Hello, and welcome to the EXL Service Holdings, Incorporated Q3 2024 earnings call. We ask that you please hold all questions until the completion of the formal remarks, at which time you will be given instructions for the question-and-answer session. Also, as a reminder, this conference is being recorded today. If you have any objections, please disconnect at this time. I will now turn the call over to John Kristoff, Vice President of Investor Relations. Thanks, Leila. Hello, and thank you for joining EXL's third quarter 2024 financial results conference call. On the call with me today are Rohit Kapoor, Chairman and Chief Executive Officer, and Maurizio Nicolelli, Chief Financial Officer. We hope you had an opportunity to review the third quarter earnings press release we issued yesterday afternoon. We have also posted a slide deck and investor fact sheet in the Investor Relations section of our website. As a reminder, some of the matters we'll discuss this morning are forward-looking. Please keep in mind that these forward-looking statements are subject to known and unknown risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements. Such risks and uncertainties include, but are not limited to, general economic conditions, those factors set forth in today's press release, discussed in the company's periodic reports, and other documents filed with the SEC from time to time. EXL assumes no obligation to update the information presented on this conference call today. During our call, we may reference certain non-GAAP financial measures, which we believe provide useful information for investors. Reconciliation of these measures to GAAP can be found in our press release, slide deck, and investor fact sheet. And with that, I'll turn the call over to Rohit. Thanks, John. Good morning, everyone. Welcome to EXL's third quarter 2024 earnings call. I'm pleased to be with you this morning reviewing our strong financial results. In the third quarter, we generated revenue of $472 million and an increase of 15% year over year. We also grew third quarter adjusted EPS by 16% to $0.44 per share. The sound execution of our data and AI-led strategy enabled us to further accelerate our growth momentum across both our analytics and digital operations and solutions businesses during the quarter. In analytics, we delivered revenue of $204 million for the quarter, up 5% sequentially and 11% year over year. This marks the strongest sequential quarterly growth for analytics in six quarters. And we are delivering on our previously stated objective to accelerate our analytics growth both sequentially and year over year in the second half of 2024. We continue to drive strong double-digit growth across healthcare payment services and data management. While we remain cautious on overall demand for discretionary projects, we are seeing growth in analytics services across our vertical markets, including insurance, healthcare, and banking. In our digital operations and solutions business during the third quarter, we delivered strong double-digit growth as we leverage our domain, data, and AI capabilities to win in the market. We grew revenue 5% sequentially and 18% year over year to $268 million. This revenue acceleration was driven by double-digit growth across all three of our digital operations segments. We continue to experience a stable and favorable demand environment in digital operations driven by our clients' focus on cost efficiency and digital transformation. Our strategy to leverage our domain expertise, data management capabilities, and AI services delivery not only helps our clients achieve real-world benefits from AI, but it also differentiates us from our competitors, expands our total addressable market, and extends our runway for growth. This is beginning to manifest itself in our sales pipeline, which is up strong double digits year to date. We have grown the number of large deals over $25 million in total contract value by more than 25% year over year. This gives us confidence that our solutions and capabilities are being recognized as industry-leading by our clients. While it is still early innings, particularly with generative AI, I would like to provide details on how we are building new revenue streams across two of our primary capabilities: data modernization and AI implementation. Let me describe our business model in each area. To implement AI, we start with data modernization by securing and structuring high-quality data, the foundational asset for any AI application. This includes all aspects of optimizing our clients' data estate, including helping them gather data, structure data, migrate data to the cloud, and build more reliable, robust data pipelines. We have continued to enhance our data management capabilities during the quarter with the acquisition of ITI Data and our expanded partnership with Databricks. The acquisition of ITI Data not only enhances our data engineering capacity but also grows our existing client footprint in banking. Under our expanded partnership with Databricks, we are building a robust team of Databricks-certified talent and deploying new data management and generative AI solutions into the Databricks ecosystem. Together, these strategic actions are speeding the development and implementation of cutting-edge data modernization solutions for our clients. Once the data foundation is in