Experian's tech chief defends credit scores: 'We're not Palantir'

Experian is one of those giant multinationals convoluted enough to have multiple CEOs all over the world, so first I asked Alex Lintner, Experian's CEO of technology and software solutions, to dig into the classic Decoder questions and explain how all of that even works. He oversees big operations like security and privacy, and now, of course, AI. If you want to participate in the modern economy — rent an apartment, buy a car, get a job, etc — you’re part of Experian’s ecosystem, whether you like it or not. At its heart, Experian’s core service is data about people and the choices they make. And this extremely valuable data weirdly makes Experian a part of your life — a life that becomes much smoother if the data the company collects about you tells a good story. Read the full interview transcript on The Verge. Links: Roughly half of Americans are knowledgeable about personal finance | Pew Research How Americans view data privacy | Pew Research Consumer voices on credit reports and scores | CFPB Mercedes-Benz CEO Ola Källenius on Decoder | The Verge The Palantir app ICE uses to find neighborhoods to raid | 404 Media T-Mobile customers exposed in major Experian breach (2015) | The Verge All the news about the Equifax breach | The Verge Subscribe to The Verge to access the ad-free version of Decoder! Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Decoder is produced by Kate Cox and Nick Statt and edited by Ursa Wright. Our editorial director is Kevin McShane. The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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• 1:30Speaker 04

  Hello and welcome to Decoder. I'm Neelai Patel, editor in chief of The Verge, and Decoder is my show about big ideas and other problems. Today, I'm talking with Alex Lintner, who is the CEO of Technology and Software Solutions at Experian, the credit reporting company. Experian is one of those multinationals that's so big and complicated that it has multiple CEOs all over the world. So Alex and I spent quite a lot of time at the beginning, simply talking through the Decoder questions, so I could understand how Experian is structured, how it works, and how the kinds of decisions that Alex makes actually work in practice. There's a lot there, especially since Alex is in charge of the company's entire tech arm. That means he oversees big operations like security and privacy, and now, of course, AI. All of which is always important, but even more critical when you factor in what

• 2:14Speaker 04

  kinds of information Experian collects and stores about, well, literally everyone. See, if you want to participate in the economy, the way the vast majority of us would like to do, renting an apartment, buying a car, getting a job, applying for a mortgage or a student loan, you're part of Experian's ecosystem, almost whether you like it or not. You'll hear Alex talk about consent a whole lot in this conversation, and he'll argue that you can opt out of the whole system. But the reality for most people is that interacting with Experian is pretty much non-negotiable. It's hard to do basically anything involving money without a credit score. That's really the tension at the heart of a company like Experian. Credit scores dominate so many aspects of our lives, and they're controlled and calculated in ways that most of us feel like we have very little direct influence over.

• 2:57Speaker 04

  But at its heart, Experian's core service is data. Data about people, about their money and what they do with it, the bills they pay or don't pay, about the decisions we make. And all of this extremely valuable data weirdly makes Experian a part of our lives. Lives have become much smoother if the data the company collects about you tells a good story. So Alex and I spent a lot of time talking about the responsibility Experian feels towards all the people it serves, not just on a security and privacy level, but also a moral one. In fact, there's one particularly illuminating exchange that Alex and I had. A lot of people don't like the power Experian has, and by extension, they don't like the company either. So I asked Alex pretty directly about that, and I found his answer to be pretty surprising. It's maybe one of the most memorable answers we've ever gotten on Decoder, actually.

• 3:42Speaker 04

  You'll see what I mean. I also asked Alex pretty directly about the other big, messy question taking him through, generative AI, and why exactly we should trust non-deterministic systems when they start interacting with really sensitive data about our financial lives and making decisions about us. You'll hear Alex talk a lot about AI oversight and how it's being woven into the systems Experian uses for everything from risk assessment to predictive financial modeling. But as we all know, AI systems are inherently risky. They get things wrong. They hallucinate. They might make incomplete or incorrect conclusions about very real human beings in ways that dramatically affect their lives. So I really dug into how Experian and Alex see AI technology being used internally and within the broader scope of credit reporting. And I also pressed Alex on the capability

• 4:27Speaker 04

  gap between what AI might be able to do today, what we think it can do, or what AI executives tell us can do, and the reality of what it can actually do and how well it does it. As you can tell, this was a really in-depth conversation about a pretty complicated set of ideas, and I really appreciated Alex's willingness to get into that complexity with me. I'm very curious to see what you all think of this conversation. Okay, Experian's CEO of Technology and Software Solutions, Alex Lintner. Here we go.

• 5:05Speaker 04

  Alex Lintner, you are the CEO of Software and Technology at Experian. Welcome to Decoder.

• 5:09Speaker 05

  Thank you, Neil, for having me.

• 5:10Speaker 04

  I am very excited to talk to you. There's a lot to talk about. Experian is a sort of fascinating company. A lot of people have a lot of feelings about Experian, which I want to talk to you about. Every company that comes on the show lately, every executive tells me that they're an AI company. I think Experian wants to be known as an AI company. We're going to get into that. Why don't you tell me what you think Experian is today, and what it has been, and what you think it should be in the future?

• 5:32Speaker 05

  Experian is a global data and technology company. We help consumers and businesses to make financial decisions and protect their data and identities. On the B2B side, we have four verticals: financial services, healthcare, automotive, and marketing services. On the D2C side, we protect, I would say, we provide consumers with information that helps them understand, protect, and manage their financial lives. So we help them build credit, qualify for their next desired loan. My favorite example is getting their first mortgage, which is a hard thing to do in America, but a major wealth builder for Americans.

• 6:17Speaker 05

  We give them access to comparing financial products so they can lower their borrowing costs. We protect them from fraud and identity theft, like I mentioned earlier, and we help them save when they buy car insurance. So that's Experian to me.

• 6:35Speaker 04

  This is going to be very reductive, and I'm saying it on purpose because I'm curious if it really is this simple or if there's more complexity there. That sounds like Experian maintains a big database of information about people, mostly about their credit. And when you say it protects that information, that's because having all that data is very important and very powerful and very valuable. But it's also the information that mortgage lenders use. It's the information that car insurance brokers use. How do you think about the core product? Is it just a database? Or do you think about it differently?

• 7:08Speaker 05

  Well, you know, what you want to do is AI. If I go straight to the AI topic, though, maybe we should back up a little bit. If AI is a platform capability, it's not a feature. And we use AI primarily to help embed governance, help explain ability, which is required by law and desired by the consumer, and to actually facilitate human oversight. So then when you then back up into where we came from and your question at the core with all the data that we hold, from a technology perspective, and I'm the tech guy, so I'm

• 7:48Speaker 05

  going to talk about the technology, that means that we apply data analytics and AI into the hands of decision makers. And those can be in businesses, financial institutions, mortgage companies, like you just said, but we also supply it to the consumer directly. And the objective is the same. The objective is to turn complex data, complex information into easy to understand, actionable guidance, so that either the lender or the consumer can make a confident decision.

