Morning, good afternoon, and welcome. I'm Rick Trainor, the CEO of Business Services within LexisNexis Risk Solutions. I've been with RELX for over 20 years, having joined through the 2004 acquisition of Seisint. I've been leading Business Services since 2009. Today, we're going to give you an update on Business Services with a particular focus on Fraud & Identity. I'll walk you through our strategy, highlighting the sophisticated data analytics capabilities. You'll then hear about our technology approach from Risk CTO, Vijay Raghavan. We'll then bring the Fraud & Identity business to life with two deep dives. Kim Sutherland, VP and Global Head of Fraud & Identity, will take us through a customer case study on how customers use our Fraud & Identity solutions. We'll then have Matthew Adams, CTO and Co-founder of IDVerse, and Daniel Aiello, Chief Product Officer and Co-founder of IDVerse, walk through a new account opening case study. Afterwards, Vijay and I will come back for Q&A. Let me start with where Risk fits within RELX. In 2025, Risk represented around 36% of RELX revenue and about 39% of the profit. Our full year revenue in 2025 was GBP 3.5 billion or about $4.6 billion. The long-term fundamentals of the risk business are strong. We have continued on a strong and consistent growth trajectory with average underlying revenue growth of 8%. While there'll always be some fluctuations across cycles, you can see a consistent 7%-9% growth over the last decade, with the exception of 2020 due to COVID. Our main business segments have an average growth rate roughly in line with the divisional average, and fluctuations tend to cancel each other out over time. We are not only a high revenue growth business, but also high margin. A key factor in this is our scale and the ability to reuse our unique data assets, our technology, and linking and AI capabilities in each of our key segments. This, coupled with a focus on continuous process innovation, helps us manage cost growth below revenue growth. The gap between underlying revenue growth and underlying adjusted operating profit growth is widening with the advancement of technology. While we operate in global industries with structural growth drivers, innovation is a key reason for our consistent performance. We continue to enhance the value we deliver to our customers through new solutions and capabilities that are deeply embedded into their workflows. In this slide, the orange part of the bar is growth that comes from new products. We define those as products launched in the last five years, which is a typical adoption cycle to roll out new products for us. A new technology is helping us develop and launch products at a faster pace. We have four key capabilities that we leverage to drive innovation and add more value to our customers. First is our deep customer understanding. We work in close partnership with our customers to help solve some of their most fundamental business challenges. Our solutions are deeply integrated into our customers' workflows, where we help inform or automate key decisions, with over 90% of our transactions being machine-to-machine. Our deep understanding of our customers' businesses, combined with our core skill and innovation, is a key driver of our success. Our second key capability is our leading datasets. This is the foundation of our business. Our data assets have been created over decades of licensing, aggregating, linking, and building data. As for scale, breadth, and depth of this data, we have tens of billions of public records and data elements across tens of thousands of sources. Most importantly, we have contributory and proprietary datasets that are unique to us and are a core part of our differentiated value proposition. We now have over 25 contributory databases across Risk. This is where our customers contribute their data to us so that we can provide them back risk analytics across the market or across industries to solve specific use cases. I'll speak to this in the context of the Business Services use cases shortly. We continue to grow the depth and breadth of our data, and we're also adding different types of data to provide greater risk insights to our customers. It's important to note that we serve customers that operate in highly regulated markets. It's incredibly important that the answers that we deliver to our customers are highly precise, accurate, explainable, and compliant. Our third capability is our advanced linking and analytics. We have a long history of using AI and other advanced analytical approaches. This underpins our linking capability, which allows us to connect these vast amounts of disparate data points to create one unique view of an individual or a business. We also utilize our sophisticated analytics and AI in the solutions we provide to our customers through scoring models, attributes, and diagnostic tools, which enables them to make decisions. Our models have been refined and improved over decades, utilizing important customer feedback loops. We continue to apply the most sophisticated approaches to ensure our products provide our customers with industry-leading quality and accuracy. Finally, our technology platforms. We have a fast, scalable platform that allows us to ingest more and more data and seamlessly plug in new AI technologies. This also allows customers to connect to our solutions seamlessly. We serve four business segments within the Risk division, where we help our customers assess and manage risk and identity fraud. Business Services is the largest segment and represents nearly 45% of Risk revenue. This is where we'll focus today. Insurance is the second-largest segment at nearly 40% of revenue. Specialized Data Services is just over 10%, Government is about 5%. Now let me walk through Business Services in more detail. This slide shows you Business Services revenue by geography, solution, and type. On the left, you'll see that we now generate nearly 30% of our revenue from outside of the U.S. Since I last presented, we've become more global, and non-U.S. expansion will continue to be an important growth opportunity and is being driven by the mix of solutions we offer and the relative maturity of each market. In the middle chart, I'm highlighting the proportion of revenue from local solutions, those built on local data assets for local markets, and our global solutions, which are those data solutions applicable worldwide. We continue to expand our portfolio of global solutions, which now represent over 45% of our revenue. We have a long runway with these solutions in both the U.S. and non-U.S. markets, and we expect this to continue to be a core part of our growth engine going forward. Finally, on the right chart, you can see we have a balanced mix of revenue from subscriptions and transactional solutions, and our transactional revenues are under long-term contracts with a volumetric component. There are very few one-time transactions. We serve a large, diverse customer base with over 18,000 customers in more than 180 countries and territories. Our solutions are developed to meet the needs of customers of every size, from the world's largest and most sophisticated businesses to small and mid-sized businesses. Our revenue concentration is quite low, with our top 30 customers making up less than 30% of our overall revenue. Our solutions are used across industries, including financial services, which is our largest customer segment, digital service providers like telcos, retail and e-commerce, and a long tail of others. We also provide solutions that other parts of the risk division take to market in the insurance, government, and healthcare sectors. We help our customers assess risks associated with a consumer, a business, or a transaction, whether that is fraud, compliance, or credit risk. This helps our customers make higher confidence decisions, makes the transaction process more efficient and safer for consumers. Our business is segmented into three primary business areas. Fraud and Identity Solutions account for a little more than a third of the revenue and is the largest part of our business. It will be the primary focus of today's discussion. What we do here is help our customers evaluate if an identity exists, can it be trusted, and whether a transaction is legitimate. We do this by analyzing hundreds of digital, physical, and behavioral attributes associated with an identity in the transaction to help our customers understand which they should allow through their systems without friction and which are higher risk, requiring additional levels of diligence, whether a particular transaction should be rejected outright. Financial crime and compliance accounts for a little under a third of our revenue and is our