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

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26:52We're back with Alex Lintner, 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 workshop with Alex and see what it could tell us about what kind of company Experian really is. How many people are Experian overall? 23 ,000. And how many are in your division? I have a direct reporting line of 4 ,000. We have 11 ,000 technologies. So think of my function, 4 ,000 on my direct reporting line. They roll up to me.

27:27Okay, I was going to say 4 ,000 direct reports is a little over the guidelines, I think. And that's in my direct reporting line. So all the way down. Yeah, no, I was joking. I got 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. And is that structured so it meets the needs of the regions or how does that work? Yeah, we're trying to walk that fine line. Exactly like I explained, you know, my job is build a backend that is superb.

28:00make our platforms the most secure and least expensive way for us to deploy software to our customers. And the regions and the business units 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 about that can vary by country. Yeah, working good enough, but, you know, we're evolving. We're growing as a company, which is a nice thing to do. And I would say, I've worked at other large corporations. As you know, the pendulum swings.

28:39So 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. 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 a good idea. It does seem that whenever there's a technology shift, the push towards centralization appears, right?

29:22We need to get a hold of this. We need to understand how to use it. Then we can spread it back out to the divisions. I'm just curious. You described yourself as a provider of back-end solutions. That's your job. Your title is CEO. Do you think of yourself as the CEO of an infrastructure provider inside of Experian? Are you a vendor to the other divisions? Well, think about it like this way. My title is CEO for Experian Software and Technology. The software stands for all the software we sell to our clients. On that side, I am more in charge of, okay, what does the product look like? Is it evolving the way it is?

29:53Do we have competitive advantage versus everybody who competes with us? And the product needs to be the best. Certainly, we try to 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 how we make money. That's how we grow those businesses. It's a typical market-going role. The other part of my title, technology, that stands for our technology infrastructure. That's a little bit what we have talked about so far. So that's empowering all the business units with all the services that they need. Yeah. And we do have platform builds.

30:30You know, 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 shared data foundations and shared backend services. You have modular services that people can use. And then you have AI models that can also be reused if they access the same type of information. You know, typically that's appropriate when it's depersonalized information, not personalized information.

31:15And 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. One more question here, and 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? No, that's the 4 ,000 plus 7 ,000 equals 11 ,000. Of the 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.

31:58And so 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? I mean, that's the work every day, Nilay. It's not always easy. People think, oh, my division can build it better or faster or different, and therefore we should. So we communicate. 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.

32:37We 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. 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? 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.

33:12I say, yeah, I like it or I don't want to talk about this. I want to talk about that. It 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 at 6 a.m., 7 a.m. my time, 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.

33:56We have some people who drive specific topics. So for example, the person who drives our AI initiatives and coordinates it across the company, et cetera, et cetera. 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 meeting with real decision makers. Meeting lasts about three hours. and we have it monthly. Tell me what the spiciest thing that you had to make a decision on was in that meeting. 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 of migration at a minimum for our internal technology teams and maybe even for the clients.

34:57Because 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? But what are the consequences, the secondary and tertiary consequences of the decision? That gets spicy. And we're not an autocratic organization. So we err on the side of letting everybody speak their piece and hearing everybody out. And if that takes several meetings, then we let that happen. But at the end, we all align.

35:40And even those who would have preferred a different decision, then rather in the same direction. those are the spiciest of all decisions. In the end, it's always migrations. It's never anything but migrations. Migrations are tricky. It's working in the background of every company. This is the other question I ask everybody who comes on Decoder. You're describing the kinds of decisions you make and the manner in which you make them. How do you make decisions? What's your framework? I think God has given us two ears 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.

36:21And then you need to let them do their work and you need to let them speak. And at the end of the day, 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 those group of CTOs would want to do. And if that happens, it is usually because I refer to a principle that they did not take into account. And I try to be a principle-based leader. You know, I have a clear hierarchy of how I make decisions.

37:03I 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 veto it and I say, well, we're going to pay the extra money and we're going to do it anyway. But it happens very, very rarely because people know the principles that we work by. So if you have clear principles, you listen to people, you surround yourself with strong people, you make the room for a debate that is open, transparent.

37:41Very 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. 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.