• 8:23Speaker 05

  Yeah, and that's the that's the objective. You need the same data for that. And both sides need to see it because the data is the is the objective truth. And then the consumer can make a decision. And the lender can make a decision if you're talking about financial services in particular, which you're example did.

• 8:42Speaker 04

  Sure, I I think maybe I'm just I'm I'm way at the bottom. I'm at the primitives here, right? The the main thing is a big database of financial information, right? About consumers and their credit history and their ability to to pay for things. Is that the is that the main thing? Is there another core element of the product?

• 8:59Speaker 05

  It's a core element. I I think you're you're you're over emphasizing the financial information. You know, financial services is one of the sectors, like I said earlier, we we have a lot of other information.

• 9:11Speaker 05

  That is useful, that has nothing to do with sort of the core lending information that we have, the the history of people's lending behaviors. And and the other information is is just as useful. So if you look at the automotive vertical, for example, you know, we have a a an equivalent to most people know Carfax. We have something called AutoCheck, you know, has vehicle history, ownership history. You know, maintenance and repair history, accident history. So there is a lot of other information that is actually relevant for these decisions. It's not only the financial information that we have about people. And by the way, you know, when people say financial information, often it's interpreted as we have account numbers, etc.

• 9:56Speaker 05

  So we do need account numbers to match, you know, the the the accounts to people. But it it never goes out and and it's it's it's double encrypted, so super protected. We don't use any of that information for any of the services that we provide, except for the pinning, so that we can match it to a person.

• 10:14Speaker 04

  Can I offer you my feature suggestion for AutoCheck? I do a lot of idly shopping for cars I'm never gonna buy. And I love feeding the AutoCheck report into ChatGPT, and then ChatGPT tells you a little story about the car. If you find a particularly sketchy auto check report, it tells you a story about how the car was obviously stolen and is being laundered. And that's wow, you should just put it in the

• 10:33Speaker 05

  I gotta try that. That's very fun.

• 10:36Speaker 04

  It's a good time. If you're holding a crying baby and you're like, I gotta sit here for another hour. It's like a very good way to spend the time.

• 10:42Speaker 05

  Just had my third grandchild and she's she's two weeks old. So that's actually very, very current. I love holding her and now I know what to do while I do that.

• 10:50Speaker 04

  I'll send you some. There are some specific models of cars where it's like all of them are stolen for some reason. It's very good. The reason I keep asking on the database. I have a, you know, a thesis sort of in 2026 that maybe what we're all discovering is that all of our lives are captured in databases, but there are these huge stores of information held by various companies, held by various governments, held by various agencies inside the government. And maybe what AI is going to do is make those databases more legible. And maybe what it's also going to do is make the holders of those databases far more powerful. Right. Because you suddenly have more access to the data. You can use it in different ways. You can connect all these databases in different ways. I hear this picture in a lot of people.

• 11:33Speaker 04

  You have the biggest database, right? Experian is one of the most powerful databases in American life. So there's a reason I'm starting with that. I'm curious how you think about that power, right? Is it becomes easier to express that power? It becomes easier to share the contents of that database with people. It becomes easier to query that database? How do you think about that responsibility at Experian?

• 11:54Speaker 05

  It's a giant responsibility and we take it very serious. You know, there are a couple of aspects to that. Our business is based on consumer trust. Once the consumer starts losing trust, the brand goes nowhere. Investors start losing faith and everything goes down the drain. So, you know, if we don't do that part of our business well, there is all the other stuff that I could talk about and then maybe talk, we talk about in a little while, it goes away. You talk about it as a database, you know? Neelai, the way I would talk about it is I would talk about that our largest businesses are on modern, cloud-native and NI-enabled platforms. And these platforms let us securely ingest and massive amounts of data,

• 12:40Speaker 05

  like you're saying, in real time, and then apply advanced analytics and machine learning while we keep privacy, consent and security at the center. That's how I think about it. So the database, you know, as a function sort of has morphed into data lakes and then now it's, you know, I would refer to it as a platform. Most of the data, what we do when we hold with the data is, you know, I start with the last part that I talked about. So keeping privacy, consent and security at the center. What you really need to think about is how do you do that? And how do you do that better than anybody else? And how do you do that in light of the fact that, you know, the bad actors know everything that you just said.

• 13:25Speaker 05

  What you just said is we're one of the largest data companies in the world and therefore we got a lot of information and bad guys like information. So, you know, to keep it secure, you need to have a, I'm gonna call it a bulletproof setup from front to back of every application. Most people, you know, talk only about encryption, but it goes way beyond that. It goes to access rights. I named that consent earlier. It goes to how do you store the information? You know, you can shard it, which I really like, you know, break it up. So when people find Eli's information and Neelai's information, excuse me, they find maybe only your first name, not your last name.

• 14:08Speaker 05

  They maybe find your street address stored somewhere else and your account information stored again in another place. In other words, if you break it up into 25 shards, they'd have to break 25 encryption keys, know how to pin it back together to one individual in order to really understand Neelai. That's complicated. So the game is we need to have security systems that stay ahead of the bad guy. And we need to have at the core of our mission, the core of our purpose as a company, that every employee needs to act to a purpose that says, what I now say for the third time, keep privacy, consent and security at the center of everything we do.

• 14:48Speaker 04

  Let me ask you an existential question about that. What if I don't want you to know me? Right, I mean, what we're talking about is we're collecting a massive amount of data on regular people. I think I hear from our audience every day, why is this happening to me? Why do these companies already know so much about me? How come when I use my loyalty card at the grocery store, that gets meshed up with a bunch of purchase data on Instagram that gets combined with a bunch of data? Like, why is my phone listening to me? Right? It's like basically that the end result of that. I'm like, I don't know that it's listening to you. I think there's a lot of data about you that makes it appear that the phone is listening to you. And that is more scary and less legible than the phone is listening to you. Have we opted in to experience? Do you think about that level of maybe I should,

• 15:32Speaker 04

  we should ask everybody if we want to be tracked in this way, or track as many people as we do?

• 15:36Speaker 05

  I have two answers to that. So the first answer is, privacy laws are such that you can opt out anytime. You know, so if you, Neelai, don't want your information stored, you can do that. You could do it on your phone, so it doesn't listen to you, and you can do it with us. The bigger answer I have is the following. And, you know, this is based on research. This is, this is an absolute truth proven over many decades. And that is that prosperity for a family and prosperity for an individual is strongly linked to access to credit. In other words, you can look at countries that don't have access to credit

• 16:17Speaker 05

  like we have here in America, and you can look that their economic evolution lags behind that of the United States. You can look at families where maybe the parents didn't have access to credit, and therefore, they couldn't do what now their children can do who have access to credit. You can look at an individual of how fast do they advance? Because credit allows you to pay forward your earning power and your ability to repay a loan, and therefore make investments that then can be accretive to your wealth. So, put it in another way, if a lender would not have information about an individual, Alex or Neelai, they cannot

• 17:01Speaker 05

  make a decision about whether they're going to lend money for you. And let's be clear, lending is one of the riskiest businesses there are. Let me describe it in the following way. You know, I- Look at you, Nilay, and I ask you a couple of questions about, you know, have you had a couple of loans before? What do you want to do with the money? How are you going to pay me back? And then I decide whether you're a good guy, worthy of getting this loan or not. Well, if I give you the money, at that point, I'm in the risk because the money leaves my account, the lender's account, goes into your account, and you can do with the money as you please. So it's a very high-risk business. So the lender needs to have the information in order to make the decision.