second-largest segment. In this segment, we deliver a suite of solutions that help our customers comply with global regulations such as know your customer, anti-money laundering, counter-terrorist financing, and anti-bribery and corruption statutes. We do this by validating that the identity exists and the identity attributes are accurate. We then screen the identity details against various watch lists, such as governmental sanction lists, economic sanctions, and politically exposed individuals. Although an identity may have been determined to exist, our customers must also demonstrate that it is legally permissible to do business with them or to treat them with a higher level of risk. Finally, the balance of revenue comes from credit, business, and other risk solutions. Here we provide a range of specialized solutions, including alternative data solutions for understanding the creditworthiness of consumers and businesses, along with due diligence tools. All of our solutions are underpinned by a combination of highly differentiated data assets and complex analytics, which I’ll talk about more in just a moment. The challenges facing our customers are large and global and only getting bigger and more complex. The number of fraud attacks and associated fraud losses are growing, driven by automated bot attacks and AI fraud schemes. There are more sanctions and regulations being imposed that must be met by an increasing number of organizations. Cross-border transactions, cryptocurrencies, and other new transaction methods make tracking money flows and compliance harder. Consumers are increasingly using non-traditional borrowing types like buy now, pay later. That, coupled with changes to traditional credit reporting, makes traditional credit files less representative risk, supporting the need for more alternative credit data solutions. With the rapid evolution of AI, bad actors are operating faster and at larger scale than ever before. There are more sophisticated deepfakes and synthetic identities and evolving fraud schemes and more systemic attacks. For our customers, this means that serving their customers and growing their business is harder, and it's increasingly difficult for them to assess risk and establish trust during a transaction flow, resulting in outsized financial and operational impacts. We are incredibly well-positioned to help our customers solve these growing challenges. We layer intelligence at every point as our customers interact with their customers, enabling our customers to see a full picture of risk associated with all aspects of a consumer interaction. Our solutions are deeply integrated into our customers' workflows, and most of our solutions are machine-to-machine, meaning within a fraction of a second, as a customer is interacting with their customers, we can assess if this is a legitimate person or an agent that they want to do business with or operating on a trusted device with identity attributes and behaviors that are consistent with recent patterns. At each stage of the process, we verify the connection between the consumer, the device, the agent, and provide intelligence around the risk that helps them make higher confidence decisions. This makes the transaction process smoother, more efficient, and safer for consumers. As a result our customers can grow confidently, onboard and protect legitimate customers without friction, and operate more efficiently and in compliance with worldwide financial regulation. The way we do this is by providing our customers with a comprehensive and multidimensional view of a consumer or a business, including attributes tied to their physical identity, their digital identity, and their behaviors. This helps identify when there are patterns during a transaction flow that appear unusual and potentially risky. Our solutions enable our customers to confidently assess whether they should trust the person, the agent, the device, and the behavior associated with whom they're transacting. This deep view of a consumer or business is what fuels our analytics engine. The scale, breadth, and depth of our data assets are truly differentiated. I'd like to draw your attention to the following stats that help demonstrate the scale of our network. We cover virtually all the adults in the United States. We process over one trillion sanctions annually. We process roughly 145 billion digital transactions annually. That includes 81 billion logins, two billion new account creations, and 28 billion payments. There are four primary sources of the data in our Risk Intelligence Network, including two foundational and two proprietary sources. The first foundational source is our public records repository. We have tens of billions of public records from tens of thousands of sources that we've built over decades. Some of the data is no longer publicly available, some is theoretically public, but extremely difficult and complicated to collect because of the format, general data source availability, or requires manual collection. The second foundational source is our licensed data, which comes from thousands of different sources to add further intelligence, breadth, and context. For these sources, the usage is commercially controlled and regulated, meaning we can only use them in certain ways in our solutions. We have our proprietary network-driven sources. First, we have our contributory data assets, which are built through customer interactions with our solutions. To benefit from the value of these solutions, customers must contribute their activity to the Risk Intelligence Network. Each time a customer transaction happens, the input data, the data attributes, the patterns of behavior, and the outcome of that transaction is captured. This means our data asset is becoming richer and deeper with every transaction. Finally, we build proprietary derived attributes, which further enhance an identity profile or its correlation to risk. All four dimensions are combined to create a longitudinal network of risk insight that grows over time. This vast data alone has little value to our customers. We transform this data utilizing sophisticated analytics and AI into specific signals and scores, and feed that into our customers' workflows to assess risk in real-time. We have created a great virtuous cycle. As we process more transactions and outcomes, we're able to see more signals about risk and how patterns of risk are evolving, which allows us to create even stronger signals of risk, which makes our product stronger and delivers more value to our customers. Today, we see over 400 million transactions every day. The number continues to grow as we add new customers, as our existing customers grow their usage, and our customers deepen their relationship with us. Our solutions deliver better outcomes for our customers and create significant differentiation in the measurable value uplift we provide. While the solutions we provide our customers represent a small part of their cost base, they have significant positive impacts on the economics over the overall business. We identify and stop more fraudulent transactions, even in the hardest to assess bands. We deliver less false positives, allowing more good customers through without friction. We make sure only the highest-risk transactions are routed to high-cost methods of review. The net of this is higher revenue and lower operating costs for our customers. Customers deepen their relationship with us over time. The left side of this slide shows an example of our relationship with a U.S. financial institution. We initially sold this customer an identity verification solution to improve their KYC program. As the customer recognized the value we provided, they adopted more solutions, adding new capabilities across more use cases, such as account management and fraud prevention. While every customer is different, the shape of the journey is very similar across most of our customer base. We price to capture a small portion of the value we provide. As customers see strong price to value of our solutions, they increase the number of products they purchase and the depth of our integration into their workflow. As we continue to innovate, we expect that all customers will continue to layer in more capabilities and expand their relationship with us in this way. This slide highlights our strong track record of growth over the past 25 years. We have expanded primarily through organic innovation, supplemented by targeted and highly complementary acquisitions. We have a very disciplined approach to M&A, evaluating hundreds of new technologies, new solutions every year. Many of our successful acquisitions, like ThreatMetrix and Emailage, started as commercial partnerships, which gave us a deep understanding