Experian plc (EXPN) Earnings Call Transcript & Summary

So good afternoon to those in Europe, and good morning to those in the States. I'm Ryan Flight. I'm an analyst here at Jefferies covering Experian. And I'm really pleased to be joined by Lloyd Pitchford, today's CFO of Experian. Before we get started, I'm required to read out a short disclaimer. So this event is only for Jefferies clients. Members of the media and the press are not authorized to attend this event. If you work for the media or the press, you are prohibited from participating. The content presented at this event is proprietary to and/or subject to the copyrights of Jefferies or third parties. As a matter of legal compliance, we remind you that you must not attempt to elicit from any speaker at this event any material nonpublic information or other confidential information. And accordingly, the speaker may decline to respond to any question in his or her sole discretion. You may not publish or otherwise publicly disclose the name of or otherwise identify the speakers unless Jefferies permitted in writing. Please note, this meeting may be recorded. By attending this event, you agree to all of these restrictions. Okay. So with that done, Lloyd, will be focusing very much on the kind of longer-term structural growth of Experian here today. But given the timing of this call before we get kicked off, I wondered if you could give us an intro and a brief overview of Q1 results.

Yes, sure. Thanks, Ryan, and good morning, afternoon, everyone. I appreciate the time today. We reported first quarter results earlier this week against the backdrop of the guide for the year of 6% to 8% organic growth. 3% inorganic growth on top and 30 to 50 basis points margin progression. That was the guide for the year. And the first quarter, we started well with 8% organic growth and 4% inorganic growth on top, so 12% revenue growth in total. So a good start to the year. No change to our full year guidance, but a bit above where we expected to be in the first quarter, particularly on strength in 2 areas. One is North America B2B Financial Services. And the second is Consumer Services across the globe, actually. I'll touch on both. So North America Financial Services has been on an improving trend. I think what we saw in Q1 was a couple of things, a very strong mortgage results on the back of strong price. We saw some improvement in underlying activity levels, modest improvement, but it was noticeable. And I think that really reflects what you've seen in sentiment from the big U.S. banks when they've reported their results this week. And we also saw a really strong set of one-off software and analytics sales in the quarter that doesn't necessarily repeat, but that gets us off to a good start. And then our consumer business everywhere, particularly our direct-to-consumer marketplace businesses grew very well. So we saw very strong growth in our Brazil direct-to-consumer business. That was up over 20% -- in the U.K., our direct-to-consumer business was double digits. And excluding our data breach business, which is a bit lumpy, we saw double-digit growth in the North America consumer business. So I think that really -- particularly the strength of marketplace shows, I think, some good underlying signs around the robustness of consumer credit despite some of the uncertainty in the backdrop. So good start to the year and particularly delivering high single-digit organic growth in a backdrop of uncertainty, I think, shows the resilience and the strength of the portfolio that we've put together.

Super. So I guess zooming out a little bit here to more of the structural -- thinking more structurally and long term. It's very clear that Experian has kind of evolved far beyond the credit bureau and we think analytics, autos, health, B2C. But I think that to me, the common theme across all of them is this data-enabled platform strategy that you employ across all of the different areas. So I think that's a good way to start this conversation, if you could paint a picture of this strategy, where we are in the story, how you built the platforms and what that enables you to do going forward?

Yes. So looking back to look forward a little bit, just picking up on your point. If you think at our core, what is it that we do? We acquire data sets and we mine and find value in those data sets. Now the heritage of that was the credit bureau. So what did we have? We had all of aggregated credit data and the insight and value we found was an ability to assess risk on lending. That's at heart what the credit score is. What we've done over the last 20 years is we built a portfolio of businesses that essentially do the same thing, but in lots of different ecosystems. And increasingly, those products and those insights that provide value are being delivered through connected platforms. And I'll talk maybe a little bit more about that. But the theme across them all really is that one of aggregating data and mining value through the delivery of product. Today, we do that principally in 5 ecosystems. Financial services, which is our heritage, but it's much broader than just credit of all that includes things like fraud and decisioning and customer acquisition, using our data to help our financial services clients. We have quite a big health care business that supports hospitals and physician practices in assuring who should pay for treatment. In the U.S.A., it's a very big ecosystem, health ecosystem, a multi-payer, multi-provider market. It's very difficult to understand who pays for a treatment with lots of different coverage models. We do it in the auto business, again, globally here where we support the auto dealer network in all aspects of managing their business from acquiring customers to providing credit to understanding the value of a car, et cetera. In the ad tech world, where you've seen as we've moved from linear TV to connected TV and digital marketing, you've seen data used to ever enrich data sets that can help connect content to audiences. So much more segmented audiences using data connected to much more segmented content. And obviously, we have a lot of data that we can enrich through that. And then lastly, in the direct-to-consumer business, where, as you've seen, we've built one of the world's largest direct-to-consumer businesses in the financial arena with over 200 million members. And as those ecosystems have grown, what you're seeing is the connection of products in integrated propositions. So in financial services, for example, not just the provision of a credit score, but the creation of a credit model that can help determine who you should and shouldn't trust for credit. The creation of a fraud model to understand is an applicant, a fraudulent applicant or not. The monitoring systems that can monitor the efficacy of those models and the successfulness of them going forward, the decisioning systems that deploy them in an application process. All of these are being connected into integrated workflows, and that's really the strategy of the company. And as we press out into the white space, most of our growth comes from doing new things for clients that automate manual tasks inside the company or that help them do things better, so spot fraud in a more successful way by using data and analytics. So that's really how we create value as a company.

