The algorithm isn’t the advantage–the team is

0:02Hey, Roberta. Hey, Lucia. So interest rates and borrowing are top of mind for many of us these days. And fittingly, today we have the CEO of FICO, Will Lansing, speaking with our North America chair, Eric Kutcher. Yeah, I really liked this interview a lot. And not just because I'm constantly checking my FICO score, I promise you. But really, Lansing gets into how the company works. One of the company's values is around ownership. And he really wants his employees to see their contributions as part of building a family business. I find that fascinating. Yeah, that aspect of culture can be super interesting.

0:44And there is so much in this interview. Let's get into it.

0:54Will Lansing: So I'm joined today by Will Lansing, CEO of FICO. Will, it is just wonderful to have you on our podcast. Oh, it's great fun to be here. Thanks. So I thought maybe to kick us off, tell us a little bit about where FICO started. What were its founders trying to solve? And how has that purpose evolved as we know the FICO of today? Well, so most people know FICO for FICO scores. And the history is that back in the mid-1950s, 70 years ago, a mathematician and an engineer got together and said, let's use data. Let's apply analytics. Let's try to be more objective about this and come up with credit evaluation done in a more scientific way.

1:39And for the first 30 or 40 years of FICO's existence, we were essentially an analytics consulting firm. And banks would come to us, ask us to build proprietary scorecards so that they could evaluate candidates for credit. We came up with this idea that instead of doing scorecards for banks one by one by one, let's have an industry-wide scorecard. Let's do a scorecard that anybody can use. And so we launched at that time, we launched the first industry wide FICO score in partnership with Equifax, one of the credit bureaus. And I would say that was pretty transformational for financial services and for the consumer credit industry in the United States.

2:18We were the infrastructure that let the consumer credit economy flourish. Before then, credit cards were not much of a factor. Revolving credit was not really a thing. And so that was kind of, that's the origins. You know, there's obviously a lot more.

2:34Will Lansing: Pretty amazing. And it's pretty amazing that it is really, it's lasted the test of time, right? I mean, it's still something that many of us talk about. It's still enabling the economy to grow, given how much we are a consumer-led economy and how important credit is. If you ask yourself the question, if I give you money, are you going to pay me back? That's kind of the core question for banks, for anybody who's dealing with consumer credit. if I give you money, are you going to pay me back? And if you said you only get to use one data set to figure that out, the data set you would use is the credit bureau credit payment history.

3:10You go into what's essentially a ledger that says, I pay my credit card bills on time. I don't go beyond my limits. And those are the kinds of things that inform the FICO score.

3:24Will Lansing: Remarkable. So today, it's hard to have a conversation that doesn't evolve or revolve around the topic of AI. And in many ways, you have been an AI company long before people really talked about artificial intelligence. What's the impact on kind of what you do and how is it differentiated? You're right that we've been an AI company for over 20 years. What we're really good at is predicting consumer behavior. We're great at taking a lot of data and applying analytics to that data to make a particular decision about how to interact with the consumer. We do true one-to-one evaluation of what's the optimal way to interact with this consumer.

4:03And so whether it's someone coming into a bank branch or showing up at a call center, inbound or outbound or email or chat or you name it, that interaction should be informed by kind of a 360-degree view of the consumer and how that relationship is evolved with the financial institution. The big change, and this is really about 15 years ago, the big change for us was going from application software where we answer specific questions around originations and collections recovery and fraud to a much broader view of how to interact with the consumer. And this is driven in part, it was driven by the internet, That is driven by retail moving to more one-to-one marketing.

4:47And it really hit financial institutions in the COVID timeframe because they realized people weren't coming into branches anymore. And suddenly it was important, much more important than ever to have a digital relationship with the consumer. And so our software filled that gap. Our software says, let's understand everything you've ever done with this consumer. And let's just make sure that when we talk to this consumer, we do things that are relevant and that are most likely to result in the outcomes that we want. AI has been a key part of this since early 2000s. We have fraud down to like a really small number, still too big, but down to a small number for our customers.

