Welcome, everybody to the fireside discussion for FICO. We're super excited to have Will Lansing with us. You're somewhat fresh off the plane from Montana. That's right. we were just- Just now. We were just talking about, the fact that the company got added to the S&P 500 index effective or so, right at the 20th or something. Not a lot of S&P 500 companies headquartered in Montana. That's true. That's true. We might be the only one. I'm not sure. Yeah. Snowflake. Sure. Okay. There's 2 out of 500. Sure. Okay. That's good. They're in Bozeman also. Yeah. How did it end up there? Oh, it's a long story. Yeah. Part of it is I live there, so I'm that CEO who brought the headquarters to where I live. It's also. We had a great little office there. Yeah. It was good. Headcount is not concentrated there. We're really a super virtual company. Okay. We have 30 people out of 3,300 in Bozeman. Really, our people are scattered all over the world. Even before COVID, I would say, nearly half of our people, were home officed because we go and try to get the talent wherever it lives. We've never had a lot of pressure to be in the office. Right. return to office, not a big dynamic. I mean, there's one place. Our Bangalore office is about 1,200 people, and there's a culture there that's a lot of training that goes on there. That's one place where we really do have that kind of office culture. I'd say outside of that, we have offices scattered all over the world, but they're lightly used. Yeah. Okay. Which then creates some efficiency opportunities and... For sure. Yeah. We've taken out probably 20% or 30% of our real estate. Yeah. Cool. All right. Well, I think we're talking about this last night. In a sense, FICO was doing AI before AI was as hot as it is now. What is, what does AI mean in your context? You know, just take a step back. FICO has been an analytics company since the 1950s. We've kinda grown up trying to leverage every analytic technique available to make data-driven decisions. That's gone on, you know, 50s up to 70s, 80s, and then we kinda got into software. It used to be more of a consulting analytic assignment, and then we got into software. We've been doing neural nets, we've been doing AI for a long time. I think that the real question is, to what end? You know, what are you gonna use it for? This isn't like a hobby. This isn't a theoretical exercise. You have to have a point of view about what you're using it for. That's where we have, you know, our deep experience in taking data-driven decisions and getting them into workflows and getting them into that point of contact with the end consumer. That's where it really matters. When it comes to AI, we're very focused on applied AI, applied intelligence. Can you make a decision and then get it into a workflow so that it can be used? Then, a corollary to that is explainable AI. You know, we operate in a pretty heavily regulated sector, financial services, and it's not okay to just make a decision and not know how you got there. It's critical that you can explain to regulators so that you're in compliance with the law, with fair lending laws. It's critical to be able to explain how you got there. We, you know, we have this notion of ethical AI, explainable AI, and we're very focused on being able to explain how we got to the endpoint. Right. Explainable, responsible, ethical. Is there a difference from one jurisdiction to the other that's notable when government versus another? I wouldn't say so. I mean, I think Europe's a little bit ahead of us on, you know, kind of enumerated rights to the data, ahead of the U.S., I should say, on consumer rights to the data. We, you know, we're focused on all of that. Mm-hmm. Good relationship with the regulators 'cause I mean, a lot of the big tech companies have a fraught relationship. You know, for many, we're not a regulated entity like our customers are. Because the regulators rely on FICO Scores, because they wanna understand how the software works, you know, we've been very focused on what the regulators want for a very long time. Our customers, the banks, have come to view us as a partner in explaining to the regulators why and how things operate, what the risk level is, portfolio by portfolio. Yeah, we have a lot of it. We pay a lot of attention to that. Good. All right. What's your crystal ball tell you about the direction of the economy and the state of the consumer in the U.S. and all the other important places? Well, you know, I guess I would qualify that by saying that we get our data in arrears. I mean, we, you know, we are six weeks behind the credit bureaus. What we're seeing is pretty much what we expected, which is, you know, volume decline, but slowing rate of decline and obviously it varies by, you know, sub-market. Scores volumes are lower than they were a year ago. We made up for some of that with price. I think the consumer is actually pretty strong, and we're seeing, you know, pretty good strength in credit card activity. Auto is stabilizing. Mortgage is down, but that's not a shock to anyone. That was expected. How about, like, where some of the fintech innovation has happened over the last cycle? I'm thinking buy now, pay later, right? It went up, it went down, that was some, quote, innovation. Are there things like that out there that you think over time drive more volume-wise? Yeah, for sure. Yeah. One of the big challenges for us and for everybody in the industry, for all of our customers, is how do we evaluate more people, get more people into the system, score, and then provide credit to people who are otherwise credit invisible? We've spent a huge amount of time trying to score additional populations. We have a lot of innovation around that. We have scores, new scores that are designed to, you know, to capture responsible behavior in populations that aren't normally captured by the credit bureau