38:14In a world of AI, you have vastly more opportunity to make something richer 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, 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?

38:52Because that seems like an awful amount of new risk as the technology shifts. Yeah, Nila, 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 a relatively 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 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. Rommel, an Indian immigrant into the United Kingdom, and he ran a large merchant store. So he kind of sold everything between sort of Nottingham and Birmingham there in the Middle East and England.

39:42And he had a big heart. So one of the things that he did is when there were people who he knew well, he did give them drugs, pharmaceuticals on loan. So 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 he sort of trusted and did that. And then, you know, his immediate relatives, people he knew well, told other people, hey, you know, C.R. Amo 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.

40:19And he expanded it. And soon, 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. 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, 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 and who were a bad lending risk.

41:05That is part of how our company started, and that's still how we practice our business. 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, you can do without knowing it's Nilay, it's Alex.

41:48You 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 going to go there. What we use AI for primarily is to make sure that governance is done correctly.

42:28Explainability 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 regional bank. It can be your lending history. Do you have the capacity to take on another car loan? It can be your income. Has it increased over time? And therefore, is it projected to continue to increase, etc., etc.?

43:16So there's a whole bunch of data that goes into those models, none of which need to know whether it's Nilay or Alex or who specifically we are. It's all about, you know, do we fit that model?

43:32The 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. 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 there be. If those variables come off, the industry term for that is it's model drift.

44:12So 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 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.

44:56And 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 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 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 slews 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 because it makes lending fairer, more accurate.

45:44It 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, 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. We need to take another quick break. We'll be right back.

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49:18We're back with experience Alex Lindner. 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. 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? No, LLMs. We've built our own large language model. We've built SLMs, small language model, for smaller tasks. We have about 200 agents built into our products already now.

49:57So there are different ways in which we use AI, but we built an LLM. But when you're calculating model drift, that's an LLM doing it? Or what kind of technology is doing it? That would be a small language model. Because basically what the model does, it reports out what's happening. And just one number is smaller than the other. That's not math. It doesn't do the calculation. It just recognizes it. It's right. I think I've definitely, we wrote an entire story about how ChatGPD 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.

50:37You 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? Human oversight through data scientists. 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.

51:15It means we lean on some of the strongest human assets that we have, and those are our data scientists. They need to look at the output, and they need to look whether it's accurate or not. And if it's not accurate, we turn it off and we fix it. Or if it's not fixable, we would throw it away. Have you run into this yet? By the way, but we would do that. Wait, yeah. That's my next question. Have you run into this situation yet where the data scientists have said we can't use this tool yet? Oh, yeah. Because we test everything before we put it in production. So it happens all the time. Like nothing goes into production without going through that kind of process.

51:47We 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. Nobody should, right? 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. Like they just can't quite do it.

52:23And, 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? 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, 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 are going to use it that way, and therefore it's a good thing.

53:11Well, we're not always right. 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 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. He'd take me to Ball Arena in Denver. And so it will put in the directions from where I'm at, and I will be taken there. I got so used to the 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. Is that good? No, it's not.

54:00Are you describing a good outcome? No, 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 point where you blindly trust it and always follow it, and you don't check yourself through the data scientist in the example that we discussed a couple minutes ago, 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 does. 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.

54:44Because I've been dying to know who else is using this thing. 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 seat heater. I mean, I'm just, you tell him I like it. The next time he's on, we'll be like, we found one. Let me ask you – this is going to be the hardest question. When I hear from our readers, when I hear 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. There's not a lot of hope 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.

55:26And we kind of see this in countries like China, right, where 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. First, do you think people like Experian today? Do you think you have the foundation to build this next generation of products? A couple of words. First of all, we're not volunteer. So we don't do reputation scores, yeah? We are very much in, like I said earlier, financial services, healthcare, automotive, and digital marketing. So that's where we play.

56:02And 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. You provide the tools that allow others to make decisions. That's right. Sure, I understand. To lenders, yes, yes, yes. They would make a decision anyway, wouldn't they?

56:35Like I told you the story about C. Ramo, who is long gone. 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 is 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 is lots of stochastic data around it. We are part of that connected ecosystem.