place, we select and embed the best combination of AI models into the workflow in order to optimize for cost, latency, and accuracy. Our repertoire of AI models includes advanced pre-trained language models, domain-specific fine-tuned models, and intelligent AI agents suited for targeted tasks. These models are then integrated into the workflow along with human-in-the-loop. This not only requires technical expertise in designing the most effective AI architecture but also deep domain knowledge to provide context and increase adoption to deliver superior business outcomes. An illustrative client example involves a large multinational pharmaceutical client where we have integrated agentic AI into their audit processes to drive significant improvements. The AI multi-agent system we developed handles various aspects of the audit process. One AI agent specializes in anomaly detection by analyzing documents for compliance issues, while another AI agent focuses on summarizing findings for auditors. Together, these AI agents collaborate to cover all audit stages, from identifying risks to generating actionable recommendations and contextualizing them against industry frameworks based on the specific needs of each audit. The result for our client has been a 60% reduction in their audit completion time, an improvement in compliance accuracy, and overall greater assurance. This is just one example of how EXL is leveraging its domain, data, and AI expertise today to deliver real-world tangible benefits to our clients. In addition to our capabilities in data modernization and AI implementation services, we have developed a number of proprietary generative AI solutions. One prominent example is our recently announced Insurance LLM, which is specifically designed to handle the nuances of auto bodily injury, workers' compensation, and general liability claims. Our LLM was built on EXL's 25 years of experience in the insurance industry and more than a decade of proprietary claims-related data. We currently have two clients live with this LLM: a large U.S.-based casualty and life insurer and a regional casualty insurer. As we implement our proprietary insurance LLM across multiple clients, we can access additional data sets to further fine-tune our LLM and improve its performance. The combination of enhanced productivity and faster claim resolution with lower indemnity costs and claims leakage is a powerful value driver for our clients. We intend to continue to build out our IP going forward by developing new proprietary solutions, including additional domain-specific LLMs and autonomous agents. We recently launched the new EXL Enterprise AI Platform to accelerate development of these solutions, leveraging NVIDIA AI software. I'm pleased to say that our Insurance LLM, Smart Agent Assist, and Code Harbor solutions are now live on this platform. From a revenue perspective, we can embed these proprietary AI solutions into long-term services contracts as an integral part of the overall solution, or we can offer them as standalone solutions based on client needs. One of the keys to maintaining our competitive advantage in data and AI is our ability to attract, develop, and retain the best talent in the industry. Our goal is to foster an environment conducive to continuous innovation amongst our more than 57,000 employees. To that end, we continue to attract top talent to EXL. We recently welcomed Sanjay Joshi as Senior Vice President and Global Leader for our data management practice. Sanjay comes to EXL with extensive leadership experience building data practices across large, diverse clients. He most recently served as a Senior Data Practice Leader at Accenture. In addition, Sarath Varanasi recently joined EXL as Senior Vice President and Global Leader for Insurance Analytics. Sarath has nearly 30 years' experience as a consulting leader in the insurance industry. He joins us from Cognizant, where he most recently led their North America insurance business unit. We also continue to invest heavily in employee development. Our employees have completed nearly 1 million hours of training this year. Nearly 800 of our engineers have completed certification training on our partners' data and AI platforms, and we have another 1,200 enrolled. These critical certifications include partner platforms from NVIDIA, Databricks, Google, AWS, and Microsoft Azure. We are not only creating career opportunities for our employees by helping them add marketable skills, but we are also enabling them to work with a purpose in a growth environment. The result has been stable attrition levels and a growing pool of highly skilled AI talent. In summary, we delivered strong results in the third quarter, and we are encouraged by the continued acceleration of growth across our analytics and digital operations and solutions businesses. The investments we are making in developing new AI-based solutions, expanding our industry partnerships, and developing our employees puts us in a strong position to successfully execute on our data and AI-led strategy and continue our growth trajectory. With that, I'll turn the call over to Maurizio to cover our financial performance in detail. Thank you, Rohit, and thanks everyone for joining us this morning. I will provide insights into our financial performance for the third quarter and nine months ended September 30, followed by our