨數據引領，同時還預計大約 600 萬美元的汽車在第四季度重複

In our listings business, the NYSE helped to raise a market-leading $20 billion in new IPO proceeds through the first three 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.

在我們的上市業務中，紐約證券交易所幫助新公司在 2025 年前三個季度籌集了市場領先的 200 億美元 IPO 資金。值得注意的是，只有大約一半的新IPO符合紐約證券交易所的上市標準，而這些高標準仍然是我們99%留存率的關鍵組成部分。由於交易所數據業務的強勁表現，我們現在預計全年成長率將達到我們先前 4% 至 5% 指導範圍的高端。

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 Munis business, which was in part driven by growing institutional adoption.

現在請看第6張投影片。我將討論我們的固定收益和數據服務業務板塊。第三季營收總額創下 6.18 億美元的紀錄，其中包括 1.23 億美元的交易收入。與去年同期相比，ICE Bonds 的收入成長了 15%，這主要得益於市政債券業務成長了 41%，而市政債券業務成長的部分原因是機構投資者的採用率不斷提高。

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.

在我們的信用違約交換業務中，業績主要受成員興趣下降的影響，這是由於與去年同期相比，聯邦基金利率走低所致。經常性收入總額達到創紀錄的 4.95 億美元，較去年同期成長 7%。

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.

在我們的固定收益數據和分析業務中，第三季營收創下 3.11 億美元的紀錄，年增 5%，這主要得益於指數業務定價和參考數據的增長，該業務的 ETF 資產管理規模在第三季末達到創紀錄的 7,540 億美元。

Data network technology revenues were a record -- increased -- 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.

數據網路技術收入創歷史新高，本季成長了 10%，高於上半年的 7% 和 2024 年的 5% 的成長，這主要得益於市場對我們 ICE 全球網路的需求增加。我們對資料中心基礎設施的策略性投資正在取得成效，這得益於資料需求的成長、容量的提升，以及客戶準備將人工智慧整合到交易工作流程中。隨著我們不斷實現對平台進行投資所帶來的收益，我們的綜合資訊流業務和桌面解決方案也持續保持著高個位數的成長。

Worth noting that the third quarter included a few million dollars of onetime 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 the fourth quarter and the full year.

值得注意的是，第三季包含數百萬美元的一次性收入，我們預計這部分收入不會再次出現。儘管如此，我們仍然預計第四季度數據和網路技術的收入成長將達到較高的個位數，並且第四季度和全年的總部門經常性收入成長率將在 5% 到 6% 之間。

Please look 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.

請看第 7 張投影片，我將在其中討論我們的抵押貸款技術成果。第三季營收總計 5.28 億美元，年增 4%。經常性收入總計 3.91 億美元，較去年同期成長。年比業績的提升主要得益於我們的數據和分析業務以及服務業務中的 MSP 業務。

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.

進入第四季度，我們預計收入將保持在目前的水平，這主要得益於 Mr. Cooper 收購 Flagstar，以及客戶在 Encompass 上重置最低消費額，需要指出的是，這也帶來了更高的交易費用。我們預計這些支出將基本上被新用戶上線帶來的收入所抵銷。

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 impact on purchase volumes, which tend to be lighter in the fourth quarter relative to the second and third quarters.

交易收入總計 1.37 億美元，年增 12%，這主要得益於 Encompass 已完成貸款相關的兩位數收入增長以及 MERS 註冊帶來的高個位數增長。展望第四季度，需要注意的是，季節性因素會對購買量產生影響，第四季度的購買量通常比第二季和第三季低。

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.

總而言之，第三季度——收入、調整後營業收入和調整後每股收益再次增長，延續了上半年創紀錄的業績，並創下了公司歷史上今年迄今為止的最佳業績。同時，在我們繼續對未來進行策略性投資的同時，今年迄今為止，我們也已向股東返還了超過 17 億美元。展望今年年底和2026年，我們將繼續專注於延續成長勢頭，並為股東創造價值。

I'll be happy to take your questions during Q&A. But for now, I'll hand it over to Ben.

我很樂意在問答環節回答您的問題。但現在，我先把它交給本。

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.

謝謝沃倫，也謝謝各位今天早上收看我們的節目。請翻到第8頁幻燈片。自公司成立以來，技術和創新一直是ICE的基石。我們對人工智慧的理解是這項傳統的自然延伸。我們正在利用它來加速我們現有的 25 年自動化進程，透過建立和實施工具來提高效率，並為 ICE 和我們的客戶提供增強的分析見解。

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.

我們現在正邁出下一步，將我們業務流程中工作流程自動化的追求與我們透過生成式和代理式人工智慧為客戶提供的解決方案相結合，並以 ICE Aurora 的名義實現。隨著我們不斷擴展人工智慧能力，我們正在利用三大核心優勢：深厚的營運和複雜工作流程專業知識；高度差異化的專有數據，我們相信這些數據的價值只會不斷增長；以及我們平台的強大網路效應。

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.

我們首先深入了解了我們的資料、工作流程、任務和文件管理，以及我們業務的規則和合規框架。然後，我們根據人工智慧工具的影響、技術成熟度、準確性和模型可解釋性，以及自動化帶來的風險，對執行這些工作流程可以應用多少自動化進行了風險評估。

Similar to benchmarks used across industries to measure the scale of automation, we rank our automation within processes on a scale of zero to five. At zero, the process is entirely manual. At five, 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.

與各行業用來衡量自動化規模的基準類似，我們根據 0 到 5 的等級對流程中的自動化程度進行排名。從零開始，整個過程完全是手動操作。到第五步，整個流程將完全自動化，包括異常處理，無需人工幹預。我們正在自下而上地將此模型應用於 ICE 的每一個工作流程，準確衡量我們目前在 AI 模型自動化工作流程（無論是否有人工幹預）的成熟度方面所處的位置，以及基於當前技術水平我們可以達到的目標。

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.

目前，大多數生成式或智慧體人工智慧模型的核心都是模式識別，而且這種識別能力仍在不斷發展中。這意味著它們具有隨機性和機率性準確性，可以衡量人工智慧模型產生的結果的可靠性和可預測性。對於我們和我們的客戶所經營的受嚴格監管的業務而言，必須認識到機率結果的準確性必須達到什麼程度才能被認為可以接受完全自動化，以及在何種情況下仍然需要一定程度的人工幹預，尤其是在異常處理方面。

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.

今天，我們對未來的發展方向有了清晰的認識，並在許多領域積極推進，同時也兼顧了我剛才提到的風險。這就是我們的策略，也是我們的 ICE Aurora 平台的核心所在，我們已經在 ICE 領域看到了成效。人工智慧正在簡化和自動化跨系統的工作流程，加速產品開發，並大幅加快我們在 ICE 內部實現多個技術堆疊現代化的速度。重要的是，我們的目標是在不損害我們對資訊安全、資料管理和隱私的遵守的前提下實現這一目標。

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.