• 17:45Speaker 05

  You, the consumer, need access to credit because it will advance your standard of living, your quality of life, and your wealth creation. So privacy laws allow you to opt out, and it is actually in yours, the consumer's interest, that you make the information available for lending.

• 18:04Speaker 04

  One other question I have about that, and I think, again, this is going to be a theme of 2026 in our coverage, I feel, is that AI enables these things to happen at a different kind of scale, right? Because you can automate the systems in different way, you can query the systems in different way, you can extract value from the data in different ways. And I wonder, I agree with you, right? Lenders need to mitigate their risk in some way, they need to know who they're lending to, they need to manage whether or not they think they're going to get paid back. Being able to do that at scale and saying, all of these should be centralized stores of information and not more local, right? It's my local bank and my local community that needs to evaluate my risk profile. There's something about that scale that feels different.

• 18:47Speaker 04

  And obviously, Experian enables massive scale. Do you think your responsibility is different with scale?

• 18:52Speaker 05

  That's a really interesting question, but, you know, I'm the tech guy here. And from a technology perspective, you know, I don't want to make a sort of macroeconomic or regulatory statement, but from a technology perspective, it's definitely true. Because if you have scale, you hold more information. And as you hold in more information, you need to deal with it responsibly. And again, it gets me back to those three tenants. We need to protect privacy, consent, and security. And if you have more information, you better do it really, really, really good. To get back to your local or national or global scale, you know, so first of all, there are very few global financial players. So let's start there. They are literally,

• 19:33Speaker 05

  we can probably count them on on two hands. And even, you know, I know those companies from the inside, they don't always act globally. You know, they often act locally. So I don't want to name any names, but you know, large international banks born in Europe, large international banks born in New York City where you're at, you know, they have an American business strategy and then they have whatever British and Australian businesses. It's actually different. And their models are different, and lending criteria is different, and the lending products are different. So their sort of global presence is rare. Now, let's talk locally versus nationally or super regionally. In North America, we

• 20:18Speaker 05

  have the good luck that we have 7,000 financial institutions. That is a model that's unique in the world. We don't have that anywhere else. And if you go all the way from the top to the bottom, at the bottom, you would find these credit unions. And credit unions are typically very local, though there are now large ones like Navy Federal Credit Union, you're familiar with all of those, you know, that serve the armed forces everywhere, or USAA, who serves members and relatives of armed forces members for insurance and banking around the country. So there are some exceptions, but largely credit unions are very local. They do not have access to capital like the large, super regional or national lenders have. And access to capital is important because it is a volume game. The more...

• 21:03Speaker 05

  You buy capital, you know, as a reseller, which is what a bank is, the better the terms you get, and therefore you have the potential of offering better terms to your borrowers, to the consumer. And therefore, I think the mix of local and national is a good mix. It has worked here in the US.

• 21:24Speaker 04

  It's definitely worked to make the cost of capital come down in various ways, although who knows what's going on right now. Every day it could be different. But I think my question is very-

• 21:35Speaker 05

  Less predictable than it's been in a long time.

• 21:38Speaker 04

  But I wonder if the trade-off is a feeling of disempowerment for the actual consumer, right? And that's one of those trade-offs. It's hard to, yes, there are some privacy laws in the United States. There are not very many, right? Yes, there are some recourse against a financial institution, or if there's a data breach, but there's not many. And so I'm just interested in that trade-off, in your perspective, in that trade-off. I will say that you have perfectly teed up the decoder questions, as you describe the structure of multinational banks, because Experian's org chart, to me, from the outside, is bananas. Straightforwardly, there's Brian Cassin, who is the CEO of Experian, then there are CEOs of regions. So there's a CEO of Latin America, for North America, and

• 22:23Speaker 04

  then there's you, and you are the CEO of technology and software. So, explain to me how that all works.

• 22:29Speaker 05

  All right, let's get into that. So, the way we work, we have a federated system, and that's not unusual. So maybe our titles aren't super intuitive and don't explain it, but let me try to explain it. You have central functions where everything is the same regardless of where you are in the world. Think of finance, think of HR, and think of technology. So you want to have technology standards, you want to have security standards that you apply everywhere in the world. There are economic reasons for that, you know, you don't want to have a slew of vendors, you want to have golden pathways, that is what keeps everybody, you know, secure, and that's how you manage consent, and that's how you manage privacy. And all of that should be done in the same way, so that we have control

• 23:11Speaker 05

  over it, our governance can look at it, auditors can look at it, because we are auditable by the SEC, and they all can say, okay, we apply those standards the same way, regardless of where you are in the world. Now, if you look at- The context, so call it the economic context, call it the socioeconomic context, so how much do people make, et cetera, et cetera. That differs everywhere in the world. So it differs whether you're in the United States or my native Germany or India or Australia. We're active in all these countries and the context is different. And therefore, our go-to-market oriented business units, they have CEOs that look over the region,

• 23:56Speaker 05

  understand their context really well and then the product is applied appropriate for that country. And, you know, by the way, regulation varies. So we do have to adjust some of our security and privacy dials to comply with the country-specific regulations. And that's why we have the matrix function. So some central functions that look at achieving scale, that look at achieving, you know, clear governance, doing everything the same way and market-specific to the consumer needs, the context that is specific to a specific country. That's how, or region, that's how you should think about it.

• 24:40Speaker 04

  We need to take a quick break. We'll be right back.

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• 26:10Speaker 04

  We're back with Alex Slintner, the CEO of Technology and Software Solutions at Experian. Before the break, we had really just started digging into the company's structure, which is really complicated as multinationals like this one tend to be. So I really wanted to dig deeper into that org chart with Alex and see what it could tell us about what kind of company Experian really is.

• 26:30Speaker 04

  How many people are Experian overall?

• 26:32Speaker 05

  23,000.

• 26:34Speaker 04

  And how many are in your division?

• 26:36Speaker 05

  I have a direct reporting line of 4,000. We have 11,000 technologists. So think of my function, 4,000 on my direct reporting line, roll up to me.

• 26:46Speaker 04

  Okay, I was gonna say 4,000 direct reports is a little over the guidelines, I think.

• 26:51Speaker 05

  And that in my direct reporting line, so all the way down.

• 26:53Speaker 04

  Yeah, I was joking, yeah.