revised outlook for the full year. We delivered a strong third quarter with revenue of $472.1 million, up 14.9% year-over-year on a reported basis. On a constant currency basis, we grew revenue 14.5% year-over-year and 4.9% sequentially. Approximately 1% of our revenue this quarter came from non-recurring revenue that is not expected to repeat in the subsequent quarter. Adjusted EPS for the quarter was $0.44, a year-over-year increase of 16.3%. All revenue growth percentages mentioned hereafter are on a constant currency basis. Revenue from our digital operations and solutions businesses, as defined by three reportable segments excluding analytics, was $268.1 million, which represents year-over-year growth of 17.1%. Sequentially, from the second quarter of 2024, we grew revenue 4.8%. In the insurance segment, we generated revenue of $157.6 million, an increase of 15.1% year-over-year and 5.2% sequentially. This growth was driven by the expansion of existing client relationships and ramp-up of new client wins. The insurance vertical, consisting of both our digital operations and solutions and analytics businesses, grew 14.9% year over year with revenue of $197 million. In the emerging segment, we grew revenue 21.7% year over year and 2.8% sequentially to $80 million. This growth was primarily driven by expansion of existing client relationships and ramp-up of new client wins in 2024. The emerging vertical, consisting of both our digital operations and solutions and analytics businesses, grew 12.4% year over year with revenue of $166.9 million. The healthcare segment reported revenue of $30.5 million, growing 16.5% year over year and 8.5% sequentially. This growth was driven by higher volumes and expansion of existing client relationships in our clinical services business. The healthcare vertical, consisting of our digital operations and solutions and analytics businesses, grew 17.2% year over year with revenue of $108.1 million. In the analytics segment, we generated revenue of $204 million, up 11.3% year over year and 5% sequentially. Growth in analytics was driven by higher volumes in payment services, new client wins, and expansion in existing client relationships in our data management and analytics services businesses. SG&A expenses, as a percentage of revenue, were down 10 basis points year over year to 20.1%, driven by operating leverage. Our adjusted operating margin for the quarter was 19.9%, down 10 basis points year over year, driven by higher depreciation on new operating centers opened in 2024. Our effective tax rate for the quarter was 22.8%, down 60 basis points year over year. This was driven by higher profits in lower tax jurisdictions. Our adjusted EPS for the quarter was $0.44, a 16.3% increase year over year on a reported basis. Turning to our nine-month performance, our revenue for the period was $1.36 billion, up 11.5% year over year on a reported basis and 11.4% on a constant currency basis. Year over year on a constant currency basis, digital operations solutions grew 14.6% and analytics 7.4%. Adjusted operating margin for the period was 19.6%, down 20 basis points year over year. Our first nine months' adjusted EPS was $1.22, up 12.1% year over year on a reported basis. Our balance sheet remained strong. Our cash, including short and long-term investments, as of September 30, was $335 million, and our revolver debt was $345 million for a net debt position of $10 million. We generated cash flow from operations of $110.1 million in the third quarter, compared to $68.6 million, a 60% increase year over year driven by profitability and improved working capital management. During the first nine months, we spent $36 million on capital expenditures and repurchased approximately six million shares at an average cost of $30.70 per share for a total of $184.7 million. This includes approximately 4.2 million shares received as part of our previously announced $125 million accelerated share repurchase plan. Now, moving on to our outlook for 2024. Based on our strong year-to-date performance and our current visibility for the remainder of the year, we are raising the full-year revenue and earnings guidance. We now anticipate revenue to be in the range of $1.825 billion-$1.835 billion, representing year-over-year growth of 12%-13% on a reported basis and approximately 12% on a constant currency basis. This represents an increase of $12 million at the midpoint from our previous guidance of $1.805 billion-$1.83 billion. The current guidance still assumes $7-$9 million of revenue from ITI Data. We expect a foreign exchange gain of approximately $1 million, net interest expense of $3 million-$4 million, and full-year effective tax rate to be in the range of 22.5%-23.5%. We also anticipate increased investments in data and AI in the fourth quarter, similar to the pattern last year. Based on these assumptions, we anticipate our Adjusted EPS to be in the range of $1.61-$1.63, representing year-over-year growth of 13%-14%. This is an increase from our prior Adjusted EPS guidance of $1.59-$1.62. The ITI Data acquisition is expected to be neutral to Adjusted EPS in 2024. We expect capital expenditures to be in the range of $48 million-$52 million. In summary, we are pleased with our third-quarter results and encouraged by the acceleration of our growth momentum across all of our business segments. By continuing to