That's super. And that leads into what I wanted to touch on next. So when we think about the white space, new products, new verticals, so forth and kind of using that platform to do new things for clients, it's clear that, I guess, innovation is absolutely key for this business. So I wondered if you could touch on before we dive into the 3 different business areas, at a group level, how do you -- how do you manage this? How do you put people in place, the culture, the systems to support this kind of innovation going forward?

So at a leadership level, we focus on 4 big enablers in the company. So the first is data. So you have to have access to the raw material to be able to do this. So acquiring the largest, most bespoke data sets possible. And in the long run, we think the deepest, richest source of data is controlled by the consumer themselves. So why do we have a direct-to-consumer business? Ultimately, because it gives us the case through a relationship with the consumer for them to consent the data that for us to use to provide value back to them. That's a really important differentiator for us. So gain access to the largest, most bespoke data sets, put it on the best technology. So we've been -- had a program of shifting to the cloud and deploying generative AI and other machine learning on top of that data. That's a really important piece of how we create value. To do that well, you have to have the best talent. So -- and to do that, you have to have a culture of innovation, which is the fourth one. And these last 2 are really important to our proposition. So we've been working really hard for more than a decade to create an innovative technology culture in the company, and we were thrilled this last year to be ranked #14 out of over 20,000 companies globally in the Great Place to Work survey, top 25 global company ranked by all of our employees. And that's a testament to the culture that we're creating. And when you put all of that together, what you're asking people to do is to be able to mine that hidden value in the data sets we have and to deploy it in a way for clients that quickly adds value and proves itself. As I say, mostly when we're innovating into new areas, what we're doing is saving the client money. We're either avoiding fraud, avoiding risk or we're displacing internal labor through automation.

Super. So if we now dive into kind of the 3 different business areas, business activities, starting with financial services, as you say, it's evolved quite a lot. I wondered if you could really give us some background on the platform there, the different products you've got at the moment, the product pipeline, competitive edge and how it all integrates together.

Yes. So we think about the flow of the consumer through financial services. So first of all, you have to try and find somebody in the environment. So we have a lot of origination type products that try and help our clients understand consumers and pitch offers to them and find them in the digital ecosystem. Obviously, we match that to our direct-to-consumer business. We have a lot of data that can help assess risk. So if you think of what a credit report and credit history and the analytics around it does, it tries to price risk. Is this somebody I should give money to with the trust to get it back. That's where we came from as a business. But if you think about to deploy that in a credit decision, you need lots of other things. You need a credit policy. So what level of risk optimizes my profit. And to do that, you get to run lots of analytics and back testing. And this is where the Ascend platform in the company was born. And it started with what we call an Ascend Sandbox. Traditionally, the way the banks will try and decide what their credit policy was and credit policy as in at what level of risk will I grant credit is to use sample data -- historic sample data to try and test -- back test what the right level of profit is. With Ascend, what we did was, we deployed the capability for every credit item and transaction for everybody in the U.S. for more than 20 years and all of the analytics and predictability around that to be deployed for clients. What that enabled us to do was to do things like show a client the prospective customers that they had acquired and then reject it because their credit score wasn't high enough, but showing them exactly where those customers went for credit when they rejected them and exactly how profitable or risky those clients turned out to be. And that allows clients to back test and make a much more effective credit score. That was our Ascend platform. We quickly deployed that in over 20 of the top 25 financial institutions. We then extended it into new areas. If you have a credit policy, you need to deploy it and monitor it, and we have a product called Ascend Ops that deploys that credit policy and monitors it. You then get to do the same with fraud, different data sets, but you're trying to assess fraud risk. We have a Fraud Sandbox, Ascend Fraud Sandbox does the same. So if you think about what we're doing is there are lots of different analytics and data pools in managing a consumer as they're acquired all the way through a relationship with a bank. Those are becoming modules on the Ascend platform that are integrated in a way that supports our clients to optimize their performance. And we're really at the early stages of that. And think of the journey that it is very similar to a CRM system for a financial services organization, and you build it module by module over time.