of who has leading capability and what the combined value proposition is for customers. IDVerse is our latest acquisition, completed in February of 2025, which added AI-powered document authentication and deepfake analytics to our portfolio. Before we acquired IDVerse, we assessed nearly every provider of scale in the space, either through partnership or other commercial discussions, comparing technologies, comparing analytics, and testing their ability to catch fraud. This gave us confidence that we're acquiring the most sophisticated capability in the market. You'll see Matt and Dan demo this in just a little bit. Our customers' challenges are far from static, and our evolving solutions, robust data network, and ongoing innovation keep us well-positioned for future success. Now let me turn it over to Vijay to take you deeper into our analytics and technology approach. Thank you, Rick. I'm Vijay Raghavan. I'm the Chief Technology Officer at Risk. I've been in this role for almost 15 years, and I've been at the RELX for almost 25 years. I'm also the chair for the RELX Technology Forum, which shares best practices across the RELX divisions. Rick briefly touched on our core capabilities, and I'd like to walk you through our analytics and technology approach in more detail. Let's start with the function of technology at RELX. At a fundamental level, we are the enablers of the innovation engine you have heard so much about today. We help our businesses execute against our growth plans by investing in the right technology capabilities to enable our teams to innovate quickly and efficiently with the right tools and to ensure that our systems are flexible, reliable, and scalable. Given the nature of our business, it is the role of technology to make sure that we have highly secure environments that protect our customers' data and our IP and to adapt to changing regulatory requirements. An integral part of technology's role is to continuously automate and optimize through the improvement of our processes and our tools. Technology is a real source of competitive advantage across RELX. At the heart of that is our people and the ability to stay at the forefront of the evolving technology landscape. These are highly innovative teams with deep experience and expertise in data analytics and AI and ML techniques and who are motivated to use technology to improve outcomes for our customers and for ourselves. We have a long history of using advanced technology within Risk. We first created our big data technology in the 1990s, long before big data was a buzzword. We created a proprietary machine learning-based linking technology in the mid-2000s. We first started talking to you about big data and the usage of analytical algorithms back in 2011. By around 2015, we had been using AI and ML techniques for over a decade. That's when we first started sharing externally about how we have woven AI and ML tools and processes into the fabric of our data and our technology. In 2018, we talked to you about how we use supervised and unsupervised learning in our AI solutions and how we assessed and evaluated multiple algorithms to provide the greatest value to our customers. In 2023, I spoke about how we were integrating real-time machine-generated data into our existing fabric of public records data, contributed databases, device intelligence, and digital identities to give our customers even more comprehensive solutions. I also spoke to you then about generative AI and how it would give us greater scale to innovate, for example, around knowledge extraction from our data repositories and automated code generation. All of these presentations remain available on our website. What we are doing today with our technology is consistent with our history. We are constantly evaluating new tools to evolve our approach to support better, faster, cheaper innovation, and we use the best and most appropriate tools for the job at hand to create even more compelling products. The way this has evolved since the last time I spoke to you in 2023 is that we are embedding generative AI and agentic AI tooling into our technology stack. Since some of these AI tools come with side effects, we have built a trusted AI infrastructure around these tools in order to not compromise the quality and integrity of the solutions. It is paramount that we continue to offer our customers the trusted, reliable, and compliant solutions they've come to expect from Risk even as we adopt new AI techniques. Here's what I mean by that. This slide describes the layers of our technology stack. Across these layers, we use a variety of technologies, including open source, third-party, and proprietary solutions. At the bottom of the stack is our infrastructure layer, where we use third-party cloud tooling such as servers, networks, storage, databases, and other infrastructure as a metered utility. These tools are broadly available in the market and are not unique to us. What is important is how we deploy these tools, which we do in a cost-effective and flexible manner. The more important things in the stack are in the middle and top layers. In the middle is our abstraction layer, which is a proprietary approach that gives us much greater control and flexibility over how we use third-party services. We can now easily leverage emerging third-party innovations, including generative AI, LLMs, and agentic solutions offered by cloud vendors and hyperscalers, and switch between these third-party services easily. For example, we can integrate a new LLM into our platform within a few hours. What's just as important is that this abstraction layer helps ensure that even as we adopt new AI techniques, we don't make trade-offs between the speed and quality of our answers or between the quality and consistency of our answers or between the speed and transparency of our answers. That is critical for our customers. Resting above the abstraction layer is our applications and product layer that represents our core IP, where we ingest data at high speeds, link the data with great accuracy, boil the data down to discrete elements that we call entities, and then build solutions that are easily consumable by our customers. We have been doing this for a long time, but we are continually finding ways to make these approaches better. On this slide, I'd like to drill down just a little deeper into the abstraction layer and the applications and product layer. Let's start with the abstraction layer. A key element of this layer is our trusted AI infrastructure that you see depicted on the bottom right. This is crucial because as we deploy new AI solutions, including agentic AI and generative AI, we can assure our customers and regulators that the decisions made by solutions are transparent and defensible. That's what I mean by not having to make trade-offs. Our trusted AI infrastructure within our abstraction layer provides us with AI validation guardrails that are superior to pure-play LLM-based solutions that claim to be nearly as good but are either ridden with bias, which is unacceptable to customers, or they are opaque and non-deterministic, which is unacceptable to regulators. The top of the slide shows two examples of our applications and product layer. Omega AI is a modernized data fabrication process. This replaces our previous generation of technology with one that is cloud-native and AI-enabled. One of the big advantages of a new approach is that we can ingest data incrementally with near real-time propagation of data into our products, which significantly improves the value we provide to our customers. The entity database is the other example. It serves as another force multiplier for us because it is a canonical entity-centric representation of data that gives all our products a shared model of entities and relationships. Entities could be people or businesses or vehicles or driver's licenses, and so on. Essentially, we are using AI to create consistent, reusable, and well-connected entities across our platform for us to not only be able to build our products more easily, but also to create an ontology of digital entities that adds much greater value to our customers. Let me touch on how we build our products with this technology stack I just described. You have seen this slide before, it is an incredibly important one. It demonstrates how we get from data to specific actionable insights that help our customers make decisions. Rick touched on the scale and breadth of our data assets, big data itself is not of much value to our customers. Our technology transforms big data into small, actionable intelligence at scale and at high speed to add value to our customers' decisions, for example, in the form of identity authentication or device