5:26And that's all built on kind of early AI neural nets. So, you know, we as a software company benefit, like other software companies, from the productivity that comes from not having the right code by hand. So that kind of goes without saying our cost of goods sold is going down. That's a good thing. Although the cost of the AI is not insignificant. But the real power, I think, for us is figuring out how to do this economically for our customers. Well, you know, there's other challenges with with AI and financial services because we're such a heavily regulated industry. And and so we run into into fair lending law challenges.

6:10You can't just have the AI and make the credit decision. It's not that simple. You know, to give you a kind of a basic example in the United States, redlining is illegal. Redlining is the use of geographic location to inform your credit decision. And, you know, in the old days, back in the 40s, bankers would put a red line around a neighborhood and say, we don't like this neighborhood. We're not lending to anybody in it. That's one kind of a problem. Another is that the regulators want to understand how you made the credit decision that you did. They want to see that it's objective, scientific.

6:45You're applying the same criteria to everybody. You're not favoring some groups over others. and very hard with AI to understand how that decision was made. You hear about AI as a black box, and you put in a question, you get an answer. We've done a lot of work in this area, and we have what we call explainable AI. We have patents in this area, where we're using blockchain to produce an audit trail so that we can go back and understand how the AI made the decision that it made. Now, it doesn't solve the redlining problem, but it does solve the black box problem.

7:22Will Lansing: You talked about, gosh, I am getting into credit and I'm thinking about how people pay back and I understand how a consumer operates. What happens as we go forward where the consumer may not be operating in and of themselves? Right. Where, you know, there's a lot of talk about agendic commerce. And therefore, I am effectively allowing something else to make those purchase decisions against a set of criteria for me. how does that impact behaviors and what you look at? You know, we've spent a lot of time thinking about this in the context of, you know, credit and financial services. I think that for the last 30 years or so, financial institutions have been interested in being the agent for the consumer for things financial.

8:07I mean, I don't think that's a secret that, you know, that's a holy grail in financial services. And you look at agents operating with AI and suddenly the possibility is quite real. And I anticipate that consumers will have financial agents that go out and shop for loans and shop for credit cards and figure out best ways.

8:28Will Lansing: We're already seeing it for what it's worth. I mean, from a credit card shopping point of view, it's been fascinating. I did this with a client not that long ago. We showed them where, like, if you type in, I live in this particular area, I've got this type, and I need a credit card, who would you recommend? It may not fill out the application yet, but we're pretty damn close. But it was fascinating to see who they recommended and why and how it differed based on the market. I mean, it was just super interesting. And this particular company had not thought about that. Well, I think eventually most financial institutions and certainly the most technology leading ones are already exploring how they're going to have their agents talk to consumers' agents to make the deal.

9:10That is clearly coming. And it opens up all kinds of new possibilities.

9:14Will Lansing: The other thing we're spending on an awful lot of time is we're working with various financial institutions to help think about how would you reimagine the underwriting process with all the context of the laws, but doing it in a very agent-first approach. Because you might be able to get to underwriting more. You might be able to have different levels of performance and outcome, which gives you more degrees of freedom, et cetera. I'm curious what you're seeing because you must see it from the other side, right? as someone that has to interact, the agents have to interact with your models and your software to be able to pull the information in to be able to do the right level of underwriting.

9:49Will Lansing: How is that? And what are the big changes that you're seeing as you come down if you are the underwriter themselves? I think that the challenge with, it's not a challenge, I mean the benefit, the opportunity with AI and underwriting when it comes, and again, within the parameters what the regulators will allow, is that you'll be able to extract signal from a bunch of other data sets beyond the usual suspects and with that signal, underwrite to a broader population, potentially give more of a loan or better pricing to people who deserve it, those kinds of things. And how does that work? It has to do with access to the data.