file. Mm-hmm. Then, you know, you take some on pricing. This idea about getting paid for the value you deliver been, you know, part of the operating philosophy. How do you make sure you don't overfish the pond on pricing? Maybe a little bit of history is worthwhile here. You know, we've been providing scores. Let's talk about how this whole scores thing happened. Where did FICO Scores come from? How did we become an industry standard? If you go back in time to, you know, the fifties and sixties and seventies, we would build proprietary scores for bank customers, and we'd say, "Here's a scorecard. Here's how to evaluate people." Low cost way of doing it. Mainly U.S. banks? In the U.S., primarily in the U.S., although we now have international scores also. In the eighties, we had this idea, we partnered with Equifax, and we called it the BEACON Score. We launched this score that would be available to any bank that wanted to buy it, built on the Equifax credit file. That was a big hit. That was, you know, it was like suddenly you didn't have to be a bank contracting with FICO to build you a proprietary scorecard. You could just go buy the score from Equifax. So that was pretty good. Then the next innovation was to provide a score with the same odds to score ratio for TransUnion data, for Experian data. We did that, and of course, the lenders loved that because it... I wouldn't say that it commoditized the bureau file, but it gave them a little more leverage and a little more flexibility. They really liked it. The regulators saw that and said, "Wow, this is a pretty good metric for understanding the risk profile of the banks that we regulate." They got into the habit of asking, "What's the FICO Score for this portfolio or that portfolio, and how will it behave in a downturn?" Of course, the lenders, they wanna securitize the, you know, not always, but they often wanna securitize the paper. The investors say, "How do I evaluate the risk in this paper?" The FICO Score is a pretty good way to do that. We have them in the game. I'd say the last innovation on our part to get deeply embedded in the system. Mm-hmm was around getting the consumer to demand it. you know, 10 years ago, FICO's unaided brand FICO's aided brand awareness 10 years ago was about 30% in the U.S. Today, it's over 90%. Some of that's attributable to marketing efforts by our partner, Experian, which has done a tremendous job of bringing up the FICO brand in the public's imagination. I would also say a chunk of it was our open access program, where we said to lenders, "If you're interested in sharing the FICO Score, you're buying a FICO Score for your own evaluation purposes. If you're interested in sharing that Score with a consumer, you have the right to reuse it for free." We thought that was pretty clever. You know, we would really get deeply embedded if consumers started to see their FICO Score every month. The lenders kind of anticipated that and said, "Well, great idea, FICO, but no thank you." Then we had a couple of innovative lenders, like Discover, who said, "This is a great idea. Let's do it." They went and put the FICO Score on the statements monthly on the, on the website, then many, many other banks followed suit. Today, we have 250 million accounts that see their FICO Score monthly. Again, deeply embedded. Mm-hmm. How much room is there for more innovation in scores? A ton. A ton. A ton. You know, the value gap is huge. We price our scores from basis points to single digit dollars, typically, they're used in a decision that's worth hundreds or thousands or tens of thousands or hundreds of thousands of dollars. The score is probably the single best predictor of propensity to repay debt that there is. You know, you'll obviously build other things around it. You'll check income. You'll do other things. If you had to rely on one thing, it would be the FICO Score. There's tremendous value there. We charge just a little tiny bit for it. There's lots of room for, you know, for over time bringing up the prices to closer to the value. There's also innovation. To your point, we don't wanna just raise price, we wanna give more value. For example, right now, we have, in addition to the FICO Scores that you're all familiar with, we've introduced a score called FICO Resilience Index. What that does is it lets a lender look at two 680s and decide which of these 680s is likely to perform well in a downturn versus the one that's gonna get hurt more in a downturn. That's pretty valuable because the blunt instrument in a downturn is for a lender to just say, "Let's just raise the cutoffs. We'll just go up to 700. We won't lend below 700." If there's 680, 690 that are pretty good, it'd be nice to know who they are. We developed a score that lets the lenders do that. That's one example, but it's an innovation that we put in. If you buy FICO Scores, you get the FICO Resilience Index with it. The predictive power can just get better and better. Yes. What's this concept of for permission data, and what's the opportunity there? I think that's a big, big, big idea. I mean, there's two pieces to it. One piece of it is, there's a lot that the system doesn't know about you. You know, the ability to score an individual is only as good as the data we capture in the bureau file and what we do with it. There's other kinds of responsible behavior that if you could get it into the decisioning, you know, it would benefit a lot of consumers. You see that with rental data, you see it with telco payment data, cable payment data. Those things are creeping into the credit file. You know, we're trying to do that. With consumer permissioned data. It comes in two ways. It comes with Experian Boost, which is a way of boosting your FICO Score, and also with UltraFICO, which is another score that uses consumer permissioned data. What we say is, "Look, we don't know you that well. Come and tell us a