invest in our data and AI-led strategy, we are confident in our ability to maintain our double-digit growth trajectory into the fourth quarter and beyond. With that, Rohit and I would now be happy to take your questions. Thank you. At this time, if you would like to ask a question, please click on the Raise Hand button, which can be found on the black bar at the bottom of your screen. When it is your turn, you will receive a message on your screen from the host allowing you to talk, and then you will hear your name called. Please accept, unmute your audio, and ask your question. As a reminder, we are allowing analysts one question and one related follow-up today. We will wait one moment to allow the queue to form. Our first question comes from Surinder Thind from Jefferies. Please unmute your line and ask your question. Thank you. I'd like to start a question about the build-out of the proprietary AI models. Can you talk about the strategy here at this point? Is the idea to build as many of these proprietary models yourself? And how does that fit in with the broader ecosystem of maybe what the hyperscalers are building in terms of their models, as well as maybe more industry-specific solutions that are also being built by third parties in the sense of the traditional software developers? So I guess, essentially, are you becoming much more of a software company at this point? Sure. Surinder, so let me try and take that. So first off, our belief is that you will have an ecosystem of large language models, agentic AI, autonomous agents, and proprietary AI models. So you're going to have a whole host of these types of capabilities that are going to be there. Each one on its own is going to be relevant and useful for either being able to undertake a task or manage a process, or ultimately using a combination of these different capabilities, being able to achieve an entire goal or an objective. At EXL, we are thinking about building out our own proprietary AI models in those areas where we have domain competency and we have access to data sets which are proprietary. What we have seen is, as we built out our first insurance LLM, that the productivity of that LLM is far greater than what's available today in the marketplace. The latency is very low, and therefore we can get real-time responsiveness, and the cost is a fraction of what would be paid for using anything that the hyperscalers or any of the other LLMs that might be available in the marketplace, so frankly, our strategy is twofold. One, to build proprietary AI models where we can create differentiation and we can create capability that cannot be developed by others in the marketplace, and two, for us to be able to stitch together the appropriate balance of large language models, agentic AI capabilities, autonomous agents, and embed our own proprietary AI models to be able to optimize the best benefit for the client in terms of productivity, latency, and cost. Does that make sense? Yes, that's helpful, and then just from perhaps a bit more of a near-term view, can you talk about the investment that's required? Is it you kind of continue with the traditional level of CapEx, R&D to kind of build this out, or do we enter a period of slightly elevated investment where there's a bit of an arms race to be the first to build some of these models? Sure. I'm happy to address that, and Maurizio can add to that as well. So in general, our viewpoint is, with the rapidly evolving technological landscape, there is going to be a need for a much greater level of investment, and at EXL, we will be significantly stepping up our investments both on OpEx and on CapEx to be able to create these capabilities and to be able to build proprietary models as well as tools that can be leveraged in our client processes. Specifically, as we've highlighted, and Maurizio can talk about this, we will be investing a significant amount in the fourth quarter this year and continue that investment going forward because that's the only way in which we can stay ahead on capability, and because the landscape is changing so rapidly, these are necessary investments that we will need to make as we go forward. Maurizio? Sure, Rohit. Sure, Rohit. Really, to add to what Rohit just said, when you really look at just kind of the overall P&L, we will be making more investments, particularly in OpEx, in our investment line, in our P&L over the next one to three years, and that's really going to be funded by us driving value from gross margin. Our gross margin needs to grow and increase in order to fund this investment. But if we do that by driving price on higher value services that we really start to provide to our clients, particularly in data and AI. So what you'll end up seeing going forward is a higher gross margin offset by a higher amount of investments going forward, and that flowing into an incremental increase in our AOPM on an annual basis. Not a significant increase, but an increase on an annual basis going forward. Thank you. That's helpful. Our next question will come from Bryan Bergin with TD Cowen. Hi, good morning. Thank you. I wanted to start with digital operations. Another strong quarter here on organic growth. Let's ask on your conviction and potential sustainability of this elevated growth relative to the medium-term outlook for the segment. And it's been good to see all industries contributing