10:31So some of the data is still proprietary, but we're living in a world where increasingly you have consumer permission data. So, for example, FICO, we take our FICO credit score, which is built on the credit bureau credit data. That's the data that's reported by the banks to the credit bureau. Here's who paid me back on time. Here's who was late. Well, if we talk to the consumer and say, hey, can we look in your checking account? Would you be okay with that? That opens up all kinds of possibilities. Now you have consumer permission data. We can look into a demand deposit account. We can see frequency of overdraft.

11:07We can see regular income coming in. We can see regular rental payments going out. I mean, there's a lot of signal in those checking accounts. And the trick with applying AI to this environment is what data can you have access to and what can't you have access to? And how do you capture the signal out of all the data that you have historically not really had access to? There's still plenty of stuff that won't just be out in the ether. But I think the possibilities for better underwriting are real.

11:37Will Lansing: By the way, it's super interesting. For once, consumers might actually get paid for all the data they give away. Which, because I mean, presumably it would come in better pricing and whatever they do. That's just what it comes in. You get a loan you wouldn't have gotten, you get a better price on your mortgage than you would have otherwise had. Exactly. Fascinating. All right. How do you think about a CEO maintaining kind of that connection to legacy and kind of who the organization has always been while innovating, especially in the moment we are in today where the level and rate and pace of innovation.

12:11Will Lansing: And I suspect even I'd love to hear that some of the moats that you might have historically had might not be the same moats that will carry you forward as you think about what this what this company will do. You know, every business is different, but, you know, some things are universal. I mean, it's it's always useful to figure out what are you really great at? What do you do better than everybody else and kind of figure out how to take that to the next level? And that's what we've always focused on. I would say having the best people. It's a cliche, but it's so true. I mean, if you have great people, they figure out the answers to problems.

12:45They figure out the path forward. I'd say a key factor for success is, and again, depends on how fast moving the industry is, but optionality. So you do your forward-looking, your planning, your scenario planning, your strategy. I don't like bets that are singular bets that if they go wrong, you're really in a lot of trouble. I really like to preserve optionality. And so as we've evolved our strategy, whether it's dealing with AI or dealing with competitors or anything else, we really think through here's a path. We think it's the right path. But if that didn't work out, here's what we'd fall back to.

13:22Here's how we'd go.

13:23Will Lansing: Yeah, I've heard this expression used a lot that I really, really have taken to is this whole idea of one way versus two way doors. right that you don't have to be overly thoughtful about a two-way door because to your point we can go through it we can figure out if it works we can come back through it and do something different we have to understand what the cost of that is and i don't mean necessarily the dollars and cents but the organizational impact but a one-way door i better be really really certain if i'm going to walk through because i can't come back the other way yeah absolutely what have you learned as ceo you know that you probably didn't think of when you first started as you've kind of been doing this for as long as you have?

14:01Oh God, there's a hundred little lessons and probably a few big ones.

14:08So much of what I've learned has been about how to get the best value out of the people that I have. I really believe that leadership comes from followership. It comes from believing in the mission. They agree with it. They want to contribute to it. And so I spend a lot of my time, whether it's all employee kind of communication, which we do a lot of, but I spend a lot of my time trying to make sure that we're bringing everyone along on the strategy. Everyone believes in what we're trying to do here. You bring them along and again, all wonderful things follow. Another thing is, we have three corporate values at FICO, act like an owner, delight your customers, and earn the respect of others.

14:51Those are our big three. and they, you know, they sound like motherhood and apple pie, but like the act like an owner, when I sit down with a new employee, it's, this is your business. You know, you have stock in the business. This is your business. I want you to do your job as though this were a family business and you control resources, you control budget dollars, you control the time of the people who work for you. You, you control your own time. And anytime you feel like you're not doing the right thing, you're supposed to stop and say, hey, mom, dad, this is not what we should be doing. This is a bad idea.

15:24And it's taken 15 years for our culture to really come to this. But I would say that most of the people at FICO act like owners. I mean, they believe that that's what we should be doing. And again, that takes a long time to kind of inculcate that kind of culture. And we've pushed stock ownership pretty far into the organization to make that happen. but, but it works not, you know, so I'd say that's, that was a good one.