little bit more about yourself, and we'll see if we can't improve your score as a result." We'll look into your checking account. We'll see you've had no overdrafts in the last 24 months. Things like that can actually help your score. Got it. Then how about like the opportunity to build more of a D2C relationship between the company and the consumer versus the partners doing it? That's a great question, and believe me, we've wrestled with that one for 10 years. The question really is, you know, you have a consumer brand like FICO, very powerful. Consumers know it and value it, and how do you maximize the value of that to our shareholders? I mean, that's the question we wrestle with. Today, where we come out on that is there are consumer marketers who are bigger and stronger and better than we are, and have more resource to promote the brand than we have. Experian in particular. Experian's our big partner. We have other partners too, but Experian's really our big partner. They do an incredible job of leveraging the FICO brand for customer acquisition, for customer relationship, and we have a rev share with them. We have a, you know, super strategic relationship with them. Frankly, you know, I don't think we could do what they do. Now, we do have our own direct-to-consumer business. It's called myFICO. It's about a $100 million business. It's an outstanding business, you know, and it grows, and it's profitable. I don't think we have the resources, you know, from a marketing spend standpoint to try to compete with our channel partners. Right. Then there's some element of coopetition, VantageScore, you know, as an example- Sure that you have to balance. Yeah. as a practical matter, you know, with the directive to for mortgage lenders to use both and also bi-merge and tri-merge, like what are the practical outcomes? Well, as a practical matter, you know, we've been spec'd in. For those who don't know, in 99% of the scores purchases in the U.S., it's completely free market and the lender decides what score they wanna use, and they almost always choose FICO. In the mortgage space, Fannie and Freddie, if you wanna securitize your mortgage, sell it to Fannie and Freddie, they demand a FICO Score. They wanna know what's the risk on the paper. Recently, an evaluation process was undertaken in which, you know, should FICO continue to have this privileged position? In the end, where the FHFA came out was, we're gonna have 2 scores. Two scores are required, the FICO Score and the VantageScore. As a practical matter, it doesn't really affect FICO at all. You know, there's the continued need for a FICO Score. You know, my own view is that investors downstream will continue to demand a FICO Score. All their models are built around it, and it's still the single most efficient way for them to evaluate the quality of the paper they're buying. I think it has very little effect. Okay. Switch to software. Sure. Let's talk software. Okay. That's the upside, right? Yeah. The bigger upside. It is. It is. Yeah. Well, there are a lot of moving parts, right? You've got the so-called established business, which is, you know, stable, and then the high growth platform business. There's lots of moving parts there. There's accounting transition, there's ramping up, there's lapping divestitures, the macro that needs to stabilize. Anyway, when all that is normalized, not in guidance, but like what do you think is possible in terms of long-term trajectory for software total? The software business could be much bigger than the scores business in the fullness of time. It The TAM is enormous. It may be worth spending just a minute on the history here. The history is that, you know, I said we started as an analytics consulting shop in the 1950s. We had this idea that if we could get analytics into software, not an easy thing to do, but if we could get analytics into software, we could get returns to scale, we could make money while we sleep. In the 1970s and 1980s, we started to build our own software, analytic software, which was adopted by banks, TRIAD and those kinds of... We also bought companies. We bought other companies. Most notably, we bought the Falcon Fraud, credit card fraud detection product, a company called HNC. you know, that is our legacy software business, is about five critical applications for banks. Fraud, It was collections and recovery. We divested that. Fraud and origination and customer management. We figured out that we could get some returns to scale if we had a common decision engine across these different franchises. We started to put a lot of energy into standardizing the decisioning within these products, within these offerings. That went pretty well, and then we took the next step, which was all these products, all these offerings require the same kind of data ingestion, data wrangling, data cleansing, data orchestration. Let's get the whole piping part of this to be more standard. We did that. It didn't take very long for us to figure out, this is now 2014, 2015, that we're really building a platform. You know, we really are building a decisioning platform that's more flexible and has more utility than just these five standalone applications, which are very important and have good market share, but they, you know, they're for very specific purposes. Over the last 7, 8 years, we've been on this journey to flesh out, put more features and functionality into the platform, what we call the FICO Decision Platform. Over the last 2 years, it has really captured the imagination of the financial services industry. All the major, I shouldn't say all. Major banks who are now challenged with, how do I do a digital transformation? How do I have a 360 degree view of the customer? How do I make sure that all my interactions across all my channels are done with a view to what the other channel did with this customer not long ago? All of that