在我們的能源市場中，宏觀人工智慧和資料中心擴張趨勢預計將在未來十年推動顯著的能源需求。我們相信，我們的交易和清算平台能夠提供涵蓋整個能源領域的深度流動性和價格透明度，因此在為客戶提供支援方面具有獨特的優勢。

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.

儘管整體市場波動性有所下降，但今年第三季度仍是我們歷史上第二強勁的第三季度，僅次於去年同期創紀錄的第三季度，這主要得益於我們全球天然氣和電力市場的持續強勁表現，第三季度銷量分別同比增長 8% 和 18%。

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.

正如我們一直所說，未平倉合約是未來成長的領先指標，我們很高興看到它繼續呈上升趨勢，10 月份能源期貨未平倉合約創歷史新高，同比增長 14%，其中布倫特原油和 TTF 基準分別增長了 25% 和 30%。這體現了我們多元化能源平台的價值、我們充足的流動性以及客戶對我們基準的信心，這些基準作為數千份相關合約的全球價格參考點，提供跨地域的可信賴的價格透明度。

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.

在我們的全球天然氣業務組合中（涵蓋北美、歐洲和亞洲），今年迄今的產量增加了 20%。重要的是，今年迄今的強勁業績得益於廣泛的成長，包括北美業務成長 16%，歐洲業務成長 26%，以及亞洲 JKM 市場成長 27%。

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.

同時，我們的電力市場也持續成長，今年迄今銷量成長了 21%，本季成長了 18%。這進一步強化了天然氣和電力市場之間的協同效應，以及對提供透明度、靈活性和選擇性的綜合風險管理工具的需求。

In Fixed Income and Data Services, driven by multiyear 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.

在固定收益和數據服務領域，由於多年投資，我們的綜合平台又實現了創紀錄的季度收入，同比增長 5%，其中經常性收入增長 7%，數據和網絡技術業務增長 10%。我們的專有數據是我們業務的基石，也是我們在不斷發展的人工智慧領域中的關鍵差異化優勢。憑藉 50 多年的經驗，我們高品質的定價和參考數據為我們奠定了基礎，使我們成為如今全球最大的固定收益指數提供者之一。從基準指數和分析到客製化解決方案，我們支援整個 ETF 生態系統。

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 permits 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.

隨著人工智慧被嵌入到各個投資領域的交易策略中，我們預計我們的專有數據將變得越來越具有戰略重要性，我們的數據集將為依賴精確性、深度和大量歷史數據的人工智慧模型用戶提供競爭優勢。我們的資料在 ICE 的基礎設施內得到安全管理，並受到防火牆和權限的保護。我們的商業協議嚴格控制存取權限，只允許透過授權的交付管道進行特定用途。這種方法有助於確保我們的資料保持獨特性並進行策略部署，尤其是在模型越來越依賴高品質輸入來提升效能的情況下。

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 from Google, Meta, Amazon and several other AI models, 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.

在我們的參考資料業務中，我們利用人工智慧處理和驗證來自數百個來源的文檔，使用我們經過徹底測試的人工智慧模型，以確保其符合用途並具有高機率結果。這些人工智慧模型來自Google、Meta、亞馬遜和其他幾個人工智慧模型，在從固定收益招股說明書中提取參考資料方面，準確率超過 95%。這項能力是收集過程中至關重要的一部分，它提高了效率和交付速度，使我們能夠用相同的資源做更多的事情。

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.

目前，僅在我們的參考資料業務中，我們平均每月就使用人工智慧處理約 40,000 份文件。經人工智慧評估，達到預先定義置信度閾值的文件將直接進入我們的資料庫供客戶使用，而低於閾值的文件將被標記出來進行人工審核和介入。這項能力是收集過程中至關重要的一部分，它提高了效率和交付速度，使我們能夠用相同的資源做更多的事情。

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.

我們也利用機器學習來增強我們評估定價的關鍵組成部分。我們持續評估定價，將交易和報價數據結合，以預測債券價格，從而補充我們深厚的市場專業知識和數據品質工作流程。其他模型利用歷史數據來確定債券市場的買賣價差，機器學習能力顯著提高了評估質量，與市場實際交易進行比較時，評估質量明顯提高。

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 and 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.

同時，我們的 ICE 全球網路繼續在彈性、延遲和安全性方面樹立標準，將參與者連接到 750 多個資料來源和 150 多個交易場所，包括 ICE 和紐約證券交易所。ICE 雲端由 ICE 擁有和營運的最先進的資料中心組成，並可與主要第三方雲端供應商無縫集成，所有這些都在 ICE 的網路安全和營運彈性框架下進行。這為我們的客戶提供了靈活性，使他們能夠在最合適的地方存取 AI 工作負載，同時又不損害網路安全和營運控制。

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.

我們將繼續投資資料中心，以支援業務成長需求並滿足不斷增長的客戶需求，包括支援人工智慧策略的更廣泛應用。這是為了確保我們能夠獲得最具成本效益、最安全、最可靠的基礎設施，以滿足ICE和我們客戶現在和未來的需求。

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%.

在產品開發過程中，人工智慧正在利用 GitHub CoPilot 等工具自動執行資料分析、模式識別和重複性流程，使產品經理能夠專注於驗證和改進。這已經加快了某些產品上市的速度。例如，我們將索引限定、計算和報告的程式碼轉換時間縮短了大約 60%。

Demonstrating the new innovation underway across ICE, we're utilizing AI with our new sentiment indicator data sets including Reddit, Dow Jones and soon, 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.

為了展示 ICE 正在進行的新創新，我們正在利用人工智慧處理我們新的情緒指標資料集，包括 Reddit、道瓊斯以及即將推出的 Polymarket，並藉助 Google 和 Meta AI 模型來幫助處理這些資料集和識別模式。雖然這些資料集仍處於開發階段，但它們對尋求透過差異化資料輸入獲得競爭優勢的市場參與者來說特別有吸引力。這說明，對於依賴模型來支援交易決策的交易群體而言，我們的專有資料集將變得越來越重要。

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 alone for our customers, a foundational part of our mortgage strategy.

在我們的抵押貸款業務中，人工智慧的應用幫助我們簡化了房屋所有權體驗，提高了貸款和服務營運的效率，透過自助服務工作流程改善了借款人的體驗，透過抵押貸款生命週期中的自動化合規性和品質檢查降低了風險，同時提高了客戶的回收率。所有這些都有助於降低我們客戶的貸款發放和服務成本，這是我們抵押貸款策略的基礎部分。

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 ship. This execution reinforces our clients' trust in us to enhance and streamline their business workflows through our workflow automation capabilities.

例如，根據最近進行的一項客戶調查，使用我們行業標準貸款服務系統 MSP 的客戶在貸款服務成本上節省了大約 20% 到 30%，我們預計隨著我們已經推出或即將推出的新創新，例如我們增強的客戶服務、貸款登記、用於服務的 ICE 商業智慧和我們的損失緩解系統，這一數字將會增加。此次執行進一步鞏固了客戶對我們的信任，他們相信我們能夠透過工作流程自動化能力來增強和簡化他們的業務工作流程。

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 two new clients to MSP, both already on Encompass, and building on the two we signed in the second quarter, including UWM. We also signed 16 new Encompass clients, five of them already on MSP or an MSP subservicer.