Great. And, I guess, you've become increasingly embedded in your kind of partner relationships with these financial institutions. Could you -- I remember speaking to you before when we spoke about the breadth of products you're currently selling and then maybe touch on where the runway is and the growth prospects there.

Yes. So we are obviously one of the leading data providers as a historic credit bureau. If you looked at decisioning systems that make the credit decision, our main competitor there would be somebody like FICO, not a credit bureau, FICO. If you look at fraud systems, our main competitor would probably be somebody like LexisNexus inside RELX. So our strategy actually is to be the only company that can provide that connected breadth across the workflow of a consumer. And the other thing that really matters here is then how you embed that in the human capital of a bank? We all know you get used to using platforms and you get used to knowing how to use them because you've used them a lot. And once you're embedded in the human capital as well as the financial capital of a customer, you become very sticky. And that's what we're trying to do with the development of our platform strategy. And of course, for clients that don't have a product, we're trying to sell them our first product. If they've got one, we're trying to sell them the second and over time, cross-sell into this connected platform.

That's brilliant on financial services. And now moving on to the kind of second big business area, that's B2B, the new verticals. So we think in autos, health, digital marketing, et cetera. Could you maybe talk about how you've built these businesses, where you have a competitive edge and maybe why you entered those markets and what would make you enter another market going forward?

Yes. So when we look at new areas, what we're looking for is a dynamic of an area that's complex and underpenetrated with data. So if I take health care, I think we all know as consumers, we live our normal digital life where you can do things digitally. And then all of a sudden, you meet the health care system, and it feels like you're stepping back 100 years. You have to fill in a form. You have to do things manually. You have to speak to somebody. So the health care system is inefficient and underpenetrated with data. And it's very complex in the U.S. where you have multipayer multi-providers. So the question of who pays is a really complex task. So we obviously have a lot of data that's relevant through the credit bureau, particularly around identity. If you think about one of the core things you try and do when somebody comes into a hospital is identify them and to prove that they are who they say they are. Well, that's the same as the start of a financial journey. So we have something that we can start from. In the health care business, we made a couple of small acquisitions to prove our way, and then we made a large acquisition more than a decade ago. And we've been growing that business very, very well. It grows very consistently, high single digit, low double digit organically plus acquisitions. It's a very high 40s EBITDA margin and has a lot of runway for growth. And probably the best closest separately listed competitor is Waystar. That's now a separately listed company in that area. But the dynamics are exactly the same, managing huge data sets to try and find value in a complex ecosystem that's digitizing. In ad tech -- maybe I'll do auto first. In auto, we again lent on our credit heritage to be able to support the provision of credit at the point of sale for a car. What we've now done is really spread out a whole series of analytics that can help value cars, it can help find customers for the cars. In North America, you have a very local dealer network. What we can provide them with rather than very manual history of understanding what their market is, we can tell them if you're a Mercedes dealer in Michigan, North Michigan maybe, we can tell you there are exactly 6,240 Mercedes in your area. And 450 of them come up for finance renewal in the next month. Actually, there are also 6,200 BMW owners and the propensity of a BMW owner to switch to a Mercedes owner is exactly this in your area. So you can see how you can use data to really help them find new customers. And that's auto. And the new auto business, if you look at new car sales in North America, they've been flat for 20 years. Our auto business has grown double digit for 20 years. So the secular growth of ever more uses of data is what drives that business, not volume. Our ad tech business, we all know 30 years ago, we all watch the same adverts. And what you're seeing as things have moved ever more digital is the segmenting of audiences and matching it to segmented content. And you do that through leveraging data. And we obviously have a lot of data that can help content to find audience. And if you can do that with certainty rather than having to try and sell a new lawnmower to the whole of the U.K., maybe only people with gardens would be interesting or maybe only people with gardens who didn't just buy a lawnmower in the last 3 months, for example. That is much more valuable than the audience of the entire U.K. And as you're seeing those audience segment, our ad tech business has grown. We help connected TV in making the ads you see on your connected TV different to mine as an example. If you're looking on The Economist or [indiscernible] the ads you see are different and they'll be different because we've helped create the match of content to audience there. So what you can see actually is a really burgeoning business that takes data sets in different ecosystems and finds new value through product creation on top.