• 26:54Speaker 05

  I got seven, seven direct reports and then it goes down. And then the other 7,000 are in technology organizations. And I still set the standards and the policies, our technology policy, that everybody needs to work by, but they're not in my direct reporting line.

• 27:08Speaker 04

  And is that structured so it meets the needs of the regions? Or how does that work?

• 27:12Speaker 05

  Yeah, we're trying to walk that fine line, exactly like I explained. You know, my, my job is build a backend that is superb. Make our platforms the most secure and least expensive way for us to deploy software to our customers. And the, the regions and the business unit's job is to build products that respond to consumer needs. And there is sort of functional needs, depending on the use case. That's the business unit. And there is regional needs that's based on the context that I just talked to that can vary by country. Yeah, working good enough, but you know, we're. Evolving, you know, we're growing as a company, which is, which is a nice thing to do. And, and I would say, you know, I, I've worked at other large corporations, as you, as you know,

• 27:56Speaker 05

  the pendulum swings. So, you know, sometimes do a little more centrally, sometimes you do a little more locally and you always reevaluate and see what's working. In the AI world, I would tell you, doing more centrally is probably a good idea. Because like I said earlier, I think about AI as a platform capability, not a feature. And therefore you have to have that capability everywhere. Uh, and you have to allow reuse of models. And you have to govern it very carefully. And I think doing that once rather than, you know, we're in 23 countries, 23 times is, is a good idea.

• 28:35Speaker 04

  It does seem that whenever there's a technology shift, the push towards centralization appears, right? That's, we need to get a hold of this, we need to understand how to use it, then we can, we can spread it back out to the divisions.

• 28:45Speaker 05

  Yeah, yeah.

• 28:46Speaker 04

  I'm just curious, you described yourself as a provider of backend solutions, that's your, that's your job. Your title is CEO. Do you think yourself as the CEO of an, an infrastructure provider inside of Experian? Are you a vendor to the other divisions?

• 28:57Speaker 05

  Well, I think, I think about it like this way. So my, my title is CEO for Experian Software and Technology. The software stands for all the software we sell to our clients. So on that side, I am more, you know, I'm in charge of, okay, what does the product look like? Is it evolving the way it is? Do we have competitive advantage versus everybody who competes with us? And the product needs to be, uh, the best. Uh, certainly we're trying to be always be the first, so the most innovative first, best, and in some cases only product that can do what our products do. And that's, that's, uh, how we make money. That's how we grow those businesses. It's a, it's a typical market, uh, going role. The other part of my title, technology, that stands for our technology infrastructure.

• 29:38Speaker 05

  That's a little bit what we have talked about so far. So that's empowering, uh, all the business units with all the services that they need. Yeah. And we do have, have platform builds, you know, the way, the way I think about it is, we want to apply data analytics. And AI into the hands of all of the business units that build their product. So the question is, what can I build centrally that enables them to do that faster? So that we can stay innovative, they can stay innovative. And so you have, you know, shared data foundations and shared backend services. You have modular services that people can use.

• 30:19Speaker 05

  And then you have AI models that, that, you know, could also be reused if they access the same type of information. You know, typically that's appropriate when it's deep personalized information, not personalized information. And that saves us then from building, if you put the three together, so the shared data foundation, backend services, modular services and the AI models. You then don't have to build one-off apps anymore, but you can reuse a lot and focus on the feature functionality that's specific to that industry, to that vertical or to that country.

• 30:53Speaker 04

  One more question here, then I want to ask the other decoder question. You mentioned the divisions making products. Do they have their own engineers, designers, or is that all in your group?

• 31:03Speaker 05

  No, that's so 4,000 plus 7,000 equals 11,000, up to 23,000 employees that we have at Experian. 11,000 work in technology organizations. 4,000 work in the central group that's sort of mine and the other 7,000 work in the business units.

• 31:17Speaker 04

  And so is, how do you align those roadmaps? You can very quickly see how you might have one division working on one product that another division is also working on. And that is redundancy you might not need, or you might decide, actually, they need to be more different than similar. How do you align that?

• 31:32Speaker 05

  I mean, that's the work every day. Neel, that's, you know, it's not always easy. You know, people think, oh, my division can build it better or faster or different, and therefore we should. You know, so we communicate. We have a, we call it Technology Executive Board, which I run, I'm the chair of that. All the CTOs sit on that and we disclose roadmaps. We talk about standards and make sure that if once we have a standard defined, there is no rebuilding, then it's all about reuse. So that's our governance model in order to coordinate everybody, the Technology Executive Board.

• 32:07Speaker 04

  Tell me about that meeting, just take me inside that room. Very, very few people will ever be in that room, right? Who makes the agenda? Is it you? How does that work?

• 32:15Speaker 05

  I have a right-hand person. So we have a group CTO, Rodrigo, and he works with the CTOs to say, "What do you think we should talk about?" And then he makes a decision on what's on the agenda. It gets to me, call it a week before the meeting, I say, "Yeah, I like it," or "I don't want to talk about this, "I want to talk about that," goes back out and then it's sent to them so that everybody can prepare. Everybody dials in. It's a worldwide meeting, complicated, that makes early mornings for me. Because I sit here in Colorado and just because time differences go both ways. We try to do this in the early morning hours for California, 6:00 AM, 7:00 AM my time.

• 32:55Speaker 05

  And then everybody dials in. Altogether, I think we have 20 people dialing in. There are 10 CTOs and CIOs dialing in. And then there's our CISO is on that meeting, our risk officer is on that meeting.

• 33:14Speaker 05

  We have some people who drive specific topics. So for example, the person who drives our AI initiatives and coordinates it across the company, etc., etc. When we did the cloud migration, we were at the tail end of that. There was a person on who was responsible for the cloud migration. So they're all high-level people. I'm going to call it an expensive, expensive meeting with real decision-makers. Meeting lasts about three hours. And we have it monthly.

• 33:45Speaker 04

  Tell me what the spiciest thing that you had to make a decision on was in that meeting?

• 33:53Speaker 05

  Well, there are so many. Spicy is when it comes to enforcing a standard where people need to maybe decommission a tool that they love, decommission a tool that their developers love, decommission a tool that's embedded in all the customers. And then adopting the standard means a migration at a minimum for our internal technology teams and maybe even for the clients. Because it becomes an effort that takes time, it becomes an effort that costs money. It becomes an effort that clients don't like. And therefore, making such a decision is long-contemplated and requires detailed plans because you don't only need to think about, "Well, is it the right standard or not?"

• 34:36Speaker 05

  But what are the consequences, the secondary and tertiary consequences of the decision? That gets spicy and we're not on an autocratic organization. So we err on the side of letting everybody speak their piece and hearing everybody out. And, and if that takes several meetings, then we let that happen. Uh, but at the end, we, we all align and even those who, you know, would have preferred a different decision than uh, rather in the same direction. Uh, that, those are the spiciest, the spiciest of all decisions. And there are many examples.