第三季度，儘管宏觀經濟環境嚴峻，但營收年增 4%，交易收入成長 12%。我們也繼續贏得新客戶，為 MSP 簽下了兩家新客戶，這兩家客戶都已在使用 Encompass，並且鞏固了我們在第二季簽下的兩家客戶，其中包括 UWM。我們還與 Encompass 簽訂了 16 份新合同，其中 5 份已經是 MSP 或 MSP 子服務提供者。

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 one-third complete, and the remaining targeted to complete within two years. The original estimate to complete this project was baseline to take up to seven 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.

我們在將 MSP 從大型主機平台遷移到 ICE 的現代技術堆疊方面也取得了重大進展，從而提高了我們的敏捷性、成本效益和規模。在這裡，GitHub CoPilot 等工具幫助我們顯著提高了生產力，幫助我們在今年年底前重寫了整個用戶界面並遷移了 3000 萬行程式碼，其中大約三分之一已經完成，其餘部分計劃在兩年內完成。最初對完成專案的估計是基準時間最多需要七年，類似於我們收購 Interactive Data Corporation 後從大型主機遷移的過程。借助 GitHub CoPilot 和其他基於人工智慧的程式碼轉換工具，我們將預期的時間視窗縮短到最初預期時間的一半左右，這大大提高了我們現在將舊技術流程轉換為 ICE 現代技術堆疊的速度。

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.

我們在客戶服務領域也應用了我們的人工智慧應用模式，這是一個很有趣的領域。在這裡，我們的能力已經發展到條件自動化的水平，在這個水平上，自動化程度很高，但仍然需要人工幹預來處理異常情況。我們正在使用生成式人工智慧為客戶服務代表提供通話意圖預測，然後產生通話摘要。

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 chat bot designed to go beyond search capabilities, one that also executes real action, 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.

接下來，我們將應用智慧體人工智慧來實現部門間問題處理交接的自動化。然後，我們計劃更進一步，添加一個聊天機器人，其功能超越了搜尋功能，還能執行實際操作，例如在我們 ICE 抵押貸款技術服務數位應用程式中為借款人自助服務安排付款計劃。我們將努力進一步擴展，利用智慧虛擬代理來解決某些問題，在解決方案的成熟度和機率結果的品質與風險之間取得平衡。

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.

總而言之，隨著 ICE 不斷提升我們的領先技術，我們始終以客戶和最終消費者為中心，同時不斷考慮如何提高我們的營運效率，並提供有助於自動化工作流程的解決方案。

With that, I'll hand it over to Jeff.

這樣，我就把它交給傑夫了。

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.

謝謝你，本。請翻到第9頁投影片。鑑於 ICE 最近宣布與 Polymarket 建立投資和業務關係，我認為解釋我們對市場發展的看法可能會有所幫助。

ICE was an early investor in the crypto space, having been an early-stage funder of back and coin base. We made these investments in order 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 adhere to a high level of then existing securities and commodities regulation. We found, however, the 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 guarantors balance sheet.

ICE是加密貨幣領域的早期投資者，曾是Back和Coinbase的早期投資者。我們進行這些投資是為了密切關注區塊鏈技術在市場中的應用發展。就 Bakkt 而言，我們認為，如果代幣系統能夠遵守當時現有的高水準證券和商品監管規定，那麼它可能會被市場接受。然而，我們發現，在監管不確定時期，傳統的受監管金融公司採用代幣的速度較慢或不願採用代幣，尤其是在違約事件會將不需要的代幣轉移到金融擔保人的資產負債表上的情況下。

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.

美國現任政府和國會一直在努力解決這些不確定因素，這促使美國移民及海關執法局（ICE）更加積極地利用我們在過去十年中累積的知識。過去十年區塊鏈投資的一個重要宏觀趨勢是重塑銀行體系的軌道。例如，ICE在全球經營六個清算所，所有這些清算所都受到嚴格監管，並且必須在當地銀行營業時間、慣例和偏好的限制內運作。

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.

鏈上銀行服務現已在全球範圍內全天候運行，可實現即時追繳保證金和交易清算。這有助於增加資產抵押和貸款，一些資產持有者顯然正在利用這一點提高風險管理容忍度，並將多餘的貿易融資抵押品放入綜合穩定幣抵押池中。這筆超額抵押品資金由交易員透過放棄其抵押品的收益來籌集。先前受監管的清算機構無法使用的功能。

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.

ICE 決定投資 Polymarket，因為我們對其利用這種新型銀行基礎設施的智慧合約底層架構設計印象深刻。除了投資之外，我們還宣布了一項戰略數據協議，根據該協議，ICE 將成為 Polymarket 高度差異化的事件驅動型數據的全球分銷商。作為非體育預測市場的領導者，Polymarket 提供選舉、經濟指標和文化趨勢等事件的即時機率，提供強大的新洞察力，支持更明智的決策。

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 to both of us.

我們相信，憑藉我們的分銷管道、對市場的了解以及與客戶的長期合作關係，我們可以加速 Polymarket 被傳統金融體系接受的過程。我們相信 Polymarket 的工程團隊可以幫助 ICE 的工程師更了解我們自身對不斷發展的銀行技術的採用情況，這種合作關係已經為我們雙方帶來了收益。

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.

ICE 正在為我們的全球清算所推出先進的清算模式，我們將其優雅地命名為 ICE 風險模式 2。我們新的清算系統建立在現有的本地銀行和監管基礎設施之上，用於資金流動和抵押品管理。然而，當前的監管環境正面臨著使用代幣進行抵押品管理的挑戰，我認為這將有助於改善監管，以利用全天候的資本流動。

And ICE intends to be at the forefront of driving this evolution, given our own use case of operating six global clearing houses with different 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+ 2 day to T+1 day settlement times.

鑑於我們自身營運六個全球清算所，且每個清算所的抵押品和監管環境各不相同，ICE 打算走在推動這項變革的前沿。這種演變可以提高全球清算和貿易結算的效率。我們已經看到，有效利用抵押品通常會導致交易量和交易收入的增加。不難看出，自從美國股市將結算時間從 T+2 天改為 T+1 天釋放抵押品以來，美國股票市場的交易量已經大幅增加。

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.

除了重新定義資金流動方式外，Polymarket 還率先實現了新市場的快速上市，這得益於即時消費者需求的驅動。傳統交易所的新產品發布需要政府批准，而這最快也需要 30 天。在許多國家，時間還要更長。Polymarket 正在推動美國就如何最大限度地減少政府監管負擔展開對話，以免阻礙創新者。我們認為，這種對話最終將有利於所有市場的新產品創新，當然也有利於內燃機市場。

Now augmenting on Ben's comments on the adoption of artificial intelligence. We see the JAG in 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.