That's super. It's proof of that leveraging of your data assets in these new value pools is really interesting. I guess, moving on at this point to the third business area, which is B2C, really interesting area of the business and obviously pretty unique to Experian having it in-house with the rest of the B2B business. Could you give us some context there about the platform that you've built, your competitive edge, the scale, product offering. And I think what's really interesting here is this future runway of products that you have, I guess, lots of ability to roll out new products going forward.

Yes. So we've built a very sizable audience in our direct-to-consumer business now. So 5 years ago, we had very few members. We've now got over 200 million relationships members on our platforms in our largest markets. And that gives us an audience that we can now curate value for, sell product to and find different value in their data. And so the job of work to do is now shifting from build audience. We're still doing that, but to increase the number of SKUs, the things that we can do for those consumers. So historically, you would have said understanding your credit position, your credit report and score would have been the core of our legacy offering. What we're migrating to is finding value for every consumer. They all have different need states. So if you think about people in your life, so for me, I have a young daughter, no credit. So she has to build credit capacity. So we're relevant to her in building credit capacity, and we have products that can help do that. My parents, my parents read all the wrong newspapers. They're mostly terrified all the time. They're not interested in credit, but they are interested in identity protection. So we have products and services that can help for that. For me, services that can find the product and apply quickly by filling forms in and finding a way through the ecosystem. Those are really interesting for me. And finding different products for different consumers is where we're heading. And we now have a whole swath of products that can help people save money, can protect their identity, can manage credit and identity risk, et cetera. And that's the breadth. And if you develop a valuable consumer platform with high engagement, what happens as you grow is the cost of acquisition reduces because you're engaging a membership base and the lifetime value increases because your swath of content products increases. And that's the journey we're on. And if you look at our direct-to-consumer business, over the last 5 years, as we've started to scale, you've seen a lot of growth. It's accretive to growth, but it's also very accretive to margin in terms of the growth. We've been adding about 100 basis points of margin each year in our direct-to-consumer business as it's reached this nice point of scaling. And we're using it to find some very unique new sources of value. And maybe I'll bring that to life with an example around car insurance in the U.S. The U.S. car insurance market is mostly a broker and direct sell market. And what that means is the insurance carriers spend something like $12 billion a year acquiring new customers, top of funnel, filling the airwaves with brand advertising that they then seek to convert. And the reason they do that is they don't know who's shopping for car insurance. So you have to have quite a high funnel spend. What we did in our B2B business, we have the data on every car in the U.S., who owns it, when they bought it, all of that data set. There are only 2 of those data sets, and we've got one. In our B2C business, we went out to consumers and gave them the chance to boost their credit score by finding in their bank account and insurance transaction. Insurance transactions are credit transactions. And if you can find them, they can boost your credit score. And millions of consumers gave us access to the bank account to find that transaction. So when we combine that, what that means is we know who you are, what car you drive, who your insurance provider is, how much you pay and when your renewal date is because we can see it in your transaction history. And if you combine that together, the data assets, the insight you have is who's shopping for car insurance now and what might be a relevant offer? And you can see how that data asset when you go to the insurance providers can help quite radically improve the efficiency and conversion of that $12 billion of customer origination costs. That's an example of how we can find new value in the data sets we have and change the value creation of some of our clients. And we have lots of examples of that in our history.

That's brilliant. And I guess there's a long runway of new products you can now onboard now you've got the kind of platform in place and the scale in the audience. I guess, having touched on the 3 different business areas, if we can maybe zoom out a little bit and look geographically, Brazil has obviously been a fantastic market for you. Could you talk -- remind us of the kind of structural story there, a bit of a growth engine, what the kind of runway is going forward?