here. Do you have a higher level of conviction in the near term in sustaining higher growth in any particular digital ops industries? Sure, Bryan. So first of all, we are very pleased with the growth of our digital operations business. And I think it's been at this elevated growth rate for a number of quarters. And actually, we've seen a bit of acceleration of the growth rate. When we take a look at the demand environment for our services in digital operations, that continues to be strong. When we take a look at our pipeline, the pipeline is actually growing very nicely. And as we've shared previously, what we also see is that the number of large deals and the value of large deals in our pipeline continues to increase. So frankly, all of these are very encouraging signs about the demand environment, the pipeline, and our ability to generate revenue growth in digital operations. There are a couple of things which I would like to kind of highlight. Number one, everything that we do in terms of selling digital, pure digital revenue is included as part of our digital operations segment, so the growth and the revenue in our digital operations segment includes all the revenue that we derive from operations, but it also includes all the revenue that we derive from digital capabilities that are sold to clients on a standalone basis. Obviously, the digital capabilities are growing much more rapidly than the digital operations core piece on a standalone basis. In terms of industry segments, frankly, we are seeing broad strength across the board, and we're seeing strength in insurance. We're seeing it in healthcare and in our emerging business unit. We see this strength take place globally. So we are seeing this strength in the U.S., as well as in U.K., Europe, and in Australia and New Zealand. So there seems to be a broad secular shift in terms of moving forward on this. Going forward, our expectation is still that we should be able to grow our digital operations business in a low double-digit kind of a trajectory. And that would be something that would be sustainable for us. And that's what we would be targeting. Okay. Understood. And then just shifting to analytics, it sounds like payment integrity and data management remain solid. What about the balance of analytics? Any notable changes in just the customer marketing view and decisioning analytics? And just how are you forecasting the fourth quarter growth in analytics? Sure. So look, I think we are very, very happy that in analytics, the business growth has accelerated. We are seeing analytic services to be growing as well alongside with our payment integrity business line, as well as our data management business line. I think some of the strength that we are seeing, again, is reflected very nicely in the pipeline. And we see a lot many more deals out there. The only thing to note here is the analytics business will always be a shorter cycle business. And therefore, there will be a fairly higher level of volatility associated with this business. But the underlying fundamentals and the traction that we are seeing with our clients and the business value that they derive from our services here, that's resonating very, very nicely. Our next question. Commentary on the fourth quarter? Yes, about our view for the fourth quarter for analytics. Sorry. For the fourth quarter, look, I think we would expect the business momentum to continue. When we had guided for calendar year 2024, we had guided that our digital operations business will grow slightly faster than our analytics business this year. But we do see both our business lines getting to low double-digit growth rates going forward. And therefore, we would expect that trend to kind of maintain itself in the fourth quarter. And keep in mind, Bryan, when you look at the prior year, we were fairly flat on revenue and analytics between Q2- Q4. So as we continue this momentum going into Q4, as total revenue continues to increase, you do see an increase in the growth rate. And that's being reflected now, Q2, Q3, and you would see that in Q4. Okay. Helpful. Thanks, guys. Question will come from Maggie Nolan with William Blair. Please unmute and ask your question. Hi. Thank you. How are you assessing the risk of a change in conversion rates of larger deals for your digital ops business? Is there anything to note there? Hi, Maggie. So actually, our conversion rates and our win rates in large deals is about the same as what we see with the rest of the portfolio. Clearly, the large deals tend to be a little bit more lumpy, but it takes a fair amount of time for us to be able to ramp up and implement the large deals. So far, we seem to be in a very good place in terms of our competitiveness and our win rates associated with large deals. And I think we've got a fair amount of traction in the pipeline, and we've got a fair amount of deals that we've won, as we've kind of shared in terms of the client wins, which still need to be implemented going forward into 2025. Thank you. And can you give some commentary on your success in the data and analytics segment organically and in data management in particular, organically versus what the acquisition is adding in terms of synergy so far? Sure. So in data analytics, the organic growth for us largely has been on analytic services. And frankly, out there, we are seeing a reversion of the demand