All right. Good morning, everyone, and thanks for joining us today. My name is Andrew Nicholas. I'm the Business Services Analyst here at William Blair. Before getting started, I'm required to inform you that for a complete list of research disclosures or potential conflicts of interest, please visit our website at williamblair.com. With that out of the way, very pleased to welcome Equifax to the 46th Annual Growth Stock Conference. We have with us today CEO Mark Begor and CFO John Gamble. We're going to spend the 30 minutes that we have with a fireside chat format with a wide array of investor familiarity in the crowd. We'll try to keep it relatively high level. Encourage you to come to the breakout in Maher after if there's anything a little bit more granular that you want to hit.
With that preamble out of the way, Mark and John, I wanted to maybe start with a quasi-AI question just around data, where you get it.
Yep
what makes it unique. I think people are generally familiar with the traditional credit bureau business, but maybe you could hit where that data comes from and then also The Work Number.
Sure
People some sense for how unique it is.
First, thanks for having us. Great to be here. I didn't know it was 46 years, so well done. I don't know if we've been here all 46, but we've been here the eight years that I've been CEO. It's always a great conference. Obviously, we talk a lot with our investors around the AI data moat that we have at Equifax. Roughly, actually, slightly over 90% of our revenue comes from proprietary data. When we describe proprietary data, it's data that only Equifax can use, meaning it's not available on the World Wide Web, it's not public market data. It's proprietary data from proprietary sources. As Andrew points out, we've got multiple data sets that we have. Most familiar to all of you would be our credit file, and there's over 10,000 financial institutions that contribute data to us every cycle. That data's proprietary in their environment.
It's proprietary in our environment. Said differently, someone else, meaning OpenAI or Anthropic or Microsoft, whatever, they can't access that data. It's proprietary. The second layer of protection around that data is the Fair Credit Reporting Act. Credit data, income and employment data, those are all governed by the Fair Credit Reporting Act, which puts regulations around how the data's used. We think about that as a secondary moat around the data from a proprietary perspective. Inside of USIS, I'll focus on the U.S., but our international markets are similar. We have a large data set that's unique to Equifax on cell phone utility data. We call it the NCTUE Plus data set, 190 million Americans in that one. Same thing as proprietary. That's data that comes from telcos, from streaming services, from gas, electric companies.
None of that data is available on the World Wide Web. It's only available in that company. When they contribute it to us only at Equifax, and then we can only use it for permissible purpose, meaning we authorize who uses it. Very proprietary. Our income and employment data is quite similar. I think everyone knows that unique to Equifax, we have Workforce Solutions. It's our largest business, fastest-growing, highest margin business. I'm sure we'll touch on that. That data is also proprietary and governed by the Fair Credit Reporting Act. It's another layer of protection around that. As I said already once in the comments, no one else can access that. Only Equifax can deliver it to customers in a mortgage verification, an auto loan, a credit card, a government social services, and a background screen.
Those are all proprietary data sets that cannot be accessed by the World Wide Web from other AI sources. When you think about the contributors, 5 million companies now contribute data to us every pay period, every two weeks. If you think about it, your payroll is in a locked environment at your company or at your payroll processor. No one can access it without permission. You can't get to it without credentials. We think about that as having a data moat around it also. When we think about AI going forward, and maybe we'll move to that, Andrew, is that we think about AI as an enabler and as a lever for growth.
When we think about it, because we have proprietary data, there are companies that I would say don't look like us, but live in different neighborhoods that rely on public market data and info services. That's not Equifax. That's not TU. That's not Experian. Our data is proprietary. We really think about with AI now that we're deploying inside of, and I'll focus on scores, models, and products. The products we're delivering to our customers, we're really leaning into using AI to deliver higher performing solutions for our customers. What AI enables us to do in our scores, models, and products that we deliver our customers is ingest more data. Remember what's super important with the Fair Credit Reporting Act and with the customers that we have, it has to be explainable. Explainable AI is super complex.