• 35:09Speaker 04

  In the end, it's always migrations. It's never anything but migrations. It's lurking in the background of every company. Uh, this is the other question I ask everybody who comes on Decoder. You're describing the kinds of decisions you make and the, the manner in which you make them. How do you make decisions? What's your framework?

• 35:25Speaker 05

  You know, I, I think God has given us two years and one mouth because we should listen twice as much as we talk. So as a leader, what you need to do is you need to hire world-class teams and people who are better at what they do than you are, than you are. And then you need to let them do their work and you need to let them speak. And at the end of the day, uh, you know, I, I try to surround myself with people who can scrutinize what people have in their brains and what's being shared. And if they come to a consensus, I usually go with the consensus. You can probably count on a couple of fingers how often in a year I will go against what the, those group of CTOs would want to do.

• 36:04Speaker 05

  Uh, and if, if that happens, it is usually because I refer to a principle that they did not take into account. Um, and, and I try to be a principle-based leader, you know, uh, I have a clear hierarchy of, of how I make decisions. I talked earlier about privacy, consent, security. That's at the top of my list and it's not always the most economic decision. And therefore, you know, my CTOs might suggest something that makes more sense from an economic perspective, but maybe isn't as tight from a security perspective. And then I, I veto it and I say, well, we're going to pay the extra money and we're going to do it anyway. Um, but it, it happens very, very rarely because people know the principles that we, that we work by.

• 36:48Speaker 05

  So if you have clear principles, you listen to people, you surround yourself with strong people. Uh, you make the room for a debate that is open, transparent. Very inclusive, everybody can speak, there is no hierarchy in the room. You take your time for it and then you make the best call you can with the information available.

• 37:07Speaker 04

  Let's put this into practice. Let's talk about how AI might be changing your business and what you're doing. The foundation here is that even the idea of the credit score is relatively recent, right? This is a creation of basically the late 1980s. And a lot of people can have a lot of feelings about their credit scores. And I would say, you know, Experian, TransUnion, Equifax, you can have a lot of feelings about whether or not those companies are responsive to you if you have feelings about your credit score and where they come from. In a world of AI, you have vastly more opportunity to make something richer, right? In the data, because you can query it differently. You have vastly more opportunity to collect information because you can ingest more unstructured information and provide predictions. And then you have vastly more risk, right?

• 37:52Speaker 04

  Because the models might hallucinate the data or they might reflect some underlying bias in the data set as a whole. Or you might have huge security problems as we build out how the AI models might talk to each other in databases. How do you evaluate all of that risk and still be trusted as Experian? Because that seems like an awful amount of new risk as the technology shifts.

• 38:16Speaker 05

  Yeah, Mila, a great question and really perfectly articulated. Let me give you two answers to that. One is just explaining how we think about the credit score. You called it's a relative recent from the 80s. So if it's okay, I'm going to provide a different perspective to that. And then I'm going to talk about, you know, just how we apply AI. Let me start first with the history of our company. We have a guy in our history, his name was C. Romo, an Indian immigrant into the United Kingdom. And he ran a large merchant store, so kind of sold everything.

• 38:55Speaker 05

  Um, uh, uh, between sort of Nottingham and Birmingham there in the Midlands in England. And, and he had a big heart. So one of the things that he did is when there were people who he knew well, uh, he did give them drugs, pharmaceuticals, uh, on loan. So they, they came and said, look, I'm sick. I have this. I can't pay for it. Can I just have the medicine so I can get better and I'll pay you in the future? And, and he so trusted and did that. And then, you know, his immediate relatives, people he knew well, told other people, hey, you know, see your mom or does this? And then people started coming who he knew less well. And he said, well, who are you? And he said, well, I, you know, know your brother or your employer or this or that person.

• 39:37Speaker 05

  And he, and he expanded it and, and soon, uh, fast forward a bit, there was a line outside of his general merchandising force with people who he didn't know anymore. People coming to him because he had a big heart, he gave away pharmaceuticals, drugs without any securitization. Uh, and he was a smart man. And so he started writing down on paper, what are the kind of attributes of those people who I gave drugs to, pharmaceuticals to, um, that paid me back and who didn't? And that to me, to us, was the beginning of credit scores. He just looked at how did people behave and what did people have in common who were good loan risk, because he gave away the pharmaceuticals without having money in his hand,

• 40:20Speaker 05

  and who were a bad, uh, uh, lending risk? That is part of how, how our company started and, and, um, that's still how we practice our business. You know, if, if you understand how people behave, you don't have to know their age, their gender, their ethnic background, their sexual preference, all the stuff that's written down in law anyway. We should all think about that and our business should work like that. And there's plenty of regulation that stipulates that it is. Well, that's our very heritage. You look at people's behavior. So what we do with the data, usually the data is depersonalized. Because what I just described.

• 41:03Speaker 05

  You can do without knowing it's Neelai, it's Alex. You don't have to know, you live in New York, I live in Colorado. You don't have to know your background, my background. You just look how we behave. So it's depersonalized data on which all those services are provided. But then let me move to the second part of the question, which was about AI. And you implied in how you asked your question that there is access to that data. So let me first say, our data is not accessible by any public AI or Gen AI models. And we currently don't see a way that we're gonna go there. What we use AI for primarily is to make sure that governance is done correctly.

• 41:46Speaker 05

  Explainability is provided and human oversight is better than it was before. Let me give you an example. The way that financial services are creating their products is basically through a model. The model says, I have this loan product. And I think the acceptable risk is this type of person that behaves in the following way. Our data feeds that. So it can be the credit score, it can be where you reside, if it's a local or a regional bank. It can be your lending history, you know, do you have the capacity to take on another car loan? It can be your income, has it increased over time?

• 42:31Speaker 05

  And therefore, is it projected to continue to increase, et cetera, et cetera. So there's a whole bunch of data that goes into those models. None of which need to know whether it's Neelai or Alex or, you know, who specifically we are. It's all about, you know, do we fit that model? The lenders need to file what those models look like and how the models are supposed to behave, meaning what kind of person qualifies? How many loans do they think they have? What would the loan losses be with the regulator? So they do that, they develop the model, the lending product goes out. People start applying, the bank starts paying out the loans.

• 43:12Speaker 05

  And then loan losses start coming in, people start missing payments. So that's the model behavior, you know, because there's a prediction of how much of that will be, will there be? If those variables come off, the industry term for that is it's a model drift.

• 43:30Speaker 05

  So maybe the loan losses are higher. Maybe we're not getting as many people of that age groups. Maybe late payments are more than we thought. All those those kind of metrics. It's called model drift. If it comes off, we use artificial intelligence. When those models drift to prompt the person who has created the model or the oversight department in the financial institution. There is model drift. Not only do we tell them that there is model drift. We also tell them what variables in their model are the reason for the drift. You're missing a data element. You set it too low. You set it too high. You need to open your funnel to people with lower credit scores.