Yes. So a bit like my health care example, we look for businesses and countries and models that are underpenetrated with data. So when we entered Brazil about 12 years ago, a bit longer than that, actually, Brazil was what's known as a negative data market. So credit was assessed just by looking at who had failed to pay a debt. Most other countries are what's called a positive data market, which isn't you assess risk not just by have you failed, but what type of credit and what's your frequency of interacting. And obviously, there are more data points. The richness of data and positive data is an order of magnitude higher. And what that meant for Brazil is it had high credit spreads. It was underpenetrated with data and credit. So if we could enter, then the direction of travel was more credit penetration and more data penetration and that secular growth would drive us forward. And so that's why we entered and that's what we've seen. Brazil has now shifted to a positive data market. We've used the richness of that data to enter new verticals around the credit bureau, so direct-to-consumer, fraud, agri payments, et cetera. And you've seen, in particular, we've built a very material direct-to-consumer business with very strong organic growth. It grew 24% in the first quarter. So that's really the dynamic of Brazil. There's some macro weakness in the B2B business just now that's holding back some of the cyclical growth. But overall, we think the medium to long-term growth in Brazil is very strong double digit.

Could you maybe -- you kind of touched on it there a little bit anyway, but I know in your most recent update, you mentioned India and some of the Southern European countries. Could you maybe talk about -- I know there's still a big runway in Brazil itself, but how you kind of manage these other geographies and keep an eye on them? And if at any point, you take a decision to kind of step up your efforts there?

Yes. So big 3 geographies are North America, U.K. and Brazil. We have a Spanish Latin America business, which is in Colombia, Peru, Chile and Panama. And then we have an EMEA/Asia Pacific business, which has core businesses in clusters of the ones you mentioned, so India, Australia, South Africa, Southern Europe, Northern Europe and Germany. And we actually withdrew from a number of subscale countries over the last 5 years to focus on the countries that we think we can scale. So I think as we look forward, I think we're in the countries that we want to be in. So the journey from here is actually scaling in those countries and building out the full distribution channel for our products rather than geographic expansion. If a dual asset becomes available in another country, maybe we'd look at it. But I think it's more likely that we scale in the geographies that we're in. Some of those EMEA, Asia Pacific assets are very large long-term potential. India is the obvious example there where we've got a -- one of a small number of credit bureaus, and we have a fraud business there as well, which we think in the long term could be material. Today, it's relatively small.

That's super on geographies. So moving on to kind of the next topic area that I really wanted to talk about, which is M&A. So you seem to have kind of stepped up your kind of number of deals recently. So I wonder if you could talk about your strategy, what you look for, how they fit into the business and what they -- kind of what they bring to you?

Yes. So obviously, we're in a technology and high innovation area. So constantly scanning the environment for new technology, new business models that we can bring into the company is really, really important. Our first filter is strategy. So looking for things that complement our business and help us develop and distribute propositions. And then we look for some of the things I've been talking about. A business that has a data asset is really interesting to us because we feel we can mine value and create value on top of that data asset better than anyone. So in this last year, 2 examples of that. We bought a business called ClearSale in Brazil. And what ClearSale has is a data asset of about 70% of every digital transaction in Brazil. So when you have that data asset, you're able to screen out fraudulent transactions much more effectively. You can find trends, you can see digital archetypes that are fraudulent. So that's a really interesting asset in Brazil. In Australia and New Zealand, we bought a credit bureau where we have a credit bureau, and we can combine them and take a lot of cost synergies. And those are examples of data assets. The other example is where we've got distribution, where we have platforms and positions with clients, if we can buy an early-stage software company with product that we can tag on to our sales, obviously, we can add a lot of value through distribution to that IP. So 2 examples of businesses where we've done that -- this. The last year, we bought a business called NeuroID, which is a behavioral biometrics business that prevents fraud, tries to identify if you're a human or a bot. And we also bought a business called Audigent, which is in the digital ad tech space. So those are some examples of what we look for. In terms of our screening strategy first, and we do 3 financial tests; post-tax, IRR, economic test. And we have hurdles based on business maturity risk and country risk. We screen on accounting return. So return on capital we try and target double-digit years 3 to 5. And then we screen on a benchmark to a share buyback. So those are our metrics. And if you look back, what we've seen, we don't set targets for M&A. What we do is we try to create value. So in the period shortly after COVID, what we saw was valuations were getting very peaky. So we pulled back from M&A a bit. They've moderated over the last 18 months. So last year, we deployed, including the first day of this financial year, about $1.6 billion of capital in areas where we've been watching for some time, and we thought we could create value.