Most of the public market AI is not explainable and can't really be used in financial services decisioning. That's a place we've been investing heavily in. We added 10 Equifax AI patents in the first quarter. It's just an example of we're investing in AI technology in order to deliver higher performing scores and models and products. The 10 we added in the first quarter, we added 40 last year. We have roughly 450 Equifax unique AI capabilities through these patents. It's a place we're investing in, and we're seeing really significant lifts in performance when we use AI in our models. 100% of our models and scores are now using our unique Equifax AI capabilities. If you think about what makes our solutions more unique now going forward, number one, we have more data than anyone else. You can't do AI without data.
That's really kind of a fundamental building block. You think about the credit file, cell phone utility assets, our alternative data assets, the income and employment data. We have a wealth of data that's more than anyone else. We've put it in the cloud over the last five years. I think you know we had a massive investment, $3 billion in our tech. We're now the only cloud-native data analytics company. That's behind us. That enables us to do AI with our solutions. We put all our data from siloed data assets as a part of that $3 billion investment into a single data fabric. That's behind us. That enables us to deliver those solutions. We're now delivering scores and models to our customers that have bigger, higher performance. As you know, we sell performance. We're selling higher approval rates.
We're selling lower losses, higher identity pass rates. AI for Equifax is offense. AI for Equifax is an enabler to really drive our top line and our bottom line. I'll wait to see if you want to touch on it, but we're also seeing big gains in productivity.
Sure
inside of Equifax, in our operation centers, call centers, paper processing centers, technology. We have a large technology organization because we're very much a data analytics technology company. We're seeing real productivity building there, and then in our support functions. In 2026, we made AI deployment across Equifax for customers, which we've talked mostly about so far this morning, and for productivity, one of our strategic priorities. That's something that we're seeing a lot of traction on. I think, as you know, just maybe to put one more point on it, we laid out in February as a part of our guide for 2026, we laid out that in operations, kind of the first area we focused on, we're going to deliver $75 million worth of productivity over the next couple of years.
We laid out a guide for this year, where our margin expansion in our long-term framework is 50 basis points. Because of AI and the productivity we're getting just in operations so far, we increased that to 75 basis points, which is a meaningful lift. You can see we're just starting to get into the first chapter of the productivity benefits we expect to roll through Equifax from an AI perspective. Offense with customers, products, models, and scores, and then productivity inside of Equifax. It's a super exciting time for Equifax from an AI perspective.
Yeah, I think last couple of years, our operating thesis for info services, particularly companies that have proprietary data like Equifax, has been kind of this threefold thing, many of which you just hit. It's.
Yep
Improves product innovation. Maybe you could talk a little bit about?
Yep
Vitality score, demand, which we can get to, and then efficiency, which you started to hit on.
Totally
We'll start on the supply side, right? You mentioned the patents, you mentioned you're selling performance.
Yep.
Anything that you could quantify.
Yeah
On that front?
Yeah. So those of you who have been maybe close to financial services, you'll be familiar that a KS score is one of the metrics on performance of a score or a model. Historically, you would fight hard to try to get 50 or 100 basis points of improvement. Now with AI and being able to ingest more data, it's common sense. If you have more data you're using, you're going to get a higher performing product or solution, meaning more predictive. If it's more predictive, it's worth more to your customer. Instead of 50- 100 basis points, we're seeing like 1,000 basis points of performance lift versus old model versus new. That just delivers a lot of performance for our customers, and what that'll deliver for Equifax is share gains, revenue, price margin. Really exciting. I think you touched on it.
Equifax is very strong around trying to be a leader in innovation. We define the KPI that we use, we've used for, I think, 10 years, longer than I've been at Equifax, our Vitality Index. We talked to you about it, talked to our investors about it. We run ourselves internally. Each of our business teams has a goal around innovation every year. We really measure that quite strongly, and it's a big performance metric for our organization. Our Vitality Index is the percent of our revenue from new products. We measure the last three years, so it's kind of a rolling three years. It takes time for a product to scale, and then it becomes into the run rate, so that's why we use that three-year window and been quite consistent on that.