• 44:14Speaker 05

  And then we allow them to adjust the model so that it behaves the way that they had filed it with the regulator. So what I'm trying to tell you through that. It's not that we use AI to access all of the, you know, personal information of people. We use AI to look at outcomes, derive the data and interpret that and then make it available to humans so that they can use it in the way that it needs to be done in the example. You know, so the human oversight of model performance. By the way, that happens today, but it happens with sleuths of people, not automated, not real time, not as accurate as AI can do. And so we think there's a real improvement of the process there

• 44:56Speaker 05

  because it makes lending fairer, more accurate. It allows the lending products to behave the way that the regulator intends them to behave. And therefore it's AI for good, just like we try to make data available for good. And that's important for people to understand. A data company like ours, I, like I said, currently I cannot see that we make our data accessible to any public AI provider and therefore let them build their large language model based on our data. By the way, the large language models are much better at text than they are at math.

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• 47:09Speaker 04

  We're back with experience Alex Lintner. Before the break, Alex gave us a very long, detailed answer as to how he thinks about the inherent risk in AI systems. But now I wanted to push on the gap between what we want these systems to do and what they can actually accomplish today.

• 47:25Speaker 04

  You're describing a lot of math. My experience with every LLM is they're pretty bad at math. Are you using LLMs? When you say AI here, are you using a different kind of AI?

• 47:36Speaker 05

  No, LLMs. Yeah, we have, we've built our own large language model. We've built SLM, small language model for smaller tasks. We have about 200 agents built into our products already now. So, you know, there are different ways in which we use AI. But yeah, we built an LLM.

• 47:54Speaker 04

  But but when you're calculating model drift, that's an LLM doing it or what kind of technologies doing it?

• 47:59Speaker 05

  Yeah, it's I would be an SM and small language model. Because basically what the model does, it it it, you know, reports out what's happening and it just one number is smaller than the other. That's not math. It doesn't do the calculation. It just recognizes it.

• 48:15Speaker 04

  It's right. I think I have definitely. We wrote an entire story about how chat G P D can't tell time. Sometimes one number is bigger than the other. It's actually quite difficult for these models. Or like increments are actually quite difficult for these models. You think that's trustworthy? I'm asking you very directly because the problems of hallucination here compound, right? They get exponentially worse as you add more and more AI tools to the system. The problems of reflecting biases in the data get exponentially worse as you add scale, as we've talked about. How are you making sure the AI systems aren't either hallucinating or reflecting an underlying bias that you can't see?

• 48:52Speaker 05

  Human oversight through data scientists. You know, you you I think we're too early in the journey that we can let it run on its own. I think we need to all practice responsible use for a data company. It means we lean on some of the strongest human assets that we have and as our data scientists. They need to look at the output and they need to look whether it's accurate or not. And if if it's not accurate, you know, we turn it off and we fix it. Or if it's not fixable, we would throw it away.

• 49:20Speaker 04

  Have you run into that?

• 49:21Speaker 05

  By the way, but we would do that.

• 49:24Speaker 04

  Wait, yeah, that was my next question. Have you run into this situation yet where the data scientists have said we can't use this tool yet?

• 49:30Speaker 05

  Oh, yeah, because we test everything because before we put it in production. So it happens all the time. Like, nothing goes into production without going through that kind of process. We have synthetic data and we have depersonalized data that we use for testing new models, new agents. And we don't put anything into production until we know it works.

• 49:50Speaker 04

  That makes sense to me. What's been the biggest gap between a capability you want an AI system to have and the one that you tested? So I'll give you this example. I think Siri and Alexa and Google Assistant, right? Everyone knows what they want them to do. And then I'm watching all of these companies try to add AI into the mix with their voice assistants and they're not there, but they just can't quite do it. And, you know, Apple had to start over and Google is pushing out in stages and however that's working, it's working. What's been your experience of, okay, we're going to ship an AI tool and we want it to work this way, but it's not quite good enough?

• 50:25Speaker 05

  I think it has to do with the interaction of AI to the human. So the way I look at AI, and I think a lot of people do, is it's a digital teammate or a digital workforce. So if it is that, you know, then that teammate or that team would perform a certain task and it would contribute it to the work of the overall team. So we assume, hey, if we provide the following information to a team, to a person as an assistant in their workflow, they're going to use it that way and therefore it's a good thing. Well, we're not always right.

• 51:03Speaker 05

  You know, people don't, I sometimes compare it with, you know, I drive a Mercedes car and I can talk to the car and, you know, it has a map where that I can talk to and say, hey, you know, Mercedes, take me to, you know, tonight I'm seeing the Colorado Avalanche play hockey, take me to, take me to Ball Arena in Denver. And so it will, you know, put in the directions from where I'm at and I will be taken there. I got so used to that tool that I now listen to the tool all the time. Though I know the area really well, and sometimes it doesn't give me the right route.

• 51:48Speaker 04

  Is that, is that good?

• 51:49Speaker 05

  No, it's not, right?

• 51:51Speaker 04

  A good outcome?

• 51:51Speaker 05

  No, it's not a good, it's not a good outcome, and that is the outcome you want to avoid. It's the answer to your question. If you get so, if you trust AI to the, to the point where you blindly trust it and always follow it, and you don't check yourself through the data scientist in the example that, that we discussed a couple of minutes ago, it, it bears risk. So the real job that we have is to make sure that doesn't happen. And the interaction with the human still happens, you can sort of force it in rather than it automatically, AI automatically doing what it, what it does.

• 52:27Speaker 04

  We've had Ola from Mercedes on the show, and I think he'll be happy to know that you're the single customer of the Hey Mercedes voice assistant in his cars. I've been dying to know who else is using this thing.

• 52:36Speaker 05

  I, I, in my car, I can turn on the lights, I can turn on the radio, I can switch radio station, I can turn on my, my, my C T, I'm, I'm just, I, you tell him, I like it.

• 52:47Speaker 04

  The next time he's on, we'll be like, we found one. Let me, let me ask you, this is going to be the hardest question. When I hear from our readers, when I hear from, from people about what AI might do, the idea that a company like Experian can make decisions that affect their lives using AI is terrifying. It, it, there's not a lot of hope when, when people think about this outcome, that there's an all-knowing AI that can generate scores about you based on your behavior and allow other people to make decisions. And we kind of see this in countries like China, right? Where there's, there are reputation scores, there are other kinds of centralized data providers that very directly affect people's lives. You're in the position to do that. So I'm going to ask this question in two parts.

• 53:30Speaker 04

  First, do you think people like Experian today? Do you think you have the foundation to build this next generation of products?

• 53:37Speaker 05

  A couple of words. First of all, we're not Palantir. So we don't do reputation scores, yeah? We are very much in, like I said, our financial services, healthcare, automotive and digital marketing. So that's where we play. And I think I answered that question earlier. Why is it in the interest of people that their data gets used? It's so that they get access to credit, access to healthcare, so that they know the vehicle history of the car they're going to purchase, et cetera, et cetera. So we try to use data for good. We do not make decisions. So you used this phrase, do you think people are comfortable that Experian can make decisions? We don't do that. We provide information.