現在補充一下本關於人工智慧應用的評論。我們看到，無論是我們自身採用人工智慧，或是我們的客戶採用人工智慧，都體現了 JAG 在智慧領域的應用現象。在 ICE 內部，我們的工程師使用輔助工具來幫助他們更有效地編寫程式碼，尤其是涉及遺留程式碼現代化的專案上。然而，要大規模地全面部署生產程式碼，並達到 ICE 運作所需的延遲精度，我們仍然需要人工智慧領域目前尚不具備的獨特技能。因此，我們目前的經驗是，人工智慧已經成為我們團隊的得力助手，但不能取代團隊。

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.

本也重點介紹了我們在改善客戶服務方面對人工智慧的應用。人工智慧使我們的服務台在診斷即時問題方面更加高效，同時還能對客戶輸入進行分類和總結，從而創建更有效率的回饋循環。

The third area where we deployed AI is in our data gathering and data organizations such as cataloging bond and equity prospectuses, cleansing our data sets and organizing unstructured data for our vast financial data offerings.

我們在資料收集和資料組織方面部署了人工智慧，例如對債券和股票招股說明書進行編目、清理資料集以及為我們龐大的金融資料產品組織非結構化資料。

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.

最後，ICE 需要監督的大部分監管工作都是以模式識別形式進行的監視。同樣，人工智慧工具正在幫助我們的同事提高監督工作的效率。總而言之，我們內部的 AI 應用案例使我們的同事在工作中表現得更好。

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.

就我們的客戶對人工智慧的接受程度而言，我們在智慧領域也看到了同樣的現象，人工智慧在某些領域非常有用，但在其他領域卻不可靠。在我們的客戶使用 ICE 產品進行模式識別或語言組織方面，我們看到了積極的接受度。例如，我們看到我們的結構化和非結構化金融數據產品都得到了良好的市場認可。同樣，我們建構到抵押貸款網路中的人工智慧工具，例如我們的數據和文件自動化以及客戶互動套件，也引起了客戶的濃厚興趣。客戶採用這些工具來更有效地鎖定新業務並最大限度地降低抵押貸款入職成本，但這些工具並不能取代受監管監督的承銷決策，也不能複製將整個行業連接起來的龐大的ICE抵押貸款網絡，包括美國聯邦住房監管機構對GSE和聯邦住房貸款銀行抵押貸款持有情況的監管工作，以及向其提供每月抵押貸款服務資訊。

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 one day forward and with the expansion of retail trading leverage that's inherent in popular one-day options.

最後，許多人向我推測，交易總量的增加一定是因為人工智慧的普及應用。雖然這有可能發生，但我認為，更大的交易量影響來自於股票結算時間提前一天釋放出的資金，以及流行的單日期權固有的零售交易槓桿的擴大。

So all in all, we think the current state of AI is helping to control costs and control new hiring in -- 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.

總而言之，我們認為人工智慧目前的狀況有助於控製成本和控制新員工招募——對我們來說，它在一定程度上推動了銷售和交易成長。我們第三季創紀錄的業績，加上去年第三季非凡的業績，再次證明了我們全天候平台的強勁執行力。我們特意將公司定位為能夠在眾多地區和經濟條件下為客戶提供解決方案，從而實現全天候的業績目標。

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.

最後，我要感謝各位顧客一直以來的支持與信任，感謝你們的惠顧。我還要感謝ICE的同事們，感謝他們為公司歷史上最好的第三季所做的貢獻，繼我們上半年無與倫比的業績之後，也取得了公司歷史上最好的年初至今業績。

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.

現在我將把電話轉回給我們的主持人莉迪亞，我們將進行問答環節，直到美國東部時間 9:30。

(Operator Instructions) Our first question today comes from Ken Worthington with JPMorgan. your lines open.

（操作員指示）今天我們收到的第一個問題來自摩根大通的肯‧沃辛頓。請接通您的線路。

Hi, good morning. Thanks for taking the question. Believe it or not, my question is on the impact of AI, in the mortgage origination and servicing business, then really following up on your prepared remarks. So maybe first, how easy is it to incorporate the benefits of of AI and MSP and Encompass given what their tech stacks look like today? So 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 prospective ice mortgage technology clients to pursue efficiency on their own?

您好，早安。感謝您回答這個問題。信不信由你，我的問題是關於人工智慧對抵押貸款發放和服務業務的影響，然後才是對你準備好的發言的真正跟進。那麼首先，考慮到人工智慧、MSP 和 Encompass 目前的技術架構，將它們的優勢整合到自身系統中有多容易？你舉了一些例子，但是你能否將人工智慧應用到所有需要應用的領域，從而最大限度地提高你的競爭力？其次，您認為人工智慧能否幫助潛在的 ICE 抵押貸款技術客戶更容易自主提高效率？

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?

新技術帶來的希望是否會延長 ICE 獲得新的 Encompass 和 MSP 客戶所需的時間，尤其是在考慮大型客戶時？

Thanks, Ken. It's Ben. I'll take this. I think the -- in my mind, the best way to summarize the impact of AI on our mortgage origination and servicing platforms is that it's enabled us to transition these platforms from what have historically been seen as systems of record to a system of intelligence.

謝謝你，肯。是本。我要這個。我認為，總結人工智慧對我們的抵押貸款發起和服務平台的影響的最佳方式是，它使我們能夠將這些平台從歷史上被視為記錄系統轉變為智慧系統。

And what do I mean? So when you think about these core platforms, we are orchestrating incredibly complex and highly regulated business processes and workflows. We alluded to it in the comments multiple times, both Jeff and I did, that we also have an incredible network attached to us, thousands of customers, hundreds of network service providers, 35,000 settlement agents, tens of thousands of notaries as an example. And we're orchestrating communication not only of those clients connecting to us, but as important, if not more important, connectivity between our clients. And we have the proprietary information on how to orchestrate that workflow and how to make it more robust.

那我是什麼意思呢？因此，當我們思考這些核心平台時，我們實際上是在協調極其複雜且受到嚴格監管的業務流程和工作流程。我和傑夫都在評論中多次提到，我們還擁有一個龐大的網絡，例如數千名客戶、數百家網絡服務提供商、35,000名結算代理人、數萬名公證人等等。我們不僅要協調與我們建立聯繫的客戶之間的溝通，而且同樣重要，甚至更重要的客戶之間的聯繫。我們掌握著如何協調該工作流程以及如何使其更加穩健的專有資訊。

We also own and maintain the most robust compliance and underwriting guideline databases in the industry, and that's the reference data that's required to really automate underwriting workflows, which we're doing through our DDA platform. We also own and maintain the most comprehensive set of closing guidelines and rules for every county in the country, which enables our electronic closing and the e-reporting of loan transactions in the business that we acquired with Simplifile. We've also have significant proprietary data -- derived data offer our platforms that helps to inform our business intelligence models and enable our clients to find more operational efficiencies and business efficiencies that our clients can benefit from.