authentication or agent authentication. To give you a sense of scale and speed, our ThreatMetrix product verifies 200 different data points for each transaction it sees within sub-seconds, and it does this across 400 million transactions a day. We do this by layering advanced analytics on top of our data and to cluster, link, and identify patterns to improve our solutions. This is what we call extractive AI, and it is at the heart of Risk's competitive advantage. As Rick said earlier, over 90% of our transactions in Risk are machine-to-machine in the form of scores or attributes, as opposed to generated text that a customer needs to analyze or interpret. It is incredibly important that the answers that we deliver to our customers are highly accurate and compliant, and that they are consistent, meaning a customer always receives the same answer in the same situation with the same inputs. That is quite difficult in the probabilistic way LLMs operate, where answers may evolve over time. A reliable deterministic approach is critical because of how we integrate with customers and also because of the regulatory nature of our customers' use cases. We've honed our proprietary algorithms over decades to continually improve these parameters and to create more and more sophisticated techniques, which continue to enable industry-leading accuracy with fast cycle times and at lower costs. Due to the nature of the Risk business, extractive AI is fundamental to our solutions. However, we also deploy generative, agentic, and other AI capabilities. We layer them on top of our extractive AI approach. You'll see a few examples here on this slide. The first example is related to image analytics and insurance. In 2023, we talked about a Flyreel product, which is an AI-based image capture solution that allows a layperson to capture video of their property in a home insurance underwriting context or a video of the automobile after an accident in a claims context. We have continued to find ways to improve the way videos are automatically analyzed in the background using increasingly advanced algorithms to process those images, assess context and risk, and extract relevant data into our platforms. The next example is LexID, which is a proprietary approach to link our data assets together in a highly accurate manner. Our linking underpins nearly all of our solutions, and we have continued to refine our algorithms to improve our linking over decades. Today, we have industry-leading linking accuracy. We continue to find ways to improve that linking to get even closer to perfection because every incremental bit of improvement in accuracy improves value for our customers. We are now using generative AI and agentic AI techniques coupled with a human in the loop to map raw unstructured data into structured data even more accurately and to further improve our linking accuracy. The third example on the slide has to do with the IDVerse solution, which you will see in a demo a little later in the presentation. Fraudsters are getting more creative every day with deepfakes. Our proprietary neural network within the IDVerse platform is being constantly enhanced to detect new fraud patterns. For example, two years ago, it would have been sufficient to rely on facial landmarks, eye movement, and lip sync to detect fraud. Fraudsters are now able to get past that. Now we've enhanced our neural network to detect liveness by using skin spectral analysis and optical flow analysis, which tracks involuntary movement of facial muscles due to blood flow. Our neural network handles document authentication, biometric face matching, liveness checking, depth-based 3D analysis, injection attack detection, and deepfake classification all in a single pipeline, which makes it incredibly sophisticated in identifying fraud. Again, you'll hear more about IDVerse a little later. These are just some examples of how we are continuing to leverage more and more sophisticated technology to improve the value we deliver to customers, and there are many more. We are also using our technology capabilities internally to enable us to improve processes and make our people more effective in their day-to-day work. You will see a few examples in this slide. I won't walk through all of these, but I will touch on a couple of examples. In our technology function, we are actively employing AI-assisted coding, which is clearly helpful for product development. Especially interesting is the value that it adds to the upgrading of systems, implementing new technology, and advancing our cybersecurity defenses. We do so aggressively but judiciously in keeping with our approach of including a human in the loop. There are many more examples across all of our functional areas, some of which you see here. We continue to find ways to apply technology internally to operate with more agility and more effectively, which in turn allows us to innovate faster and serve our customers better. To wrap up, I hope you walk away with a better understanding of how we use technology at Risk. Our technology and analytics approaches play a central role in enabling rapid, agile, and low-cost innovation across the business. We have used AI for decades and continue to deploy the most advanced methods to constantly refine the accuracy and value of our products and enhance the effectiveness and efficiency of our internal teams. As technology continues to get more and more advanced, we are well-positioned to adopt these tools quickly, deploy them in the most appropriate manner, and strengthen our position over the long term. With that, I'll turn it over to Kim Sutherland to bring our technology to life with a customer case study. Thank you, Vijay. My name is Kim Sutherland, and I'm the Global Head of Fraud and Identity. I've been with Risk for 20 years, and during most of that time, I've been focused on building our commercial market strategy for our portfolio of fraud and identity solutions. The way that consumers interact with businesses is evolving and increasing in complexity. During a single interaction, a consumer may log into an account on their phone, move from a mobile app to a website, issue a real-time payment, and initiate an account-to-account transfer. We're seeing a growing number of interactions through more devices and channels from mobile browser and digital wallets, and now to the emergence of agentic commerce. This growth means more opportunity for fraud. Recognizing trusted behavior and detecting anomalies in real-time across every device and every channel is no longer optional. It's a baseline expectation. Vulnerability to fraud attacks persist across the entire consumer life cycle. We help customers reduce fraud by layering defenses at each of those touchpoints. The first layer, digital and identity assessment, uses device, location, behavioral, bot, and agent intelligence to establish trust from the very first signal. In addition to basics like identity attributes, such as email address, name, and phone number, they're also verifying. The second layer applies decision analytics, adaptive fraud analytic models, machine learning, and orchestration to identify anomalies and velocity patterns in real time. The third layer adds authentication from passive methods to bind a trusted device to active methods, including biometrics and document authentication. The fourth layer, investigation and review, closes the loop with forensics, case management, and even the incorporation of fraud feedback. We leverage an integrated platform for dynamic and coordinated use of these solutions. Underpinning the layers is our Risk Intelligence Network, ensuring that every signal adds the required context to assess risk for every interaction. How these capabilities are deployed is determined by the customer. This enables a fast, frictionless experience for the vast majority of consumers and transactions that are low risk while providing strong protection when there are signals of fraud. A consumer transaction can seem very simple, but behind that moment, thousands of data signals are being collected and analyzed. Fully automated risk decisions are running in real time, and fraud risk models are scoring the interaction. This is done in approximately 85 milliseconds and over 400 million times a day. At the core of that decision engine are three fundamental questions. First, who is this? Does this identity, device, behavior, and combination of signals have any history within our network? Identity recognition is the foundation of trust. Second, can they be trusted? Are the attributes accurate? Are there any suspicious activities associated with this behavior, this device, or this identity? Critically, is this person a victim themselves, potentially being manipulated without