Kind of pre-cloud, our investments in the technology and the AI, we were running 5%, 6%, 7% of rev on our Vitality Index. We set a goal, I guess it's now four or five years ago, to be 10%. First quarter, we were 17%. Last year, we were 15%. We've been 13%, 14% the last four years, so well above that. From our perspective, we believe a company that's innovating is a stronger partner to our customers. Our customers want ideas for growth. They want ideas how to improve their business, and it focuses on higher approval rates, which means more revenue for them, lower losses, obviously higher margin for them, higher identity pass rates. Everything we're doing around innovation really drives around how do we help our customers grow.
We're super excited to see the momentum really post-cloud, moving from that kind of 10% goal to be well north of it at the 17%. A lot of momentum around innovation. As we completed the cloud really a little over a year ago, it was a five-year process, as I said, $3 billion, a huge lift to get everything in the cloud to move all our data there. We rewrote all of our technology, all new platforms. We think that's a real competitive advantage in this AI world going forward, and it also is enabling us to innovate more, which we think is really positive. As you know, in our business, that next credit report that we sell, that next credit score we sell, that next dollar of revenue is very high incremental margins.
It's very attractive to us, and that's what drives really our long-term framework that I think everyone in the room's aware of. We want to grow 7%-10% organically on the revenue side, and we're kind of at the higher end of that this year. We want to grow our margins 50 basis points. That incremental growth drives that operating leverage going forward. Of course, as we said, this year we've got a guide of 75 basis points, and first quarter we were stronger than that. We had a very strong start to the year, which we're quite pleased with.
One aspect, I believe, of the really strong Vitality Index most recently is the credit report.
Yeah
Number flagged.
The income and employment data.
Kind of brings together what you led with. Maybe just very briefly for the audience, talk a little about that development and why?
Yeah
That's really something.
Super exciting for us. We have this unique asset, and I'm sure we'll touch on it a bit this morning, of our income and employment data set, and just maybe framing that for, I think most of the people know, but for those that don't, it's approaching half of our revenue. It's growing low double digit, has 50% EBITDA margins. The data set is income and employment data that we've been gathering over the last two decades. We get the data from companies and partners like payroll processors. There's about 250 million income-producing Americans in the U.S., including everyone in the room here. We have, on an active basis, about 105 million. That was up 11% more people in our data set in the first quarter. It was up about 10 last year.
We're growing into that 250, but point is, we got a long runway for growth. We monetize that data in a mortgage. In a mortgage, for example, you verify credit and you verify income and employment. Auto loans do the same thing. Personal loans do the same thing. We monetize it in background screenings. One of the attributes we get is your job title. We have a digital resume, so we monetize it there. Of course, government, which we'll probably touch on. We use it for the delivery of social services. When I joined Equifax eight years ago as CEO, the first thing I wanted to do, which it took me seven years because of technology, was really to combine credit and income and employment.
You think about the process, both are used in a mortgage and auto loan, in a personal loan, and some of the other verticals. What's really important is, I think you understand this, is that your credit score is your propensity to repay your bills based on your past behavior. Did you pay your bills in the past? It's a score that says you're going to pay them in the future. When you're doing it just off the credit score, which really happens in the marketing side of a mortgage and auto loan, you have no idea if Mark's working or what
Mark's income is, but you have to verify it later in the process, and it's part of that underwriting. One of the things we wanted to do for quite some time, we are now executing it in the marketplace, is we are adding income and employment data to our credit report for free. Our goal is to drive share gains. In a 1B world where there is only one credit report pulled, which there is in the pre-qual and mortgage predominantly. In the application process, they pull three, TransUnion, Experian, Equifax in a mortgage. In the pre-qual, they are only pulling one. We want to differentiate our credit file.