我們還擁有並維護著業界最強大的合規和核保準則資料庫，這是真正實現核保工作流程自動化所需的參考數據，而我們正在透過我們的 DDA 平台來實現這一點。我們還擁有並維護全國每個縣最全面的貸款結算指南和規則，這使得我們能夠在收購 Simplifile 後，對貸款交易進行電子結算和電子報告。我們還擁有大量專有數據——衍生數據為我們的平台提供信息，幫助我們完善商業智能模型，使我們的客戶能夠找到更多運營效率和業務效率，從而使我們的客戶受益。

So you take all of this together and how we're applying AI throughout each business process from a bottom-up perspective using that Aurora process that I had mentioned. Going through business process by business process, understanding what the probabilistic accuracy of a pattern recognition model that AI is providing and what's the business tolerance around the regulatory rules, the compliance associated to how much automation can be applied versus when human intervention needs to take place.

所以，把所有這些因素綜合起來，看看我們如何從自下而上的角度，運用我之前提到的 Aurora 流程，將人工智慧應用於每個業務流程。逐一分析業務流程，了解人工智慧提供的模式識別模型的機率準確度，以及企業對監管規則的容忍度，以及在多大程度上可以應用自動化，以及何時需要進行人工幹預等方面的合規性。

So we're extraordinarily well positioned to take advantage of this. And it shows up in our results. We had our highest quarter of the year in terms of sales in the third quarter. Across our ICE Mortgage Technology segment, we had two MSP clients, both of which are already on Encompass signed in the last quarter, and that's on top of the two that we had last quarter, including one of the largest lenders in the U.S. with United Wholesale Mortgage.

因此，我們擁有得天獨厚的優勢來利用這項機會。這一點在我們的結果中也有所體現。第三季度，我們的銷售額創下了全年新高。在我們的 ICE 抵押貸款技術部門，我們在上個季度新增了兩家 MSP 客戶，這兩家客戶都已在 Encompass 上簽約。此外，上個季度我們還新增了兩家客戶，其中包括美國最大的貸款機構之一 United Wholesale Mortgage。

And then we had 16 Encompass wins, five of which are on MSP or MSP subservicers that are really buying into our vision of the benefits of a front-to-back workflow. So we feel very well positioned, and we're looking at the funnel behind that, we feel like we're in a very strong position.

然後我們獲得了 16 個 Encompass 訂單，其中 5 個是 MSP 或 MSP 子服務商的訂單，他們真正認同我們關於端到端工作流程優勢的願景。所以我們感覺自己處於非常有利的位置，而且我們審視了背後的管道，感覺我們處於非常強大的地位。

Thank you. Our next question comes from Dan Fannon with Jeffrey.

謝謝。下一個問題來自丹·範農和傑弗裡。

Please go ahead.

請繼續。

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?

關於抵押貸款，這裡還有個問題。沃倫，你針對第四季度發表了一些關於 Flagstar 的近期評論，但你能否更詳細地闡述一下短期動態，以及 PennyMac，該公司在本季度宣布他們也將隨著時間的推移離開你的平台。如今，這種貢獻體現在哪裡？

Sure. Thanks for the question, Dan. So in terms of the third quarter, which I think is what you're referring to, yes, we were a little bit lower by a few million dollars. There were three real reasons for that. So first, -- and we mentioned this a little bit last quarter, was there was the roll off of -- the typical roll-off of inactive loans on MSP. That came in a little bit higher than we anticipated. But that said, active loans on MSP ticked tire for the first time in a few quarters, too. So there was a positive there on that front.

當然。謝謝你的提問，丹。所以，就第三季而言（我想你指的是第三季），是的，我們的收入比上一季少了幾百萬美元。這背後有三個真正的原因。首先，——我們在上個季度稍微提到過——MSP 上不活躍貸款的典型註銷。這個結果比我們預期的略高一些。但即便如此，MSP 上的活躍貸款數量也幾個季度以來首次出現疲軟。所以，在這方面還是有正面因素的。

And the second component of that too is, and you heard us talk a little bit this last couple of quarters, we did have some customers renew at slightly lower minimums than we had expected. But overall, we do continue to see the discount to prior minimums narrowing versus last year, and the percent of loans above the minimums are improving, which is helping our transaction fees.

第二個方面是，正如你們在過去幾個季度聽到我們談到的，我們的一些客戶續約時的最低金額比我們預期的要低一些。但總體而言，我們確實看到與去年相比，先前的最低貸款額折扣正在縮小，高於最低貸款額的貸款比例也在提高，這有助於降低我們的交易費用。

And then third, we did have some implementations in the fourth and the first quarter of next year, just really all based on customer needs. But as Ben noted, we just noted we had the best quarter of the year for sales across the platform. Not all of those, of course, hit in the current quarter in the fourth quarter, but certainly a good forward-looking indicator for the business as you think about next year.

第三，我們確實在第四季和明年第一季進行了一些實施，這完全是基於客戶需求的。但正如本所指出的，我們剛剛注意到，我們平台的銷售額迎來了今年最好的一個季度。當然，並非所有這些目標都能在本季或第四季實現，但對於展望明年而言，這無疑是一個很好的前瞻性指標。

So all that together is nothing terribly significant on a stand-alone basis, but did have to a couple of revenues coming a bit lighter. And that sort of impacts the fourth quarter from a run rate standpoint and also some of the implementations too that I noted have an impact in the fourth quarter as well. And then, of course, as you mentioned, Flagstar, that will roll off in the fourth quarter, which has an impact, but we had mentioned that before.

所以，所有這些加起來，單獨來看並沒有什麼特別重大的影響，但確實有一些收入有所減少。從運行率的角度來看，這會對第四季度產生影響，而且我注意到的一些實施措施也會對第四季度產生影響。當然，正如你所提到的，Flagstar，這種情況會在第四季度結束，這會產生影響，但我們之前已經提到過這一點。

In terms of PennyMac, I think the way to think about that is it's probably about half point of growth, but that won't be an impact for us until 2028. And to be clear, it's a half point on recurring revenue that, that would have an impact on. But -- and again, not until 2028, would we expect to see that.

就 PennyMac 而言，我認為可以這樣理解：它的成長幅度可能約為 0.5 個百分點，但這要到 2028 年才會對我們產生影響。需要明確的是，這將對經常性收入產生0.5個百分點的影響。但是——而且再次強調，我們預計要到 2028 年才能看到這種情況。

Our next question comes from Ben Budish with Barclays. Please go ahead.

下一個問題來自巴克萊銀行的本·布迪什。請繼續。

Hi, good morning and thanks for taking the question. Maybe following up on Jeff's commentary on Polymarket. I was wondering, maybe first, if you could give us any more details about the data licensing or redistribution arrangements? What sort of P&L impact might that look like? And then maybe you bought -- you took a big stake in the company, can you talk a bit about your longer-term plans? Do you have any plans to list event contracts. We've heard your competitor talking about that quite a bit. Or is this more about the partnership?