has put pressure on these financial institutions to transform the way they do business. Our decisioning platform is the right tool for the job. It's basically next generation CRM. It's a decision platform that ingest any and all data from any and all sources, apply analytics, make a decision, feed that decision into a workflow such that when a B2C company, for us, typically a bank, but any B2C company could do this. A B2C company that's interacting with its consumer in the call center, email, text, walk into a branch or a store, whatever it is, all that's done with a view to the entire transaction history with that individual consumer. What we do, we're in the optimization business, we optimize that interaction for some objective function. Is it conversion? We want the revenue right now. Is it build lifetime value? You know, let's kick them into a more valuable category, even if it has lower conversion. Is it profitability? You know, what are we trying to achieve? Then we can make sure that the decision engine drives us to the thing that's that we're trying to optimize. That's kinda what the platform business is. We have this... We have, a half dozen legacy products that are really well established, deeply embedded, still on a very strong renewal cycle. We have a platform business, which is about $100 million growing 50%, 40%-50% a year. You know, it is kinda the next generation of CRM. Right. Everybody in the world is having to get the profit versus growth equation balanced correctly. I guess you could say the platform business gets subsidized by the legacy and by scores, if you wanna look at it that way. Correct. Like, how do you think about the investment priorities for the business and what's the right cadence of that so that you don't go overboard and spend too much and get the right profit equation, but still maximize long-term opportunities? Because we have this super profitable scores business and we have this high growth software platform business, we do balance those two. Every year we think about it. We do start with what does the software business need to continue its strong trajectory? That's where we start. A lthough we're proud of the fact that our software business overall is now break even. It used to run $40 million-$50 million a year negative. It's now a break even business, and we like that. The margins will improve as we get more scale. We're not so focused on margins in the software business right now. You know, it's a land grab. We are clearly ahead of all of our competitors on this decisioning next generation CRM platform. The potential, you know, You said, "What's the value? What's the potential?" I mean, I think if you did a sum of the parts on FICO market cap, you know, we're $17.5 billion market cap today. You know, $17 billion of that is our scores business. You know, just if you look at the characteristics of our scores business and its profitability and put a reasonable multiple on it, you get to $17 billion. Which means that our software business is not highly valued. It's just not. The potential for us is to demonstrate to the world that we have this amazing software business, and, you know, I have every confidence that this software business is gonna be worth $5 billion-$10 billion in the next couple of years, next few years. So that, you know, that's the potential. Okay, how about, like, the investment priority? Do you go, like we were talking about this yesterday, down market in financial services, horizontal to other verticals, geographic expansion? Like, how do you get? It's a great question. We, you know, we're actually quite a small company. We have about 150 quota-carrying reps that do direct sales to financial services. Even that, it's really the top 200 or 300 financial institutions around the world. Then beyond that, we do it with partners. We do it with processors, we do it with other partners that give us geographic reach that we wouldn't otherwise have. We don't think that we'll ever have kind of direct sales that can capture all the value of the IP. We're really an IP shop. We, you know, we have a ton of IP, and our distribution is super anemic. Okay. That's the situation. The focus is how do we most efficiently get this IP out to the world? I think this very interesting question, do we go down market tier two, tier three in financial services, focusing on risk, or do we go across to other verticals, focusing on digital transformation? Remember, if we've had tremendous success with the top financial institutions where, you know, that we have pretty good penetration with the top 200. If you can crack the code on digital transformation for banks, so much easier for the other verticals, right? Because banks have the highest needs in terms of, you know, security and failover and disaster recovery and everything else, and heavily regulated. We think we have something that we can offer to other verticals that we're not in today. You know, everything from retail to, you know, telco to other verticals. I think we'll go there, but I think we're not gonna ignore going down market either. I think that you'll see us pushing in both directions. Do you feel like the reset that a lot of the high-flying fintechs have experienced takes some of the heat for innovation and digital transformation off of the established players? I don't think so, no. I think that the heat is very real. I mean, I think that COVID woke up all the big banks. Yeah, they were feeling the pressure in 2016, 2017, 2018 from the fintechs who are doing a better job with the app, doing a better job with, you know, how quickly you can get a loan or an application processed or whatever. I think COVID really woke them up. Everyone knew the branches were shrinking. We're closing branches. That's not the future. I think that the financial institutions recognize. They knew it was a strategic priority, but I don't think they