coming back and therefore growth of that business taking place. Marketing analytics for us certainly had declined, but now is less than 10% of our portfolio and therefore is not really having much of a material impact on our data analytics business. The data management part, there are a number of capabilities that we have built over the last five years that continue to grow very nicely on an organic basis. The acquisition of ITI did add to our revenue growth in the third quarter. But if we excluded the revenue that came from ITI, our growth of the analytics segment would have been between 9%-10% growth still. So actually, it still continues to accelerate and is still a pretty healthy growth rate even after excluding the acquisition of ITI. Thank you. Congrats on the results. Thank you. Our next question will come from Puneet Jain from JP Morgan. Please go ahead with your question. Hey, thanks for taking my question, and great quarter. Excuse me. I wanted to follow up on the question that Maggie asked about the data and analytics. So the overall segment, the analytics segment, grew 9%-10% on an organic basis, like you mentioned, Rohit. How much of that segment stems from services that you didn't offer two or three years ago? The data management, for example, came through Clairvoyant and ITI, AI models, and the stuff that you're building right now. So how much of that overall segment stems from services that you didn't offer three years ago? Thanks, Puneet. Look, I think the data analytics business is changing quite rapidly, and the kind of services and the kind of capabilities that we have continue to evolve. In the past, there used to be a fair amount of data visualization that used to be done, a lot of reporting that used to be done. All of that has completely changed, and we've moved much more towards AI and much more complex analytical services. The data management part for us continues to build out very nicely and rapidly. The partnership that we have with Databricks, that adds to our ability to grow this. But also the partnership that we have with each of the hyperscalers, with Google, with Amazon, with Microsoft, these are all adding to the growth rate of our data analytics business. I think the big thing is as clients gain confidence with us in this space and the buying center, which is shifting from the chief analytics officer to also include the chief information officer and the chief technology officer, I think that's helping us build and grow this business also very nicely. So frankly, there's a lot of evolution that's taking place in our decision analytics business. Hard for me to say how much of that growth is coming from new services versus old. We don't really have data that we share on that externally right now on that. That's fair. But thanks for the explanation. And then you talked about Insurance LLM earlier. So as you build some of these models, which are not tied to any specific client, what data do you use to train those models? I know you have some proprietary data, but are you allowed to use client's data to build some of these industry-specific LLMs? And follow up on that would be how much automation of existing tasks or processes can be achieved using these AI solutions? Sure, so first off, we absolutely have contractual permissioning rights by our clients to be able to use the data. And we only use that data which our clients give us express and written permission to be able to use, and the Insurance LLM has been trained on that dataset. If you take a look at it over the last 10 years, we've got a lot of claims-related data from insurance carriers, and those insurance carriers that allow us to use this data, that's the dataset that we've used to train our Insurance LLM. What this work is on auto bodily injury, workers' compensation, and medical liability, so basically, what the datasets that we have is when a claim comes in, what is the incident that took place, and what was the outcome that was finally settled as a result of that incident, and what was the claim that got paid out. Now, that dataset is really, really valuable because we can use it to train our models so that the next time a new claim comes in, we already know what is the best pathway for that claim to be settled, negotiated, and paid out, and from a client perspective, they're very willing to share that data with us because it's not personally identifiable information. It's not protected health information. So it's not PII and PHI. It is basically a derivative dataset which allows you to be able to continuously improve the accuracy of that LLM. And as we deploy these LLMs in our client operations, we are actually going to be getting more and more datasets from them. And therefore, we can continue to fine-tune and train our Insurance LLM and become really an expert in terms of the productivity and the accuracy that these LLMs can be deployed in. Now, keep in mind that it's going to be for a pretty narrow use case. And our ability is that how do we take it from that narrow use case and then we expand it into other use cases? Because we are in a privileged position where we do so much of work with carriers in a particular industry or payers in a particular industry or banks and financial institutions in the banking industry, having access to that dataset makes us a unique player to be able to develop these types of LLMs. What