even knowing it? Third, do we need more proof? Ambiguous signals require further verification. Our approach turns disconnected signals into a single connected digital identity. Every digital identity creates data in our network, an email address, a device, and how you interact with it, a phone number, a billing address, a payment card, a location. In isolation, each of these signals tells a partial story. Connected together, they can reveal something far more powerful, a trusted identity. A typical user has one to two email addresses, two to four devices, and two to four payment cards. When something shifts, a new device, an unfamiliar location, automated filling of identity attributes, or behavioral signals that suggest the user is being coerced, which is a hallmark of sophisticated fraud. Our network recognizes those moments. Let's walk through an example of a consumer logging into their account. What I'm showing here is an example of how our customers protect digital logins while keeping the experience seamless for trusted users. On the left is the consumer experience, a familiar login screen. On the right is ThreatMetrix and BehavioSec working together in real time. As soon as a user lands on the page, we begin building risk context using device, network, and hardware intelligence. Now I complete the login, and you'll see the outcome is a pass. In near real time, tens of milliseconds, all of these signals are evaluated behind the scenes with no impact to the user experience. What's important for our customers isn't just the decision, it's understanding why the decision was made, and that's where reason codes come in. Reason codes provide transparent, explainable insight into what contributed to trusting this user. In this case, we're seeing multiple positive signals come together. This is a recognized user logging in from a known device. There's an established historical behavior over time, not a one-off interaction, and this identity is trusted across the digital network. Together, these signals create high confidence that this is a legitimate low-risk user. Let's look at a different example. Here is a consumer registering an account with one of our customers. This is the customer's first time seeing this consumer, and the only data they have is an email address and a device. On its own, that's not enough to confidently assess risk. With our network, we layer in significantly more intelligence. Through our solution, that same email is seen transacting successfully elsewhere, linked to known devices, established payment behavior, and consistent with digital and behavioral patterns across other customers. Now, we're looking at the same flow, but with a bad actor. Here, a fraudster is reusing stolen credentials at scale. On the surface, these look like separate transactions, different emails, different devices, all appearing unrelated. Our network and sophisticated linking resolves these events into a single identity, not by depending on the device, but by linking across emails, locations, and behavioral patterns across our network. Even as the fraudster changes devices or spoofs credentials, we're still able to recognize that this is the same identity. These devices and emails are no longer isolated events, but a singular view into a customer's digital journey. Now let's apply this in a case study. In this instance, one of our banking customers utilizing our layered fraud solutions noticed a bad actor trying to use stolen credentials to access an account. This same device made multiple attempts to log into the account using different stolen credentials in a short period. In real time, we connected the device, the behavior, and network intelligence, and flagged the behavior as suspicious and inconsistent with a genuine user, and we immediately stopped the fraud attempts, and our customer avoided any associated losses. The scale and visibility of our network enabled us to link that one incident to multiple connected devices and prior fraud activity instantly. From a single suspicious device, we identified 26 additional high-risk devices and blocked 18 more fraud events across 18 different organizations. The result, we were able to stop a coordinated fraud ring that was moving across institutions and channels, and additional customers were able to quickly prevent losses. No single institution could see the full picture on its own. However, one incident prevented fraud across our entire network. This example demonstrates how the scale and global reach of our network delivers significant measurable value to our customers. I will now turn it over to Matt and Dan to walk through how we create a safer new account opening journey. Thank you, Kim. My name is Matthew Adams. I'm the Chief Technology Officer and Co-founder, IDVerse. I'm Daniel Aiello, chief product officer and co-founder, IDVerse. We founded IDVerse in 2016 and have been with Risk since the acquisition last year. Together, Matt and I lead product and technology for the IDVerse product suite, including the platforms and identity verification capabilities our customers use globally. New account opening has always been one of the most demanding trust decisions in financial services. Institutions make a binding decision about a new customer with very limited history at the moment of decision. Bad actors only need to succeed once. There is constant commercial pressure to approve quickly because digital growth depends on it. When something is flagged, the fallback is manual review, which is expensive, slow, and inaccurate and creates significant consumer friction. Every safeguard introduced to mitigate risk from CAPTCHA to SMS, one-time passwords, document data checks, Q&A, phone calls, adds friction to the customer experience and are often insufficient. Financial institutions have long balanced three competing imperatives: growth, friction, and protection. That balance has been fundamentally disrupted by AI. Our own Risk Intelligence Network sees this in the data. In 2025, synthetic identity fraud attacks tripled within the 12-month period. Fraudsters are producing complete synthetic identities, fabricated documents, fake faces, and deepfake videos. Breached personal data supplies abundant raw material. Credentials trade on underground marketplaces for as little as GBP 10. Newer network-generated fake IDs for around GBP 15. Generic AI tools and frontier models are not designed to detect these threats. What you're about to see is a recorded demonstration showing how a fraudster or an agent generates a synthetic identity document using a generative AI model. These are fraudulent models hosted on underground sites. Sites like this are real and persistent. As the demonstration begins, the fraudster selects a country and state, and in some cases, with a physical or digital mobile driver license. Genuine stolen or leaked data can be purchased and injected directly. A synthetic face is generated or a real one substituted. The output in seconds at almost no cost, a very convincing identity document image sufficient to open a new account at companies without the right safeguards. The attack method has also evolved. We are now seeing AI agents deployed, prompt-injected to act as adversarial networks, attacking the bank's defenses autonomously and at scale. We can now see the fraudster prompt an LLM to target multiple banks and open accounts, running the IDV process with the synthetic data generated earlier. It is a guardrail demonstration that simulates a real coordinated attack. As you can see, the agentic AI replicates a human's interaction, submitting the ID image to defeat template-based checks and presenting a face image or video to spoof liveness. The volume and sophistication of attacks are clearly increasing. To combat this risk, we offer IDVerse, integrated with the broader Risk Solutions fraud defense platform. Here we see a customer visiting the website of a bank to apply for a credit card. They choose their preferred card and proceed with their application. The first step the bank requires is identity verification. By using IDVerse embedded within its website, the bank can verify the applicant's identity while also capturing trusted data to pre-fill the application, reducing friction for the customer. The applicant taps Start verification, which seamlessly opens the IDVerse identity verification flow. They view and accept the privacy consent. They are shown instructions and move to capturing their identity document. We are also able to support a growing list of digital IDs. They confirm the extracted details and present their face for the biometric checks. Within seconds, the user is verified and returned to the bank site to complete their credit card application. What the customer experiences as