您好，早安，感謝您回答這個問題。或許可以接著 Jeff 對 Polymarket 的評論做個補充。我想先問一下，您能否提供更多關於數據許可或再分發安排的細節？這會對損益表產生什麼樣的影響？然後，也許您收購了——您持有該公司的大量股份，您能談談您的長期計劃嗎？您是否有計劃列出活動合約？我們經常聽到你的競爭對手談論這件事。或者，這更多的是關於合作關係？

And maybe -- sorry to squeeze another one in there, but to what degree is that the blockchain technology itself part of the appeal rather than sort of a means to an end to access this type of trading type of new market data points? Thank you.

或許——抱歉再插一句——區塊鏈技術本身在多大程度上是其吸引力的一部分，而不是獲取這類交易新市場數據點的手段？謝謝。

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 has 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.

這位是克里斯·埃德蒙茲。我來回答你問題的第一部分，剩下的部分就交給傑夫來解答吧。但對數據本身的情緒分析已經成為我們客戶一個有趣的回饋循環。根據我們使用 Reddit 數據以及 Ben 在準備好的評論中提到的道瓊斯數據的經驗，我們看到了客戶的巨大需求。因此，現在我們能夠獲取這些訊號，並圍繞這些訊號創建一個市場，然後從 Polymarket 上發生的活動中獲得反饋循環，這確實為我們提供了一個圍繞這些訊號建立完整生態系統的機會，而這正是推動客戶對此產生興趣的原因。正是這一點促使我們萌生了成為這些數據分發者的想法，以確保我們的客戶能夠在我們的生態系統中使用這些數據。

And I think -- as I tried to -- this is Jeff. I think what I mentioned -- tried to convey in my prepared remarks was that we really believe Polymarket has done something particularly innovative and special in the way they have historically settled their contracts. And it's through blockchain non-intermediated settlement between two parties sending tokens on a second layer that they've been adopting that gives them some performance capabilities. And we wanted to learn more about that, get our engineers more involved in it because you can see the trends in traditional finance are that there are going to be more assets that are tokenized, potentially, bank deposits, and we won't think that, that will ultimately make its way into the clearing infrastructure and allow us to better run 24/7.

我想——正如我努力辨認的那樣——這就是傑夫。我認為我在準備好的演講稿中提到的——試圖表達的是，我們真的相信 Polymarket 在歷史上處理合約的方式上做了一些特別創新和特別的事情。他們採用的區塊鏈技術，即在交易雙方之間透過區塊鏈進行非中介結算，在第二層發送代幣，從而賦予了他們一定的性能能力。我們想更多地了解這方面，讓我們的工程師更多地參與其中，因為你可以看到傳統金融的趨勢是，會有更多資產被代幣化，例如銀行存款，我們認為這最終會進入清算基礎設施，使我們能夠更好地全天候運行。

The thing for us, as I mentioned a couple of times in my prepared remarks, the fact that we have six clearing houses means that clients tend to keep excess collateral at all six because of the banking hours that are required to move capital around when those particular clearing houses are open. And we think, by having 24/7 collateral management, we'll be able to minimize overall collateral requirements for our customers. And that will feed its way into higher trading volumes, which is we have seen that correlation. And so it's in our interest to help make our customers trading more efficient.

正如我在準備好的發言稿中多次提到的，對我們來說，我們有六個清算所，這意味著客戶往往會在所有六個清算所都保留多餘的抵押品，因為在這些清算所營業的時間段內轉移資金需要銀行的營業時間。我們認為，透過全天候的抵押品管理，我們將能夠最大限度地減少客戶的整體抵押品要求。這將導致交易量增加，我們已經看到了這種相關性。因此，幫助客戶提高交易效率符合我們的利益。

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 with -- 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.

我想補充一點，我們花了 20 多年時間，真正深入了解商業用戶及其工作流程和全球供應鏈，並幫助他們管理商業風險，從而創造了 ICE。我們在零售領域，甚至在高頻消費領域，從來沒有特別強大的實力。其他人都專注於此，而我們則非常非常注重商業利益。所以與 Polymarket 建立合作關係是件好事，因為他們真的教會了我們他們是如何與零售客戶打交道的，他們是如何利用有限的資金資產開展卓有成效的地面營銷，並真正用有限的資產負債表打造品牌和品牌知名度的。所以，我們再次對他們的所作所為表示欽佩。

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.

我們試著向他們普及傳統金融知識，同時他們也向我們普及消費金融知識。希望這在未來能為我們雙方帶來回報，但團隊合作互相學習是理所當然的，希望一加一能等於三。

Our next question comes from Patrick Moley with Piper Sandler. Your line's open.

下一個問題來自派崔克·莫利和派珀·桑德勒。您的線路已開通。

Yes. Maybe just double clicking on Ben's question on Polymarket and just at kind of the contract level, in prediction markets, a lot of the volumes we've seen so far has been in sports contracts. There's been a lot of lawsuits and questions about whether regulators are going to allow that to proliferate, but it seems like in the next few years, if they do allow it to continue, you could see a lot of sports book volumes move on Exchange. So just wondering if you -- what you think -- how you see that playing out and what opportunity could present for Polymarket and ICE. And maybe just if you can talk about how you see sports contracts versus non-sports contracts and their applicability at the commercial level progressing from here?

是的。或許只需雙擊 Ben 在 Polymarket 上提出的問題，然後看看預測市場的合約層面，我們目前看到的許多交易量都來自體育合約。關於監管機構是否會允許這種現象蔓延，已經出現了很多訴訟和疑問，但似乎在未來幾年，如果他們允許這種現象繼續下去，你可能會看到很多體育博彩交易量轉移到交易所。所以我想知道您——您認為——這件事會如何發展，以及這可能為 Polymarket 和 ICE 帶來​​什麼機會。或許您可以談談您如何看待體育合約與非體育合同，以及它們在商業層面的適用性，以及未來的發展方向？

Sure. This is Jeff again. Well, I reached out the Shayne, the founder of Polymarket, early in the summer after it became clear that the Trump administration and the U.S. Congress was going to validate much of what was being done in stablecoins and ultimately on the blockchain. And it was in that environment that we began conversation, and that was before the NFL football season.

當然。我是傑夫。今年夏天初，我聯繫了 Polymarket 的創始人 Shayne，當時很明顯，川普政府和美國國會將要認可穩定幣以及最終區塊鏈領域的許多做法。正是在這種環境下，我們開始了對話，而那時 NFL 橄欖球賽季還沒有開始。

And we were attracted by their non-sports activities where they really are a global leader. And we really think that data and information, supply chain data, acts of God, weather, corporate actions, we think that kind of information is going to be very, very interesting to the traditional finance. In fact, we know it is anecdotally, Shayne and I are very well aware of many institutional investors that are already scraping data or finding data and making its way into -- informally into their traditional decision-making.

我們被他們在非體育領域的成就所吸引，他們在這一領域確實是全球領導者。我們真的認為，數據和訊息，供應鏈數據，天災，天氣，公司行為，我們認為這類資訊對傳統金融來說將非常非常有趣。事實上，我們知道，根據一些軼事，謝恩和我非常清楚許多機構投資者已經在抓取數據或尋找數據，並將其非正式地融入他們的傳統決策中。

And so sports was not something that really got our interest. I think it's great for Polymarket. If they can make a business around that and make earnings around that and certainly longer term for our equity stake in the business, that would be great. But we're not a venture firm. We don't -- you guys won't really reward us if we make a lot of money on that investment.