we are finding is our knowledge about handling the process and our domain expertise combined with our ability to access this data, that's what's creating the differentiation for us. That's what is allowing us to be able to build these proprietary models for our clients. Okay. Thank you. Our next question will come from David Koning from RW Baird. There we go. Can you guys hear me? Yes. Okay. Sorry about that. So I guess just from a high-level perspective, it seems like a lot of the industry has struggled recently, both IT, BPO, etc. Yet you guys seem to be growing significantly faster than most. Is it just functionality? Some of the stuff you're doing is better. Is it the mix of clients that you're serving and just industries you're serving? What do you think are the two or three biggest differentiators that you're doing compared to a lot of your competitors? Yes, Dave. So look, I think in our business, it ultimately boils down to two essential ingredients. One is execution, and two is capability. On execution, we've been absolutely maniacal in terms of being able to deliver to the commitments that we make to our clients. And we believe that we have one of the highest customer satisfaction scores in the industry. We just completed our annual customer satisfaction survey in the third quarter, and we've got outstanding results. That kind of acceptance and referenceability by our existing clients to our prospects and to other players in the industry, I think, stands out, and it generally results in us being able to grow our business at a faster pace. The second aspect is capability. I think we were just a little bit early in terms of investing in analytics, investing in digital, and building up market-relevant capability that can be embedded into the operations and processes of our clients. Today, when every single client is trying to embed AI into the workflow, they find our capability, our ability to execute on that capability and integrate that into to be differentiated. Therefore, our win rates are very healthy, and we've been able to build and grow our business. So these two things are very important. And I think on a go-forward basis, we will need to continue to execute well, and we will continue to invest much more to be able to stay ahead on the capability front and be a relevant partner for our clients. Gotcha. Thanks. Yeah. I mean, you guys are crushing it. And I guess my follow-up question, insurance analytics, that had been flattish, give or take on a year-over-year basis for the last four quarters or so, but it accelerated to about 15% or so growth this quarter. Is that where ITI falls? And it seems like organically, it's also doing better. But why did insurance analytics accelerate so much? So ITI, firstly, is largely focused in on banking and a little bit on healthcare. But that's where most of the revenue stream came in from. Insurance analytics has a couple of different pieces to it. It's got insurance analytics services. It did have marketing analytics in it. And the marketing analytics, as we've shared with you previously, was something that declined year over year. And that pretty much has stabilized. So that's something which is no longer a drag. There is a huge amount of work that we do around actuarial work. We do work around insurance indemnity spend. And therefore, analytics services are a critical aspect of the services and the value that we provide to our insurance clients. That seems to be resonating nicely and is growing well. And like we said, we've also invested in building up leadership capacity out here and being able to kind of continue to grow that. One other aspect is data management within insurance analytics. That's growing strongly as well. And most of the insurance carriers have got legacy platforms and systems. And therefore, data management for them becomes a critical element. And we're seeing good traction on that as well. Great. Thank you. Our next question will come from David Grossman with Stifel Europe. Please go ahead. Thank you. Rohit, perhaps you could further refine the elements of growth in the digital ops business. As you mentioned, it's been fairly strong for several quarters now. And I know you mentioned it includes some of the pure digital revenue. I'm not quite sure what exactly you're including in that bucket, but anything you can do to kind of help us better understand the growth dynamics there versus the legacy core pricing units, whatever it is that you can help us better understand that. Sure, David. So first of all, the digital revenue that we have, you can broadly break up the digital revenue into two pieces. One is digital solutions that we have created, which our clients buy from us on a standalone basis. So think about this as digital platforms or tools or solutions that our clients want to leverage across the enterprise, regardless of whether they give us their operations or not. And therefore, that's one bucket. And that bucket for us has been growing very nicely and very sharply. The second part on the digital side is work that we embed and we combine with the workflow. And therefore, it gets embedded into the digital operations work that we are doing, and it's digitizing our clients' operating workflow. So both of these, and this is much more of an integrated service that they tend to buy from us. So those are the two elements within digital. And