We are adding this data at no charge. In the auto card and P-loan world, they are principally a 1B world, meaning they are going to pull one credit report. Same thing, we want to differentiate our credit report for share gains. We are rolling that out. We put it out in mortgage first, and we are getting some traction there.
You saw it in our first quarter results. We've had some share gains in that pre-qual, pre-application 1B portion of the mortgage environment because we're offering that data. We're also in mortgage, just as another example of that, we're adding our cell phone utility attributes trade lines to our mortgage credit file, which only Equifax can do to differentiate our mortgage credit file. We're adding 40 attributes from our cell phone utility data to make our credit file more valuable, again, at no charge because we want to drive share gains. Same model, obviously, is that that next credit report we sell is very high incremental margin, and in a 1B world, we want to differentiate. We're really excited about that. The mortgage side is just getting going.
We've seen share gains that have showed up in our first quarter results, and those are in our guide for the year. We expect that to grow going forward. I was with a big mortgage originator on Monday in the Northeast, and they're very excited about using it because what it gives them in that marketing funnel is that additional visibility about that consumer who's applying. They know what their credit score is, so that helps them think about what product will they qualify. Now they've got some visibility that Mark's working. We give them the employee name. Mark works for Equifax, which is really important. We're also giving them last year's income.
They know they're this kind of a earner, and that helps them in the underwriting get them to the right product and do a better job really managing their funnel and bringing it through to a product, bringing it through to an application. Think about that same thing in mortgage. Think about it in an auto loan. You have no visibility if Mark's working or what his income is. Can Mark really get in that new BMW, or does he have to go in the used BMW? How do we position them in that marketing process? We think it's really powerful. We're rolling it out in auto card and P-loan, and those are really just getting into market. It's an example of what we can do now with the cloud.
It's an example of what Equifax can do that our competitors cannot because we have scaled data assets to really differentiate our position going forward. We're super excited about that. It's a great example. Another area we're using AI is we now have this alternative data and advanced scoring. Historically, those were generally used by larger financial institutions that had large data and analytics organizations. What we've now done with AI is we've launched AI advisors that would allow, for example, in auto, for a mid-size or a smaller financial institution to run analytics against our entire data set using their portfolio, so they could see how using different data assets from Equifax and modifying their scoring algorithms and approval flow will actually improve their approval rates as well, and then help them implement that in their own decisioning system.
If they want to use ours, we're happy to provide it to them, or also in the channel decisioning system they may use, because for smaller financial institutions, often they go through distribution. These capabilities, what artificial intelligence is allowing us to do, is make these advanced capabilities that were generally only available to the larger financial institutions available to our entire customer base. Very important in the U.S. When you get outside the U.S., most financial institutions are smaller, right? It helps us really also accelerate what we're doing outside the U.S. The same technology is available there because of the cloud investment we made. We have common data fabric, common analytical systems, common decisioning worldwide, and we think that's something that we're highly differentiated in, is the consistency of our systems around the world. They're all cloud-based, and they're all cloud native.
Yeah. That's perfect. We hit supply, we hit demand democratization. Maybe just to wrap up, you mentioned it briefly, just on the AI topic specifically, operational efficiency.
Yeah.
You have the cost savings initiatives. Can you speak maybe more to specifically what you're doing more efficiently?
Yeah
of the savings are coming from and maybe what the run rate is?
I got to tell you, the pace of change here is amazing to me. You think about it, when we were at this conference last year, we didn't talk about AI productivity. It was just starting to ramp. It was early days. It's exponential, and our teams are just adopting it so strongly. As I said earlier, important to us is you set strategic priorities for the organization. We made, in 2026, a big move to say AI in product scores and models and in productivity is going to be a strategic priority. Really important. Each of our teams have KPIs, and we're just seeing exponential adoption across Equifax. When you think about our operations side, we have a large team that is call centers. We take calls from consumers, we take calls from businesses. We're adding AI there.