• 54:18Speaker 04

  You provide the tools that allow others to make decisions.

• 54:20Speaker 05

  That's right.

• 54:20Speaker 04

  Sure, I understand.

• 54:21Speaker 05

  To lenders, yes, yes, yes. They will make a decision anyway, wouldn't they? Like I told you that the story about C. Ramo, who, you know, is long gone, you know, he made decisions. People will make decisions about you and about whether they lend to you. And the more you have to do that at scale in North America, we have 247 million Americans. If you want the economy to blossom, if you want people to have access to credit, you need a scalable model. I'm not saying that our system's perfect. But you can draw a worldwide comparison and you still have to say it is the best credit economy in the world. It really is. And there's lots of stochastic data around it.

• 55:06Speaker 05

  We are part of that connected ecosystem. We're not all of it. We are part of that and we try to perform our role within that connected ecosystem responsibly and the best we can. If somebody has an idea on how to make it better, we'll be first in line.

• 55:22Speaker 04

  Sure, but let me, let me just try again. The answer to the question, do you think people like Experian is not, we're not Palantir? That's a very low floor in a very specific way. You've talked a lot about trust. I'm saying right now, the way individual Americans encounter the brand of Experian is not always positive, right? And in many cases, it's a faceless entity that controls a downstream decision, yes, a financial institution's making. But the recourse is low, right? This is the trade off we've been talking about with scale this whole time. AI might allow you to change the amount of recourse people have. It also might allow, I don't know, a bunch of bad guys to launch ever more sophisticated attacks and get that data out. Like, there's more trade offs here than not.

• 56:07Speaker 04

  So I'm just asking about the foundation of trust that you're working with to begin with. Do you think enough people like and trust Experian for you to build this next set of capabilities, which might make you even more powerful?

• 56:18Speaker 05

  I think enough people do. Let me maybe answer the question not with one sentence, but be a little more granular. I could point to data of the consumers who give us their data. So we have a direct to consumer business. And in the various countries that we are active with our direct to consumer business, we have hundreds of millions of consumers who proactively make their data available to us. We protect their identity. We do everything that I described earlier. We give them access to comparing financial products so they can lower their cost of borrowing. We give them access to lower cost car insurance, et cetera. And those consumers like us. And I know that because we ask them.

• 57:00Speaker 05

  And we get a net promoter score and we look at that, you know, religiously every month to see how are we doing? Are we doing right by all these people, et cetera, et cetera? Now, there is another population that may not have that relationship with us that have, through life's circumstance, have a bad credit score. And those people, you know, sometimes don't like us. And I'm gonna make it really personal. Neelai Alex was one of them. I'm an immigrant. I came here just about exactly 30 years ago. And when you're an immigrant, you don't have a credit score. You don't have access to credit. Life's really hard, really, really hard for us immigrants in the beginning years.

• 57:47Speaker 05

  And I wish there were a system that the law would allow to make life easier for people like us. But there isn't. And my life be- became difficult, you know, because I, you know, uh, wanted to stay here. And, uh, you know, I went to school here. That's initially how I came here. And then I, I wanted to stay here and, and get a job and all of that. And if you don't have credits, you know, you're riding public transportation to work, etc., etc. I mean, it's, you know, I had an hour and a half commute for, for years and years because I couldn't afford a car. Couldn't buy the car because I didn't have enough cash. It, it, life, life's hard. And, and in those situations, there are much worse stories than my, my personal stories.

• 58:30Speaker 05

  But I just want you to know, I've felt it before. What we try to do is we try to do away with people having low credit scores by giving them tools to improve their credit score. The way that the initial formula was written, it allowed for all recurring financial transactions to become part of the score. I don't want to pick on our, our competition, so I'll phrase it this way. We're the only ones who allow that. Credit bureaus, other credit bureaus, they, they only take lending history, so have you had a loan before into account? Well, there's other recurring financial payments. Your streaming service, your cellphone bill, etc., etc.

• 59:13Speaker 05

  There are so many payments that you make, your utility bills that you make every month. And if you make them reliably every month, that should be part of your score and therefore increase your score. We've created a, a system called Boost, Experian Boost, where people can upload that information, that credit score goes up. So they, they don't have to go through that period that I did because I did rent an apartment. I did pay all my utilities, etc., etc. And I wanted to have access to credit. So we tried to lower the hurdle and therefore have fewer of those people who are impacted by life circumstance. To me, it was the fact that I was an immigrant. So that we don't have people upset and therefore not liking Experian. I don't think they don't like Experian. They don't like what that score expresses at the time.

• 59:56Speaker 05

  And if we have issued it to whatever lender they talk to, then the finger gets pointed at us.

• 1:00:02Speaker 04

  Sure, I just think there's a feeling of helplessness that comes with that score sometimes, right? There's a feeling of lack of recourse, particularly if you feel that score is wrong, right? And that's where I think a lot of the-

• 1:00:13Speaker 05

  But that's why Boost, right? That's why-

• 1:00:15Speaker 04

  But Boost is like an interesting set of incentives, right? For you, it's a product you sell. It might help some underbanked or low credit people immediately.

• 1:00:23Speaker 05

  Don't sell it. Free.

• 1:00:27Speaker 05

  We don't sell it. We provide it by free because it's the right thing to do. It's free for the consumer. It's free for the bank. Not a-

• 1:00:34Speaker 04

  I didn't realize it was also free for the bank. I assumed the bank, that there was- There's no economic incentives for Boost at all?

• 1:00:40Speaker 05

  No, no, no. It's the right thing to do. Because what you're pushing on, Neela, is you are expressing in your own words what kind of company we are. I would probably express it differently, but directionally, you're describing it right. When you're in that business, you need to have a really clear ethical compass on how you conduct business. We have that at Experian. Boost is an expression of that. Let's help the consumer get it right. Let's help the consumer fix their score if the score is wrong. It's not okay if the score is wrong because it makes life really difficult. And therefore, we have provided the mechanism to do that. By the way, for that, you need a real-time bureau. We're the only real-time bureau in the world. Nobody else is real-time. It delays 30 days.

• 1:01:23Speaker 05

  So if they had a functionality like that, our competition, you put in the information, 30 days later, you get your score updated. It's useless. We built it real-time. You put your data, it changes right then. And you can go back in the door, you know, not that people still go to the branches, but back in the door, talk to the lending office and say, hey, take a look at my score. It's not what it was 10 minutes ago.