57:18We'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. Sure. But let me just try it 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? In many cases, it's a faceless entity that controls a downstream decision that, yes, a financial institution is making.

58:00But the recourse is low, right? This is the tradeoff 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 tradeoffs here than not. 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? I think enough people do. Let me maybe answer the question not with one sentence but be a little more granular.

58:35I 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. And we get a net promoter score.

59:11And 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 going to make it really personal. Nelai, 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.

59:57And 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 became difficult, you know, because I, you know, wanted to stay here. And, you know, I went to school here. That's initially how I came here. And then I wanted to stay here 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. I mean, it's, you know, I had an hour and a half commute for years and years because I couldn't afford a car, couldn't buy the car because I didn't have enough cash. life's hard. And in those situations, there are much worse stories than my personal stories, but I just want you to know I've felt it before.

1:00:42What 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 competition, so I'll phrase it this way. We're the only ones who allow that. Credit bureaus, other credit bureaus, 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 cell phone bill, et cetera, et cetera. There are so many payments that you make, your utility bills that you make every month.

1:01:26And if you make them reliably every month, That should be part of your score and therefore increase your score. We've created a system called Boost, Experian Boost, where people can upload that information and that credit score goes up. So they don't have to go through that period that I did because I did rent an apartment. I did pay all my utilities, et cetera, et cetera. 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.

1:02:02I don't think they don't like Experian. They don't like what that score expresses at the time. And if we have issued it to whatever lender they talk to, then the finger gets pointed at us. 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 - But that's why Boost, right? But that's why – 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.

1:02:33Don't sell it. Free. 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. I didn't realize it was also free for the bank. I assumed the bank. There's no economic incentives for Boost at all? No, no, no. It's the right thing to do. Because what you're pushing on, Nila, 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.

1:03:11We 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. Your delay is 30 days. 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.

1:03:42You put your data in, it changes right then. And you can go back in the door, 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. I'm curious about that because, again, the trade-offs as you attract more scale, as you provide more products, as you use AI to build even more scale, down at the bottom, the individual consumer, the thing I'm pushing on is, will they feel more recourse or more control or less? Over time, I would say increasing centralization and scale in the economy has led to feeling less empowered.

1:04:19Actually, 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, it'll help you with AI. In other ways, sometimes the only way people hear about companies like Experian is because of data breaches. Your competitor had a massive data breach recently at Equifax. How do you think about that? 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.

1:04:53100%. 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? That's the first dollar we should spend. And 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.

1:05:31We are now 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 we're perfect at it but we're pretty darn good at it so good that even our competitors give us that business it it's job number one Nilay 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 has a business similar to us, it's the biggest risk for us.

1:06:12And therefore, it's the first dollar we're going to spend. 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? 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 said, actors use bots to get in.

1:06:47We bought a company called NuraID, which, you know, detects bots in a much better way than anything else that we have seen. And, you know, banks are eating it up. There's an economic incentive, by the way, to do that well because it's a service we provide. And we've got to stay on it. experience 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 it's the ethos of the company well sure 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.

1:07:34So not at Experian. I don't know who you're referring to, 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 We have natural scale benefits, meaning your fixed costs are a larger part of your total cost. The variable costs are a load up part of your variable costs. So when it comes to security, what does that mean?

1:08:14That means if 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. 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 they might help them build, the kinds of scale that you might operate at.

1:08:56Some 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 Experian actually empowers consumers? Not just 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 now, a lot of people feel like their agency in the economy is actually going down.

1:09:31Unfortunately, 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 going to call it stages, so more severe, credit freeze, and then it's harder to undo, credit 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.

1:10:16We should make that easy in whichever way you want to contact us, whether you want to do it online, which is economically better for us. It costs us less per interaction with the consumer, or whether you want to call us. We have a call center with thousands of people. It is a US-based call center. A lot of people complain about, oh, I talked to a person in country X and in an accident, 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 B2B2C business. because at the end, we affect our consumer, which is what you keep emphasizing.

1:10:52And we are very conscious of that responsibility and try to show it in how we continue to evolve our services. 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. Nilay, thank you so much for the invite. It's good to talk to you. I'd like to 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 decoderatheverge.com. We really do read all the emails.

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