what we are seeing is that clients, when they think about how the future of work should be managed for them, they naturally gravitate towards those players that can not only execute and deliver operations at high service delivery, low cost, and deliver exceptional value, but also those that are forward-thinking that can apply data, can apply analytics, can apply digital, and be able to deliver incremental value to them. And we've been fortunate that we've been winning a large number of deals out there, and our growth rate there has accelerated and is presently at elevated levels. So are these standalone solutions that you mentioned, are those sold as a technology tool? Is that the revenue model? Yes. That's correct. And how big can you dimension just how big these components are? David, it's difficult for me to kind of do that. We've not shared the data on that externally. But these are tools that clients will buy. Keep in mind, they'll buy the tool and pay a license fee for the tool, as well as there are services associated with the implementation of that tool that also need to be purchased by the client. So anytime we have this IP that a client is buying from us, they're paying us a software license fee for being able to use that. But they're also asking us to come in and implement this into their environment, and we charge them for that service. Right. So I mean, is that the shift impact in gross margin? Is that when you talked about the need to fund incremental investment? I mean, should we expect the gross margin and ops to continue to go up? So on these standalone solutions, as we get scale, we certainly would expect the gross margin to go up because once the solution is built, it should be able to kind of provide us with higher gross margins. But keep in mind that every single new tool that we introduce actually is a drag on gross margin because in the initial one or two years or when we don't have the size and scale, actually, it's a lower gross margin. So it really depends on the portfolio and how fast we are introducing new tools and solutions and how fast we are achieving scale in existing solutions. Got it. And sorry, just one more. It's hard not to notice the volatility in margins quarter to quarter on the EBIT line. Is that unique to this year, or are we going to see that kind of volatility going forward as well? Hey, David, it's Maurizio. You've seen a little bit of volatility this year. We started with a lower margin right around 18.7% in Q1. It increased to 20% AOPM in Q2, and now you're seeing about 19.9%. The average for the first half of the year was 19.4%, right in line with last year. Last year was 19.3%, and we do believe the second half will be right around 19.4%, so we're a little bit higher in Q3. But as Rohit talked about, we do have a number of investments that we want to make in Q4, so I think you've really got to look at this on an annual basis. We will manage AOPM so that it incrementally builds on an annual basis marginally. But the quarter to quarter will have a little bit of volatility, but I would stress less looking at the quarters and really think about the annual margin trajectory. Okay. All right, guys. Thanks very much. Our last question will come from Vincent Colicchio with Barrington Research Associates. Please go ahead. Vincent, your line is open. Feel free to unmute. Can you hear me? Yes. Rohit, can you talk about the new deals added in the quarter? Weren't even particularly large. Were they new to outsourcing? Any other relevant commentary? Sure, Vincent. So I think we are very happy with the new deals that we won this quarter. It's pretty well spread out between our digital operations business and our analytics business. In terms of the quality of the deals and the quality of the client logos that we won, it's pretty much similar to the past. It includes some Global 1000 names and Fortune 1000 names. So those are always great. And there are a few that we would expect to ramp up over the next couple of quarters. And therefore, there should be significant large deals for us. So nothing different from previous quarters. So pretty much the same kind of complexion and quality of deals that we would have won in this quarter. And are you seeing a substantial M&A pipeline? How are valuations in the market? And Maurizio, could you reiterate your capital allocation thinking? Sure. So in the M&A market, we continue to see plenty of opportunities and assets that come to market. Obviously, we're going to be very selective in terms of the strategic value to us going forward. We purchased ITI for a number of different reasons, particularly to really drive more capability in data management and also expand our client base. Going forward, we'll continue to allocate capital to both M&A and share repurchase. Share repurchase, we've allocated about $185 million this year. And we've gotten a relatively very good price on that repurchase activity, just slightly north of $30 a share. So you'll see a fairly balanced capital allocation trajectory going forward, really tailored between M&A and share repurchase. Thank you. We have no further questions at this time. I will turn the call back to John Kristoff for closing remarks. Okay. Well, I'd like to thank everyone for joining us this morning. And as always, if you have additional questions, please feel free to reach out to me directly. Thank you.