• 1:01:45Speaker 04

  I'm curious about that because again, the trade-offs is you attract more scale, as you provide more products, as you use AI to build even more scale. Down at the bottom, right, the individual consumer, the thing I'm pushing on is will they feel more recourse or more control or less, right? And over time, I would say increasing centralization and scale in the economy has led to it feeling less empowered. Actually, I'll slightly change the subject here because I want to end on security. You have a lot of data. I know you're moving a bunch of your data to AWS, you're moving to the cloud that will help you with security in some ways, will help you with AI in other ways. Sometimes the only way people hear about companies like Experian is because of data breaches, right? Your competitor had a massive data breach recently at Equifax.

• 1:02:30Speaker 04

  How do you think about that, right? As we collect more data, we're a much richer target and then the bad guys are going to use AI to launch automated attacks. We've seen the studies from the frontier labs already saying this is going to start happening.

• 1:02:43Speaker 05

  100%.

• 1:02:44Speaker 04

  That's another place where the consumer basically has to trust you, right? Like, that's just how it's going to be. How do you think about the cost of mitigating against the increased attack service of your scale, the increased capability of the attackers, and all of the products that you want to provide to people?

• 1:03:00Speaker 05

  It's the first dollar we should spend. If we don't do that well, we don't have a reason for existing because a bad actor will go in. You know, just to say it for a second, I've been here 10 years. The last time we had a breach occurred, went two weeks into my tenure at Experian, so 10 years ago. We are now, we are in a business where we actually protect the identity of people whose identity was stolen because we have access to the dark web, we know how to clean it up. So when Equifax had their breach, they paid us to protect the consumers whose information was stolen. So, you know, I'm not saying

• 1:03:41Speaker 05

  we're perfect at it, but we're pretty darn good at it. So good that even our competitors give us that business. It's job number one, Neelai. There is no, there is no two ways about it. That is the biggest risk. In this sector, that is the biggest risk for anybody who has a business similar to us. It's the biggest risk for us and therefore it's the first dollar we're going to spend.

• 1:04:06Speaker 04

  When you say first dollar we're going to spend, do you think about that in terms of return on the investment specifically? Or just this is the enabling cost of all of the other investments we're going to make?

• 1:04:15Speaker 05

  This is the enabling cost of all the other investments we're going to make. So I'm going to buy all the tooling. I'm going to hire all the people that we need to keep us safe. And we're going to deploy the technologies that do that the best. And we're going to try to stay ahead of the bad actors who do deploy AI, who do, you know, now, as you, as you, as you said, actors use bots to get in. We bought a company called NeuroID, which, you know, detects bots at a much better way than anything else that we have seen. And, you know, banks are eating it up. So there's an economic incentive, by the way, to do that well, because it's a service we provide.

• 1:04:53Speaker 05

  And we got to stay on it.

• 1:04:55Speaker 04

  Experian is a public company. Obviously, there's some amount of pressure to deliver increasing profits. Enabling costs, especially big enabling costs, can come under pressure. Is that just you who has to defend it? Is it the ethos of the company? How does that work?

• 1:05:10Speaker 05

  It's the ethos of the company for sure.

• 1:05:12Speaker 04

  So if you show up and say, I need to double the cost of security, that's, it's just going to be fine. I hear from our listeners who have similar situations at you that the incremental cost of security sometimes is hard to defend.

• 1:05:24Speaker 05

  So not at Experian. I don't know who you're referring to, but, but not at Experian. And I will tell you this. So the good thing about the business model that we have, it's a scale model. We talked about scale a lot and you sort of talked about the risk of scale. But the benefit of scale is as you scale, there are some costs that are fixed, that are then distributed over a greater amount of business. And therefore you actually have natural scale benefits, meaning your, your, your fixed costs.

• 1:05:53Speaker 05

  Are a larger part of your total costs, the variable costs are a lot of part of your variable costs. So when it comes to security, what does that mean? That means if, you know, today we have 200 million consumers that give us their information and tomorrow we have 300 million. There is not a 50% 300 million, 50% bigger than 200 million. There's not a 50% increase of security costs, even if I buy the leading edge technology. And therefore, our scale, I think, actually allows us to buy all the best tools, hire all the best brains in the industry to defend against bad actors.

• 1:06:33Speaker 04

  Let me wrap up by just trying to tie all this together. I've talked a lot about the individual consumer. That's a lot of our audience, people who build things, people who think about the kinds of products AI might help them build, the kinds of scale that you might operate at. Some people who just want the kind of scale that you might operate at, right? That's the ambition. As you see us go into this next era, where there is more legibility of data, that's what I would call it, right? That's really what the AI that you're describing will provide to financial institutions. How do you make sure that experience actually empowers consumers? Not just be in access to credit, which is what you've come back to over and over again, but increases the feeling that our agency as individuals in the economy is going up instead of down? Because I would say right

• 1:07:16Speaker 04

  now, a lot of people feel like their agency in the economy is actually going down.

• 1:07:21Speaker 05

  Unfortunately, and I don't want to make any political statements, but that is, unfortunately, I would say you're correct with that. We try to have our own compass of what's right and what's wrong. And we try to empower consumers. So opting out needs to be easy. Opting back in needs to be easy. You know, we have several ways of doing that. I was gonna call it stages, or more severe, a credit freeze, and then it's. Harder to than undo, create lock, easier to do and undo, you know, depending on what happened to you, identity theft or not, or just a precaution, or just because you don't like it. So we allow you to lock your data away and we should make that easy.

• 1:08:06Speaker 05

  We should make that easy in whichever way you want to contact us, whether you want to do it online, which, you know, is economically better for us. It costs us less per interaction with the consumer, or whether you want to call us, you know, we have call center with thousands of people. It is a US-based call center. You know, a lot of people complain about, oh, I talked to a person in country accent and accent I couldn't understand. And we don't do anything like that because we want to do right by the consumer. We are, even in our B2B business, really it's a B2B to C business. Because at the end, we affect our consumer, which is what you keep emphasizing. And we are very conscious of that responsibility and try to show it and how we continue to evolve our services.

• 1:08:49Speaker 04

  Alex, this has been great. Thank you for being so candid. Thank you for being on Decoder. We'll have to have you back soon.

• 1:08:54Speaker 05

  Neelai, thank you so much for the invite. It's good to talk to you.

• 1:08:58Speaker 04

  And I thank Alex Lintner for taking the time to speak with me. And thank you for listening to Decoder. I hope you enjoyed it. If you'd like to let us know what you thought about this episode, or really anything else, drop us a line. You can email us at [email protected]. We really do read all the emails. You can all see me up directly on Threads or Blue Sky. And we're also on YouTube. You can watch full episodes at DecoderPod. We also have a TikTok and Instagram. They're also at DecoderPod on those platforms. A lot of fun. If you like Decoder, please share it with your friends. Subscribe wherever you get your podcasts. Decoder is a production of The Verge and part of the Vox Media Podcast Network. The show is produced by Kate Cox and Nick Statt. It's edited by Ursa Wright. Our editorial director is Kevin McShane. The Decoder music is by Breakmaster Cylinder. We'll see you next time.

• 1:10:07Speaker 07

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