simple is anything but. Behind that short process, multiple layers of proprietary technology operate simultaneously, orchestrated by the neural network, combining physical identity data from the document, biometric intelligence from the face, and digital identity intelligence from the broader LexisNexis network. Across these layers sits IDVerse's purpose-built neural network, engineered specifically for identity and fraud. This is not a general-purpose LLM or third-party AI model. It has been trained for over seven years on real-world fraud attempts, not available in public data sets, and is updated continuously as new threats develop. Let me explain the three primary layers of our technology. Layer one is document authentication. The physical or mobile digital ID is analyzed using our purpose-built AI, designed to detect subtle fraud patterns, document inconsistencies, and a wide range of attack vectors and methods seen across our network. It performs up to 300 automated checks. Some of these including pixel-level analysis, color consistency, lighting angles, micro security features, font integrity, and screen detection, along with file-level metadata analysis. It recognizes virtually all government-issued IDs across more than 200 countries and territories and more than 140 languages. The outcome, a real, trustworthy, and present ID document with a real identity on it. Layer two is biometric liveness and face match. The face presented by the applicant is analyzed using purpose-built proprietary liveness and presentation attack detection. The system detects synthetic injection attacks, including deepfakes, two-dimensional and three-dimensional masks, screen replays, and AI-generated face swaps, all server-side, without requiring additional steps from the user. Once confirmed live, the face is matched against the document using our own face matching engine, engineered for real-world variation and differing document standards. The outcome of Layer two, a live present person confirmed and matched to their document. Layered beneath that document and biometric check is layer three, which is a context layer, assessing the device, the network, the behavior, and how they compare against everything the network has previously seen. Risk Intelligence Network was covered in the previous case study. What matters here is what it brings to the decision. Every applicant is assessed against signals drawn from more than 300 million daily transactions contributed by institutions across the network, so every customer benefits from what every other customer has seen. Document, biometric, and digital identity. Three layers, each with deep capabilities beneath them, and together, they give our customers the confidence to open good accounts safely. The impact is measurable. Modern AI-enabled fraud, including deepfakes, synthetic identities, and coordinated attacks, are stopped before accounts are opened. Legitimate customers complete onboarding in seconds. Manual review volumes fall, and the bank can scale digital growth safely. Demand for these capabilities has accelerated across our global customer base since the IDVerse acquisition. As AI-enabled attacks scale, institutions are moving to layer defense because their existing tools cannot keep pace. That demand reflects a clear market reality. AI-enabled fraud cannot be met with static general-purpose tools. It requires specialist capability, deep data, expert human judgment, and scale that compounds. That is what LexisNexis Risk Solutions provides and what this market is increasingly reaching for. Now, let me hand it back to Rick. Thank you, Matt and Dan. In summary, we have leading positions in attractive growth sectors. AI is accelerating the volume and the complexity of fraud, which is increasing customer demand for our solutions. We are well-positioned to help our customers address these challenges by providing them a better, more holistic view of risk through every interaction they have with their customers, delivering a measurable value uplift. Our objective is to continue to deliver strong underlying revenue growth in the high single digits for a long time to come, a decade or more, driven by organic product innovation, supported by targeted acquisitions. We're well-positioned to continue to adopt new technology to add greater value to our customers, accelerate the pace of innovation, and operate more efficiently with underlying profit growth exceeding underlying revenue growth. We'll now be happy to take your questions. Thank you. We will now begin the question and answer session. To ask a question, you may press star then one on your touchtone phone. If you're using a speakerphone, please pick up your handset before pressing the keys. If at any time your question has been addressed and you would like to withdraw your question, please press star then two. At this time, we will pause momentarily to assemble our roster. The first question today comes from Nick Dempsey with Barclays. Please go ahead. Yeah, good evening, or good morning if you're in the U.S. I have three questions. The first one, have your customers so far asked you to work together with some of the big AI modeling companies so that you combine your data with other big processes using AI that are running through the institutions that are your customers? How do you respond to those requests if you had them? Second question, can a agentic AI be trained specifically to beat your network and effectively stay ahead of you in terms of fraudulent activity, find the way through all of the sophistication you've been presenting to us? Third question, there are 12,000 technologists across RELX. I know that's across the whole business, but I guess that's pretty weighted to risk. Do AI tools present an opportunity to make some headcount savings here over time? Yeah, just give me a second just so I get all three of these now. Yeah. I think the 1st question was, have we been working with our customers with the likes of some of the big AI companies? We're working with our customers to help identify how they wanna deploy, how they wanna begin accessing our systems, accessing our risk signals and intelligence into their AI models. We're not there yet in terms of customers actually integrating yet into our systems, but certainly the discussions are being had around how do we get access to those signals to inform what our, you know, financial services customers are doing to help them better stop fraud on their side. From the financial crime perspective, the alert remediation space, certainly they're interacting with us already, pulling our signals into their agentic processes, for false positive remediation at level one or level two. Second question, can agentic AI be trained to find a way to break through? Vijay, can you help me address that? Yeah, certainly. The way I would answer that question is when we talk about the solutions we provide our customers- there's this very delicate balance between a term that we use, two terms, precision and recall. Our customers use the same term, but the concept is the same. Precision is a measure of whether we're giving accurate answers without giving spurious answers, right? When we talk about false positives, that's what we mean. Agentic AI sitting on top of an LLM does not do that very well. It can be used to augment what we do, or we also use it to augment what we do, but using agentic AI in and of itself might cause a problem where, in fact, customers will try to build solutions themselves without our data or without our trustworthy AI. They might have a precision problem where they generate lots of false positives. For example, in the Financial Crime and Compliance space, that poses a cost problem or an expense problem to our customers. What do they do? They try to cast a smaller net, whether it's using agentic AI or using some other LLM, and they try to improve the precision. That causes the opposite problem. It causes a recall problem. The short answer to your question is agentic AI in and of itself is not gonna compromise the quality of our solutions. You need the breadth of the data assets that we have, along with the domain expertise that we have, along with AI tools that we have. All that put together is what renders the value that we offer to our customers. Vijay- Rick, I can take the third part as well about the 12,000 technologists. We are absolutely seeing value in AI-assisted coding tools, right? We are actively experimenting with these. Last year, we saw value, but the value that was generated by these tools was also compromised to some extent because of the technical debt that was creating, meaning it wasn't adhering to our standards. Because of the evolution of concepts like Spectra development, we are seeing