That's brilliant. And I know that the M&A provides kind of growth runway fits into the group nicely, does have an EBIT margin headwind to the group, which leads nicely into the next topic because that is -- we now -- which we didn't really have historically with Experian, but we've now got this really nice structural EBIT margin expansion story even with the M&A kind of dilution. So I wonder if you could talk about how you're delivering that and the kind of story where we are and what we have going forward.

Yes. So if you go back over the last -- before last year, we were adding about 20 basis points of margin per year. So quite modest. One of the reasons for that was we were investing in our cloud program. And for anybody who's dealt with the cloud transition program, you build up dual run costs because you have 2 sets of infrastructure. So we've been progressing our margin modestly despite overcoming that growth in dual run costs. The progress we've made with the cloud transition means this time next year, those dual run costs start to abate. So last year, we changed our framework to be able to grow margin 30 to 50 basis points a year. And in the first year, we beat that. So we grew organic margin by 90 basis points. That was offset by a bit of FX and a bit of acquisition dilution that you mentioned. This year, we've guided to 30 to 50 basis points. But acquisitions are dilutive by about 30. So organic margin is 60 to 90 -- sorry, 60 to 80 in that frame. So I think what you're seeing is the benefit of scale and the benefit of some of those technology program costs abating. And I think -- we think that gives us enough -- that framework gives us enough flexibility to make sure we're continuing to invest and innovate. But you see we've started very well in our -- against our medium-term framework.

Could you touch on a very hot topic, of course, AI, how you're using it in the business, how it may affect the business going forward and so forth?

Yes. So AI is really interesting for us. It's an extension of a trend we've had for some time, which is machine learning. And to bring it to life, we have something like 11,000 employees that are involved either in data science or technology or product development and product management. So people who are coding and building a product. So we have a really large talent base that are very involved in and interested in new technology and coding and the like. And so this is clearly super important for us. If you think about what AI can do in our business is this value creation of finding insights and data. Well, AI can accelerate. It can find new insights in our huge data set. So it can accelerate the product development funnel. What it can also do is accelerate how product is developed. So it used to be if you were coding for a product, you had to code it line by line. Then you could get code repositories to reuse code or Copilot to draft code. With Vibe coding, you can now build whole products with artificial intelligence. So the productivity in the product development life cycle is increasing markedly. Of course, what we are also offsetting that with is investment in AI in building our own LLMs that we can leverage on our bespoke data set. So there's a constant tension between those 2, between are we investing too much given productivity? And are we investing enough given technology change? And we have a very, very deep conversation about that regularly given the scale of technology change. But overall, AI is an accelerant for us.

Brilliant. And I think it's really interesting how it's been used in the B2C platform as kind of tailoring it to the individual. Lloyd, we've got a couple of minutes left. The last topic that I wanted to talk on just because it is quite lumpy and quite volatile is, what you do in the data breach area. So structurally, we're clearly seeing more and more data breaches. What exactly does Experian do in that business?

Yes. So this is our North America data breach business inside our Consumer Services business. And essentially, what it does is if you're a brand with a big consumer base and you suffer a data breach or a ransomware attack, one of the things you may choose to do is to provide the Experian identity protection service to all of your consumers to help them cope with the risk that they've seen. And what you find is it's lumpy. It's also bifurcating. You see some brands, high-value brands want to attach themselves to a high-value service like Experian. So that's our target market. Lower-value brands perhaps just want to tick the box, and that tends to be a lower margin business. So we don't tend to play in that bit of the business. So it can be a bit lumpy. Last year, there were a lot of breaches. We had quite a lot of revenue, but it tends to be lumpy and one-off. So what we've seen since then is as we've grown over the comp, it's been about a 1% headwind to growth. that headwind ends with Q1 that we've just reported. So you see that drop away as we go into Q2.

That's super. That's really, really helpful to understand that and everything else you've mentioned. So once again, thank you very much for joining it. We've come to the end of our time. Thank you to everybody else that's joined the call as well. Please do reach out if there's any follow-up questions. I'll hand back to Lloyd for closing remarks. But again, thank you all.

Thanks, Ryan, and thanks for everybody in the U.K. for joining us at 3:30 on a sunny afternoon on Friday. We really appreciate the time. And hopefully, you can see actually a really interesting data-enabled technology story in Experian that's moved now well beyond its heritage in the credit bureau. And you can see that in the financial results that we're delivering. So I wish everybody a good weekend, and thanks for your time.

Thank you. Have a good weekend.

Thanks.