really acted on it. I think with COVID, they've all decided to retool their businesses. They have to retool them. The pressure is from the fintechs, it's also from COVID. It's just consumer behavior. Consumers have an expectation that, I mean, what could be more digital than moving money? Consumers have an expectation that their relationship with their financial institution should be a digital one, you know, maybe you augment it with a branch, maybe you augment it with a call center, but it's gotta be a digital relationship. Mm-hmm. That's really the pressure. That's what's happening. At the same time, generally speaking, there's pressure on IT budgets. How to reconcile the pressure to innovate with the reality of. That's a super interesting question for us. 15 years ago, you know, in the last big downturn, IT budgets shrank a lot, and FICO got hurt. I mean, but, you know, FICO's revenue went down, our profit went way down. We laid off a lot of people. I mean, it was really tough times. This was before my time, but it was tough times. There were questions in the boardroom about, "Should we be as concentrated in financial services as we are? I mean, you know, when these downturns happen, we get killed?" You know, there's always been a lot of talk in the boardroom about, you know, moving to other verticals and diversifying the risk. What's happened with our offering is it is so strategic that even in a world today where IT budgets are somewhat diminished, we, you know, we don't have any trouble. We're, you know. It's a C-suite discussion. It's a strategic priority for the bank, and they may be putting other things off, but they're not putting off our stuff. Okay, you talked about all the upside that's locked in there or that's potential. Scores and software belong together? That's a good question. They're, you know, they're two ends of a continuum, right? If you're, if you're thinking about how are you gonna evaluate the risk, what, you know, what's one of the really big decisions that has to get made? It's if I lend you money, are you gonna pay me back? That's the big decision. A lot of value in getting that decision right. That's why early on, you know, even 50 and 60 years ago, we were focused on let's make that a data-driven decision and not just be the intuition of some old banker sitting in the corner office. We've been trying to bring data to this decision for a very long time. The one end of the continuum is scores, where you have a single dataset with very high caloric value. You know what, I mean, what better dataset to look at? Are you gonna pay me back in the future? Well, have you paid me back in the past? Good, good thing to know. That's scores. Software says, you know, our software business says, "We're gonna make the same kind of a decision about are you gonna pay me back in the future as, you know, as you have in the past, but we're gonna try to get some incremental value out of incremental datasets." You know, you hit diminishing returns pretty quickly, but, you know, the next dataset and the next dataset and the next dataset, they all provide a little bit more predictive value. As you go to chase those incremental datasets, you get to much smaller populations 'cause the datasets aren't available for big populations. At the one end, we got the, you know, the broad population, you know, super efficient credit bureau file-based scores. At the other end, you have the software business where any and all data will make the smartest, best decision possible, leveraging everything that could possibly be known about this individual. At kind of a, at a theoretical level, these businesses are two sides of the same idea. At a practical level, they could be separated. Mm-hmm. I mean, the truth is that the investors who are interested in the scores business and the info services side of things are not the same as the investors who are interested in the software potential. I think in the fullness of time, these two businesses are likely to be separated just to unlock the value. There's incremental value to be unlocked by separating them. They don't have to be together. There's some synergies, but they don't have to be together. For now, one funds the other. Yeah, exactly. Okay, we've already blown through most of the time. Does anybody have a question they wanna ask Will? Mic here. Down here. We've got a mic coming. Thank you. You probably get asked this, you know, all day, every day, but I think I know your answer, but I just wanna hear if there's any update. The AI threat to the business to credit scoring. I know people bring it up from time to time that, you know, eventually big tech will have more info about us than anyone else. That's a threat to scores. Could you just? I. We don't see it as a threat at all, and here's why. You know, if you're interested in whether someone's gonna pay you back in the future, that's the question, right? It's the question all the lenders have. Am I gonna get repaid? The first thing you're gonna do is you're gonna go to the dataset that has the most caloric value, which is the credit card data in the credit bureau file, and it just says, "You paid me in the past. I bet you'll pay me in the future." It doesn't matter how fancy you make the analytics afterwards. It doesn't matter how many incremental datasets you bring to the party, up to and including an infinite amount, okay? Let's say we just go out into the ether and munch, munch, munch all the data available. It doesn't matter. You're still gonna wanna leverage that data that has the most caloric value, and that's the score. You know, I see a future for scores where it's the cornerstone of this decision for a very, very long time. Even if you throw AI on there, it doesn't matter. Okay, I think we're out of time, so, thank you everybody. Will, maybe you can hang around for a few minutes for folks who. Yeah, sounds good. Thank you.