improved value where not only is AI-assisted coding helping us, but it's also using our tools and our technology stack. There is promise, but I will say that while we expect to see some margin improvement over time as a function of better utilization and productivity of technologies, I do think that some of the productivity will be used to bolster our products, improve the quality and security of our products. It's a mix and match. Improve our productivity with the gains and also see some margin improvement. Thank you. The next question comes from Henry Hayden with Rothschild & Co Redburn. Please go ahead. Yeah. Hi, everyone. Thanks for taking our questions. We had three on our end. The first one was on international expansion. You mentioned that you have 45% of your revenue is tied to globally applicable solutions. So far, if you look at the divisional level over the past, let's say, five years, there's been fairly limited mix shift in terms of geographic exposure. We were curious as to how that 45% has evolved over time and how you're thinking about the algorithm going forward from here. Second question we had was around the moats, around the data that feeds kind of some of your solutions. Fairly comfortable with the moats around data and fraud and ID, but more curious as to that in financial crime and compliance as well as business and credit risk. What level of propriety, kind of surround that data, and what prevents a competitor from potentially aggregating it? The third question I had was on customer captivity. Given you're primarily indexed to financial institutions, I appreciate there's sort of this degree of captivity around answers need to be accurate. Does that same sense of, let's say, competitive advantage read across to other customer segments as you look to expand there? Thanks. Okay, great. I'll take those. International expansion. Two points there. Our revenue mix right now is 70/30, and our global products, those that are, you know, unbounded by local data assets is 45%, you know, roughly half and half. The mix has improved on the revenue side, and that's where our international businesses are growing slightly faster than our kind of than our divisional average and slightly faster than the U.S. The U.S. is quite strong as well. We're seeing both of those markets grow, and that's why that expansion from when we last spoke, you know, has improved, but maybe not as dramatically as you may have thought. Yeah, continue to see strong movement between, you know, taking our global products around the world and getting expansion there. Again, the U.S. is quite a strong market for us as well, so we see strong growth there. In terms of moat, I think the question was around what is the moat? You know, you understood the moat relative to our fraud and identity solutions, but let's maybe back up a minute. You know, the data that we use in fraud and identity is the same data that we use across our financial crime and compliance suite, as well as our credit risk. That, you know, highly proprietary nature of our public records, our license content, our network data, and the analytics and risk insights that we build off of that all goes into our data repository, and that data is used in financial crimes for, you know your customer and account onboarding. A lot of those same insights and differentiation and distinction is applicable to financial crime as well as credit risk. The credit risk data assets, it's all about ability, willingness, ability and willingness to pay, and we use the same data asset and build those insights to drive it into the credit risk space. The moat is equally across all three of those sectors. Finally, I think the last question was around,is the, what was the question? Relative customer captivity from financial institutions versus, you know, other customer segments. Yeah. I mean, our solutions are the same across customer segments. You know, the reliability, the accuracy that we build into financial services are also built into those other sectors as well, if that's where that question was going. Yeah, that's very clear. Thank you. Yeah. Thanks. The next question comes from Jo Barnet-Lamb with UBS. You may go ahead. Excellent. Thank you very much. You referenced that you have 25+ contributory and proprietary databases. I think that was in reference to risk overarchingly rather than business services. Is that correct? If so, how many do you have in business services? I'm sure this remains a very small proportion of your data sets, but could conceivably drive a significant proportion of the value you create. It sounds like it's the combination of many data sources that multiplies the value creation. Is there any way you can frame the influence on outcomes that your proprietary databases have? What proportion of outcomes are touched by proprietary data in some form? Thank you. Okay. Yeah. You're right. The overall risk contributory databases is 25 or so. Business Services has 10, and it's an area that we've been really focused on the past 10, 15 years, driving more and more contributory sources, whether it's network activity, whether it's outcome data supplied back by our customer. It is a significant source of our data. In fact, you know, on a daily basis, you know, we're getting more data signals from that data than sort of our licensing and direct sourcing public records data. It is a considerable value add. In terms of specific proportions, I can't, I don't have that number offhand. It's not something we track. But it is a significant value contributor and significantly differentiated. Was there another part to that question? Thank you. Well, not really. I mean, I think you've given me what you can give me. I mean, you did reference that you're getting more data signals than from your licensing and direct sourcing. Could you explain a little bit more of what that actually means? Yes. When you think about what's happening every day with our network activity, we're seeing over 400 million signals a day coming through. There are multiple elements to that signal. That contributes to our data repository each and every day, and that continues to grow and grow. Actually, one of our, in the past three weeks, we had some peak days over 500 million. The signals that we get from those contributory, that's just one of these contributory sources, is significant and continuing to differentiate and add value across the portfolio. That is helpful. Thank you very much. Next question comes from Christophe Cherblanc with Bernstein. You may go ahead. Yes. Good evening. Thanks for taking my question. I had one question about customer value proposition. Revenues have been growing high single digits. You were mentioning digital interaction going at, I think, 13% per annum. Attacks going up 15%, so I think that's giving us a sense of the improvement in the value proposition. If the intensity of attacks is going up, as you were stressing, do you see room to extract a bit more value from what you bring to your customers? Should we expect acceleration reflecting that increased risk exposure for your clients? Yeah. Yeah. I mean, we have a, you know, our portfolios, you know, roughly GBP 2 billion annually, we, you know, expect to see upper single-digit revenue growth rates with profit exceeding the growth rate in terms of sort of acceleration or volumes there. Certainly, we are seeing more volumes across the digital portfolio, you know, with fraud attacks escalating, with AI-driven deepfakes and things like that, it all gets blended into the overall mix. We, you know, our sort of our guidance on revenue growth remains in that upper single digits overall as a portfolio. Would you say it's fair to assume that the customer value proposition is accelerating versus what you had, two, three years ago? Oh, yeah. Absolutely. You know, we continue to add more and more capability to the portfolio, continue to expand the value that in the case of ThreatMetrix in our digital solutions bring, as well as all of our other solutions bring to the marketplace. We continue to see strong growth across the portfolio. Okay. Just one last one related to that. You mentioned that you were low share of your cost base. How low? Is it way below 1%? Based on the client base and the numbers you were mentioning, it seems to be pretty low numbers on an absolute value. I'm, I apologize. I missed the first part of that last question. Well, you mentioned that your products were a low share of the cost base of your clients, so I was just trying to get a sense of how low the share was. Was it below 1% of the client cost base? Is it 0.1%, 0.5%? Um- Just an order of magnitude would be helpful. I don't have a specific on that, but it is a very, you know, it's a low percentage, single-digit