We're putting agents as the front line, taking the calls. Massive productivity. The calls are better. They're more interactive. We can resolve the first call resolution much more effectively, and obviously, it drives massive productivity. The cost, there's a lot of stuff in The Wall Street Journal, there was an article this week about the cost of tokens are very challenging. We're not seeing that. We're seeing massive ROIs when we're deploying AI from what it's delivering. The first step for us, which we laid out in Feb, was in our operations center, where we have 3,000+ people of our 15,000 fielding calls or managing lots of paper. People, consumers send paper into us about freezing a credit file, or they send paper into us about they think there's an error on their credit report that we have to fix.
Managing that, you think about that's purpose-built for AI. That's the first step that we put pencil to paper and said, "We're going to take $75 million of costs out in 2026 and 2027." We put that in our guide for the year, the 75 basis points versus 50, 25 basis points of incremental margin lift. Really a big deal. Our next place that we're making a lot of traction is in technology. Not quite half, but a little less than half of our workforce is technologists coding. You know that we're a technology company. Every time we talk about a product that gets coded into our technology environment, that's the team that's doing that. We're deploying all the AI tools like Claude and others to really have the AI do the coding.
You can just do the math on the kind of potential productivity and speed of our ability to get products to market more quickly, but do them more efficiently. I think that's the next chapter for us is really getting into what kind of productivity can we deliver from our technology team from the use of AI. We're super energized around how that's ramping, and the adoption of it is very rapid. The last piece is really all the support functions. Think finance, HR, legal. Just really great deployment happening there. I'm super energized. I think it's going to change companies. Every company's going to benefit from it.
We think we're a fast mover in this space, and it's really month-to-month, it's just scaling so rapidly as our teams get more comfortable using the tools and really deploying them. I'm really super energized, first and foremost, about what we're going to be able to do with our customers around higher performing scores, models, and products built off our data and the cloud. Second is the productivity, because it's going to drive not only productivity, it's going to drive speed of decisioning, it's going to drive accuracy, it's going to drive higher compliance. All these benefits are just going to be much stronger because you're able to really digitize a lot of the manual processes and get to more of a decision-making mode versus an accumulation mode of data. Super exciting.
The teams have done an outstanding job of building privacy, security, and all of the requirements that we have because of the confidential type of information, the proprietary data Mark talked about. We need those types of controls before agents can deploy and scale. They've been built, and they're now available. We can now deploy agents at scale while knowing that we're meeting all of our privacy requirements, all of our security requirements, and not putting any of the data we have at risk. We're in a very good place to see this start to accelerate.
Again, if you compare the journal article to our world, very different.
Yeah.
We're not seeing tokens going out of control. We're seeing massive ROIs on each kind of implementation of the AI agents in our operations, and just massive productivity. We're excited about it. Very helpful. Thank you. Maybe we switch gears a little bit with the time we have left and talk about the current environment.
Yep.
I feel like it's been several quarters, maybe even years in a row now of a stable but muted environment.
Yeah
Maybe outside of mortgage. Can you just describe what you're seeing today and how the consumer looks?
Yeah, I think it's the same. Obviously, things changed in the last 60 days since the Middle East conflict, and what's happened with oil and energy and the impact on inflation. That's obviously impacted really all demographic classes, but it's had a more significant impact on the lower-end, subprime, lower-income consumer base. What's positive about the environment is unemployment is very low. When people are working in our world, our customers are comfortable to do new originations and continue running their business. I think broadly, that's how our customers think about it, and we do too. Unemployment being low is really a good thing. When we look through the balance of the year, it feels like that that's going to be fairly stable. How long is this Middle East conflict going to go on?