所以，我們對體育運動並沒有真正產生興趣。我認為這對Polymarket來說是件好事。如果他們能圍繞這個主題建立起業務，並從中獲得收益，而且從長遠來看，這對我們在公司中的股權投資也是有利的，那就太好了。但我們不是創投公司。我們不——如果我們從這項投資中賺了很多錢，你們就不會真的獎勵我們。

Honestly, I think we'll be rewarded if we can bring the underlying technologies into our workflow and increase our sales revenue and manage our costs. And so long-winded way of saying good for Polymarket if they can navigate the sports complex, kind of not eye on our list in terms of what we're going to contribute to them and what they'll contribute to us.

說實話，我認為如果我們能將底層技術融入我們的工作流程中，提高銷售收入並控製成本，我們就會獲得回報。長話短說，我的意思是，如果 Polymarket 能順利拿下運動中心，那就太好了。至於我們打算為他們做些什麼，他們又會為我們做些什麼，我們倒是不太關注他們的計劃。

Next question comes from Brian Bedell with Deutsche Bank. Please go ahead.

下一個問題來自德意志銀行的布萊恩·貝德爾。請繼續。

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.

偉大的。或許只能繼續還房貸了。沃倫，我只是想澄清一下關於第四季度的展望，我認為之前提到的第三季度到第四季度營收持平的預期，是指整個業務板塊嗎？還是那隻是針對定期付款的？我知道您確實提到了交易費用的季節性變化。

So if you could just clarify that. And then just longer term, outlook on that build of the revenue synergy, what's been action 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.

所以，如果您能解釋一下就太好了。那麼從長遠來看，對於建構收入綜效的前景，目前採取了哪些行動？你們的整合工作是否仍按原計劃進行？然後，或許可以談談更長遠的，關於區塊鏈和區塊鏈提供者在抵押貸款領域面臨的競爭的評論。我知道這比較偏向未來主義，但只是想聽聽你的看法。

All right, Brian, I'll try to hit the first two there and then hand it over to Ben. So yes, thank you for clarifying. So the comments in the script were referring to recurring revenue being around the same level as the third quarter. I did mention that, of course, there is a typical seasonal impact from just lower purchase volume that happens in sort of the winter months. You see that in the fourth and the first quarter of each year.

好的，布萊恩，我先試試能不能打中前兩個，然後就交給本。是的，謝謝你的解釋。所以劇本中的評論指的是經常性收入與第三季大致相當。我之前也提到過，當然，冬季月份購買量下降會帶來典型的季節性影響。你會發現，每年的第四季和第一季都是如此。

So I don't -- I'm not trying to give a specific guidance on that. I just think because we don't know where volumes are ultimately going to be in a particular period. So it's more of just a helpful guide for you guys to just sort of think through that as you update your models.

所以，我──我並不是想就此給予具體指引。我只是覺得，因為我們無法預知特定時期的成交量最終會達到什麼水準。所以，這更像是一份對你們有幫助的指南，讓你們在更新模型時可以思考這個問題。

I think your second question, if I remember, was around just maybe longer-term guidance. I think we'll give guidance on the fourth quarter call, of course, but the MBA is forecasting loan growth kind of in the high single-digit range right now. Industry originations will be slightly below the $6 million next year based on what they're seeing today. And I'm not confirming you're denying that, but that's kind of the information that's out there. So based on that, I would just point you back to the scenarios that we provided in the past where when we closed the Black Knight transaction that we would probably be more in the lower mid-single digit range in that kind of an environment.

我記得你的第二個問題好像是關於一些長期的指導意見。我們當然會在第四季財報電話會議上給予指引，但目前MBA預測貸款成長將維持在個位數高點。根據他們目前觀察到的情況，明年行業貸款發放量將略低於 600 萬美元。我並不是說你否認了這一點，但這確實是目前流傳下來的訊息。因此，基於此，我只想提醒您回顧我們過去提供的情況，當時我們完成 Black Knight 交易時，在這種環境下，我們的收益率可能會在個位數的中低水平。

But that obviously can change as interest rates move -- mortgage rates move, that can obviously change pretty quickly. So we'll have to see as we get closer to guidance next year in terms of what we provide there.

但隨著利率的變動，這種情況顯然也會改變──抵押貸款利率的變動速度非常快。所以，明年臨近發布指導意見時，我們才能確定我們將提供哪些服務。

Brian, I'll hit the competitive landscape question that you had towards the end. We -- customers, and I've said this in prior calls, customers continue to focus on having an independent, well-capitalized neutral technology provider to help develop and enhance this critical market infrastructure for them. And in particular, one that doesn't compete with them. And that's why we continue to have the sales success that we highlighted. Obviously, we've said it in this call, that we had the highest quarter in sales in the third quarter than we've had all year. So we're continuing to have a lot of success in there.

布萊恩，我會回答你最後提出的關於競爭格局的問題。我們——客戶，正如我在先前的電話會議中所說，客戶仍然專注於擁有一個獨立、資金雄厚的、中立的技術供應商，以幫助他們開發和增強這一關鍵的市場基礎設施​​。尤其是一種不會與它們競爭的產品。正因如此，我們才能持續取得先前提到的銷售佳績。正如我們在這次電話會議中所說，第三季的銷售額是全年最高的。所以我們在那方面持續取得了巨大的成功。

On the landscape itself, there was a question about PennyMac earlier. The reality is with PennyMac, just a little bit of history on that, that there was a long-standing dispute between PennyMac and Black Knight. An arbiter found that PennyMac used our confidential information to build a servicing system. So it wasn't a surprise to us, to be honest with you, after buying Black Knight, that they took an ownership stake in a platform, and they are trying to build a loan origination system to potentially move to over time. So it's not a surprise. But again, there's -- in our mind, it's not a neutral independent platform.

關於景觀本身，之前有人問過 PennyMac 的問題。事實上，關於 PennyMac，簡單回顧一下它的歷史，PennyMac 和 Black Knight 之間存在著長期的糾紛。仲裁員認定 PennyMac 利用我們的機密資訊建構了一個服務系統。坦白說，收購 Black Knight 後，他們入股某個平台，並試圖建立一個貸款發起系統，以便日後可能轉型，這並沒有讓我們感到驚訝。所以這並不令人意外。但話說回來，在我們看來，它並不是一個中立的獨立平台。

And then you have the [Rocket] conversation that we have, our understanding is that Rocket's moving their loans to their -- to a legacy Cooper platform mainframe system called LSAMS. It's not going to Sagent. And they've decided that they want to have their own proprietary custom system that is mainframe based to go to.