percentage, I would, if I was to make an estimate. Thank you. The next question comes from Steve Liechti with Deutsche Numis. Please go ahead. Yeah. Hi there. Thanks. I've got three as well. Just going back to one of the previous questions, actually, the sort of consistent growth at 7%-9%, which is a great growth rate, but given as you've kind of alluded to in your previous Q&A, the market and attack growth are growing higher than that, you're innovating very strongly. I'm just trying to figure out why 7%-9% is the right number going forward from here. That's the first question. Second question, I don't think you gave a customer retention number or percentage. Can you give us anything that you can on that for business services or broader? The third question is on competition. Apologies, can you just educate me in terms of who you see your key competitors as being and whether there's been any kind of new innovators in the market that you've lost any share if you have done, too, that you might highlight? Thanks. Yeah, sure. Yes. Back to the revenue growth. You know, we You know, our, you know, we expect to see seven, you know, upper single digits revenue growth, you know, for the foreseeable future. You know, our portfolio is complicated. It is a GBP 2 billion portfolio. We do have sectors that are growing greater than the average, but offset by some sectors, some solution sets within the portfolio that are lower growth. On average, it balances out to that upper single digit range. And that's where we feel comfortable with. In terms of customer retention, no, we don't we didn't share that, but it is low or I guess the inverse of that. What is the attrition level? It is low single digit range. You know, our customers stay with us for a long time. As you saw in one of the slides, where we show the growth with a customer over time, you know, we continue to see, you know, we land their, land an account maybe with one solution. It could be an FC&C screening, that quickly moves over to fraud and identity and other solutions. We continue to grow our customers over time. Do, we do serve a lot or, you know, you know, the largest financial institutions in the world, to medium and smaller size accounts as well, where they may be purchasing fewer solutions. We tend to see, if we're seeing attrition, it tends to be in those very small accounts. Second part of the question was, or the third part, You know, competition for us is, it's generally by geography and use case. So in the U.S., physical identity, you know, there's quite a few players in the physical identity space and then globally and digital as well. Digital, much less so. It's by, you know, FC&C, credit risk, fraud and identity. It all depends by geo and by use case. Really, you know, the list of competitors is long. I'd rather not get into that level of detail. Is there anyone who is a major part of the market that's at the scale that you are? Apologies again, this is my ignorance. Oh, sure. Or is the competition more fragmented? When I look at the portfolio from what we do, from F&I to financial crime to credit risk and business risk, that bring the types of solutions that we bring to market, there are very few. There are very few that have that, you know, holistic suite that we have. You'll see some on the, you know, certainly on the credit risk. The credit bureaus are competitors, we don't tend to play in their traditional credit space. We're doing alternative credit, you know, basically providing underwriting solutions for those that are not on the credit files. They're partners of ours as well. It's, you know, complementary, a little bit of competitive on the credit risk side. On fraud and identity, you know, from a digital perspective, physical perspective, you know, certainly no one has the depth and breadth of assets that we do. You know, there are players. I mean, bureaus have some components, mostly on the physical side. On the digital side, less so. Around the world it's, it, you know, it just really differs by country. Certainly there are, you know, a number of startups that are out there. We, you know, we see them, you know, a lot of them are positioning as AI-driven, AI delivery engines and things like that. What they don't have is they don't have the depth of the insights that we have from all the risk signals that our solutions provide. We tend to see them competing on the fringe, and not in the main. Great. Thank you. Again, if you have a question, please press star then one. The next question comes from Ciaran Donnelly with Citi. Please go ahead. Thanks, Rick and Vijay. Couple of questions remaining for myself. Firstly, on M&A, it's been an active piece of the strategy historically. I'd be interested to get your thoughts. Do you think the need for M&A to add capabilities given the current pace of technological innovation relative to history has increased? You can point to any areas specifically that might be an area of interest. Secondly, one of your peers talked about developing their own proprietary model, but in some cases outperforming the frontier models. Vijay, I'd be interested to hear, is building your own domain-specific model something that you guys have considered and maybe could you help us think about the cost-benefit analysis of model usage moving forward? Thanks. Okay. If I heard the M&A question correctly, you know, our whole approach to M&A actually starts with organic product development. You know, first we're looking to, you know, we work with our customers to understand what their problems are, what the issues are, you know, and how best can we solve those. We look internally to say, are we, you know, do we have the capability and the time frames and whether that all works together. Then we say, Okay, if that doesn't work for us, let's see if there's a partner solution that we want to consider kind of working with, and trying to embed that into our solutions. T hat often gives us insight in around, you know, kind of what a capability gap that we may be missing is all about. You know, that may be a partner that we eventually acquire, or it may be a partner that just gave us insight into the capability, and therefore we go into the market to then say, okay, we have a capability that we want to solve. At that point, we then look for the best in the industry at solving that problem and work with them, depending upon timing and things like that, to acquire that business. We've been, you know, as you pointed out, quite successful. It's a continuous part of our strategy. As capability gaps do emerge, we then look for that channel approach, then partnership or organic approach first, channel approach, and then M&A. In terms of, you know, where we're looking, I mean, it's really about use case, FC&C, F&I, credit risk, business. It's, you know, we look across our portfolio, but specific gaps in our portfolio, that's not something that I'm prepared to share. I can take the second part of the question, Rick, about proprietary LLMs. I think that was the nature of the question from Citi. Yeah, it depends on the use case. When it comes to our people assets, our device assets, and so on, that is a very deterministic kind of solution. We use LLMs, which tend to be third-party LLMs, to augment the quality and scale of our solutions. It's not really the forefront. There's no need for us to build a proprietary LLM. In fact, it would weaken our solution. When it comes to IDVerse, that is in fact a proprietary model. It is a proprietary LLM that we built, a neural network that we built, I should say. When you heard Matt and Dan talk about the IDVerse solution, that is a neural network that we built ourselves starting in 2016, trained with data over the last 10 years it's been trained. It's a very specific kind of use case that operates on liveness detection, document authentication, and then we take the output of that and feed that into our Risk Intelligence Network. It totally depends on the use case. We absolutely do build our own domain-specific LLM neural networks as we see fit. All right. Thanks. This concludes our question and answer session. I would like to turn the conference back over to Rick Trainor, CEO, for any closing remarks. Yes. Thanks. I'd like to thank you on behalf of the team for taking the time to join us today. I hope you share our enthusiasm for the business with its leading positions in attractive growth sectors, strong organic product innovation, increasing customer demand for our solutions, which gives us confidence in our objective of continuing to deliver strong underlying revenue growth in the high single digits for the foreseeable future. Thanks. Have a great day. The conference has now concluded. Thank you for attending today's presentation. You may now disconnect.