Probably longer than we'd like, but it's got to get resolved at some point. I think most people believe once it's resolved, oil prices will come down. That'll have a positive impact on inflation. The second is our customers, whether it's a fintech, the big banks, the credit unions, the medium-sized banks, they're very strong. They're operating in a very, I would call it normal, you used the word muted, which I think is fair, kind of environment. It's maybe a better way to put a bow on it. It's a good environment for us. You said outside of mortgage.
Mortgage obviously has been significantly impacted over the last three years, around where rates are. Andrew would remember, but some of the room may remember in February, rates kind of came down pre the conflict. We saw an uptick in mortgage activity. It only takes 25, 30, 40, 50 basis point change in rates for refi activity to pick up for someone to be in the money on a refi. We saw that lift in the first quarter. We beat the first quarter. Part of that beat was from that February bump up in mortgage. It's just a reminder, though, that there's a backlog that's quite significant from the last 3+ years now at these higher interest rates of consumers that are. There's 15 million consumers that have mortgages now, or homeowners that have mortgages over 5%.
There's $10 million over six and $8 million over six and a half, I believe.
Pretty much, yeah.
Big backlog that are ready for a refi when it comes. Of course, we got into March and April, rates went up, and obviously that dampened back down. We're looking for that window for when rates do come down and know that we're building. People are still taking out mortgages, just at a lower rate. It's down about 40%-50% from normal levels, but they're still taking them out now at this 6.5%, roughly. That's a great tailwind for us sometime in the future. As you know, we've sized that for you and our investors that a return to normal would be somewhere around $1 billion of incremental revenue to Equifax. Describe normal, is it like a 4.5% rate? Something like that.
There's a lot of mortgages in there that could stimulate on the purchase side and on the refi side. Then, of course, it's very high incremental margin. That's like $600 million of incremental margin that would come through. What we've also been clear about, Andrew, and you know this, is that when that happens, and we did it in the first quarter, we're going to pass it through. When that mortgage recovery comes, which we view as inevitable at some point in the future, that these kind of 20-year high rates will come down once inflation's under control. We're going to pass that through in higher dividend and higher buyback. We're not going to invest more in Equifax. We're investing the right amounts. We're not going to do more M&A. We're doing the right amount of M&A. It's going to go to our shareholders.
We've been very clear about that, and we did that in the first quarter.
Only have a minute or two, I'll maybe ask a quicker one since you mentioned M&A, and maybe circling back to the start of our conversation around AI.
Yep.
Does AI, the ability to leverage it for new product innovation, does it change the types of assets you would be looking at? Does it make them more attractive, less attractive? Is there any kind of switch?
Yeah. I think it's a great question. The answer is yes. I won't tell you the company, but we were looking at an acquisition last fall that we had kind of a new lens on it, which the market has put on us about could this business be disintermediated by AI? We looked at this business. The businesses we like to buy, and I'll give you a couple examples. Appriss Insights, we bought the incarceration dataset. It's the only incarceration dataset in the United States. It's proprietary, and we own that now. That's like strike zone kind of acquisition. We bought Kount, which had very unique identity data. Buying businesses that have data moats was always important to us. It's more important now.
Sure
with what's happening with AI. This company that we looked at, we said, super interesting company. Liked it a lot. We're not going to go forward because it felt like it could be disintermediated by AI. I think that's a lens that we have. There's still a lot of footprint for us to look in M&A, and I think as you know, we're quite disciplined around what we want to do in M&A, whether it's international platforms. As you know, we bought Boa Vista in Brazil, the number two credit bureau that's growing high singles, low doubles for us. We bought that three years ago. Doing great. Those are the kind of acquisitions we'd like to do, but there's definitely a lens around it to make sure it meets our strategic priorities, that that defensible AI data moat is maintained around what we're adding to Equifax.
Vault Verify, the acquisition we bought in the fourth quarter met that. It was a EWS acquisition. We're excited. We're going to be very disciplined around bolt-on M&A.
Great. We'll wrap it up there. Thank you both for being here, and we'll move to Maher for the breakout for anyone interested. Thank you.