然後，我們與 Rocket 進行了對話，據我們了解，Rocket 正在將其貸款轉移到名為 LSAMS 的傳統 Cooper 平台大型主機系統。它不會送到薩金特那裡。他們決定要擁有自己專屬的、基於大型主機的客製化系統。

And then you look at platforms like we have with MERS, where MERS is a comprehensive platform, handles first and second loans. It's got legal standing within the mortgage processes. It's got proven expertise in the bankruptcy foreclosure space. It's an incredible business that's run with an independent Board. Board members that are part of the industry, and it's a great business for us.

然後你看看像我們 MERS 這樣的平台，MERS 是一個綜合平台，可以處理第一筆和第二筆貸款。它在抵押貸款流程中具有法律效力。該公司在破產止贖領域擁有成熟的專業知識。這是一家由獨立董事會管理的卓越企業。董事會成員都是業內人士，這對我們來說是一項很棒的事業。

So you take all of that and then our positioning of where we're, again, an independent well-capitalized, proven technology provider for many, many different industries, and that we're neutral and don't compete with our customers, we think we're very well positioned.

所以，綜合所有因素，再加上我們的定位——我們是一家獨立的、資金雄厚的、經過驗證的技術提供商，服務於眾多不同的行業，而且我們保持中立，不與客戶競爭——我們認為我們的定位非常有利。

Thank you.

謝謝。

Our next question comes from Alex Blostein with Goldman Sachs. Please go ahead.

下一個問題來自高盛的 Alex Blostein。請繼續。

Hey, good morning, everybody. Thank you for the question. 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 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.

嘿，大家早安。謝謝你的提問。我原本希望回到您之前在關於人工智慧計劃的準備發言中提到的一個觀點，即工作流程自動化，而您也花了相當多的時間來討論各種流程。當你把視角拉遠時，我想，我們的目標是什麼？你們認為這項措施實際上能為公司節省多少成本？這件事需要多長時間？您打算如何考慮將這些節省下來的資金進行再投資，還是讓它們直接計入最終收益？或許也能幫助我們理解這對公司長期獲利能力意味著什麼。

Alex, it's Ben. So we went through, and I alluded to in my prepared remarks that we have a strategy and a process that we're applying across ICE, that ICE Aurora platform. And for us, it's really about literally breaking down business process by business process internally that we have within ICE as well as the solutions that we're providing to customers. And figuring out on our automation scale, how much automation can be applied, where and when human intervention should be applied along that because we and our customers operate extraordinarily highly compliant regulated businesses in all of the areas where we operate.

Alex，我是Ben。因此，我們進行了討論，我在事先準備好的演講稿中也提到過，我們有一個策略和一個流程，我們正在整個 ICE 系統上應用，那就是 ICE Aurora 平台。對我們來說，這其實就是要逐一分解ICE內部的業務流程以及我們為客戶提供的解決方案。並且，我們需要根據我們的自動化規模來判斷可以應用多少自動化，以及在何時何地應該進行人工幹預，因為我們和我們的客戶在我們運營的所有領域都經營著高度合規的監管業務。

And at the end of the day, these AI models, their pattern recognition software that have various levels of probabilistic outcomes and some are really -- some processes are really good and apt to be to move towards almost full automation, and there's others where you've got to have human intervention, especially in the exception handling process because in some areas like compliance checks, for example, in mortgage, it's going to be a very low level of tolerance accepted.

歸根結底，這些人工智慧模型及其模式識別軟體具有不同程度的機率結果，有些流程確實非常出色，並且有可能實現近乎完全的自動化，而另一些流程則需要人工幹預，尤其是在異常處理過程中，因為在某些領域，例如抵押貸款中的合規性檢查，可接受的容忍度非常低。

So what we're seeing through this is, are we seeing efficiency gains? Absolutely, we're seeing efficiency gains. Where, right now, our best guess from the way we've been applying is, is that we're going to be able to do more with the same, more with the same number of people. We're going to be able to speed to market as the types of offerings that we want to provide, the types of solutions that we want to provide to our customers. There's more and more demand for us to do more, and we think we'll be able to do that with the same head count that we've had historically.

所以，我們透過這個例子看到的是，我們是否看到了效率的提升？當然，我們看到了效率的提升。根據我們目前的申請情況來看，我們最好的猜測是，我們將能夠用同樣的人力完成更多的工作。我們將能夠快速地將我們想要提供的產品和服務，以及我們想要為客戶提供的解決方案推向市場。對我們工作的需求越來越大，我們認為在保持以往人員規模不變的情況下，我們能夠做到這一點。

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

下一個問題來自加拿大皇家銀行資本市場的 Ashish Sabadra。請繼續。

Hey, good morning, guys. This is Will Qi on for Ashish Sabadra. Just with the continued strength we've seen on your data services and solutions businesses across ICE, can you maybe give a little bit of a commentary on the drivers there? Where maybe the appetite is coming from a customer perspective, either kind of quantity of data consumed versus pricing? And also with the development 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?

嘿，大家早安。這裡是Will Qi，為您報道Ashish Sabadra。鑑於我們在 ICE 的數據服務和解決方案業務方面看到的持續強勁勢頭，您能否就其驅動因素做一些評論？從客戶角度來看，這種需求可能源自於何處？是數據消耗量還是價格？此外，隨著情緒指標等新型高價值資料集的發展，這是否可以說是推動這些領域成長的另一個助力呢？

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 that. Certainly, the high-value assets that you made reference Tier-1.

是克里斯。感謝您的提問。我認為它比這更全面。這是一套完整的操作指南，你可以從客戶端充分利用它，而這正是它引起共鳴的原因。當然，你提到的高價值資產指的是一級資產。

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.

但是，如果你看看我們在整個 Exchange 空間中擁有的關鍵任務數據，你會發現，這是人們逐漸了解並信任我們有能力將這些數據交付到他們的系統中的基礎，並根據他們在特定時間認為最合適的交付管道進行交付。而且還可以添加其他內容，無論是我們討論過的新內容，還是他們可以從其他來源獲得的其他數據。

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.

正如我們在準備好的演講稿中所說，我們有 750 個不同的資料來源，可以接觸到這些不同的交付機制。正如沃倫和本在事先準備好的演講稿中所說，我們對這些能力進行了投資。這些投資正在獲得回報，您可以看到客戶有能力做出這些改變，並將這些機會融入他們的營運工作流程中。

Thank you. 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.

謝謝。我們沒有其他問題了。那麼，我想把電話轉回給董事長兼執行長傑夫‧斯普雷徹，請他作總結發言。

Well, thank you, Lydia. I appreciate the way you managed the call today, and thank you all for joining us this morning.

謝謝你，莉迪亞。我非常欣賞你今天主持電話會議的方式，也感謝各位今天上午參加會議。

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.

我再次感謝所有同事，感謝他們為我們公司創造了公司歷史上最好的第三季業績；再次感謝我們的客戶，感謝他們一直以來的支持和對我們業務管理方式的信任。我們會盡快回來繼續為您帶來最新消息。但同時，我們將繼續努力為客戶進行創新，並繼續建立我們的全天候商業模式。謝謝，祝您今天過得愉快。

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

通話到此結束。非常感謝您的參與。現在您可以斷開線路了。