15:50Will Lansing: Every young hire does not have that same orientation today. So how do you, how do you bring them along? I mean, there's some extra pressure on young colleagues these days because, you know, FICO, many companies too, but FICO always had this view that we want the best people we can get wherever they live, however they want to live. And, you know, the smartest mathematicians, the smartest scientists, that's data scientists. And so we were a virtual company long before COVID. We had people scattered all over the world. We had people who live in Chamonix because they like to ski, but they're brilliant, so we like it.

16:26We have people who live in the Outer Hebrides, you know, and fortunately we have internet access. I mean, you know, that was always our thing was you live wherever you want. With COVID, that got multiplied because suddenly everybody really said, okay, I'm going to go live wherever I want. And our people scattered all over. that's fine for the people who already are pretty involved in the culture and understand FICO pretty well. But if you join a company that is that virtual, it's a challenge. Yeah. And so, you know, I think we go out of our way to find in-person get-togethers three, four times a year for different groups of people so that they actually can spend some time together.

17:07It's harder to forge that when people aren't together physically. One of the things, again, because you're entrusted with this incredible institution and the legacy of it, but also to go forward, you have this remarkable need to balance innovation and kind of taking it forward with the responsibility that,

17:27Will Lansing: by the way, comes with that and manages. And so how do you think about that tradeoff and that balance? And some of it is the one-way versus two-way doors that you described. I think that's clearly part of it. But we invest a huge amount in R &D. Okay, so we're, you know, as a company, we pour money into R &D. And so, you know, and we have hundreds of patents and we're constantly getting more. And I mean, it's in the nature of who we are that we're trying to be cutting edge on analytics. And so the innovation part is not, that's not the hard part. The hard part is the balancing. The hard part is how do you square that with you're running a business, you have to produce profits for your shareholders.

18:07you know you want to compensate your employees fairly you want to make your customers really happy and you know can't all be theoretical we got to get it out to market like in a reasonable time frame and so a lot of energy goes into the balance especially with software and software releases you know what what's the balance between feature functionality and getting it out the door and every software company wrestles with that and we do too we're moving to a much more kind of continuous release approach because you can now because the technology kind of allows for it

18:38Will Lansing: but it's and the pricing models of it i mean we shouldn't the pricing models change too dramatically dramatically you're now you're not in term you're not in perpetual licenses and so you're not like people didn't pay for something and now you're getting like this is kind of what you do on an annual or monthly basis well you know we you go back in time we had perpetual licenses and then we went to term licenses and then we combined various kinds of IP licenses with some kind of usage. As we moved to the cloud, we obviously had costs in running the cloud. And so that, you know, we moved to a little bit of usage-based pricing.

19:13But I would say that historically, pricing has chewed up way more time than it should. And it's just a shame. And I can tell you that at FICO, we are moving rapidly to consumption-based pricing where we're going to provide the infrastructure. We're not going to renegotiate every time you want to expand your use. Go use whatever you want. You know what it costs you. Use what you can. Use what you like. And, you know, it's for a software company with a land and expand strategy like we have, it's perfect. You get installed and then you let the customer decide how much or how little they want to use.

19:48And that's worked out really well. And I love the fact that we're spending less and less time trying to negotiate the next deal, trying to negotiate the next chunk of capability and what it should cost. and it's much more up to the customer to pull down whatever they want as they need it.

20:04Will Lansing: I've got two more questions. One, as you look forward, what do you really hope for for FICO's next chapter? Well, I think our core skills and competencies in credit are not going to go away. That's still going to be the center of the company. it's just a gigantic, gigantic market, right? We are the cornerstone for the consumer credit economy in the United States. That's not going to change. But making sure that we leverage analytics and technology advances and AI to be the best at what we do, that's pretty critical. So I hope that looking back 10 years from now, 15 years from now, 20 years from now, my successor and the people working at FICO then will think that we laid the groundwork for continuing to do that.

20:59The basics are the same. The basics are, you know, let's find more data with more signal in it. Let's figure out how to extract it at a reasonable cost. And then let's make the very best, most optimized decisions that we possibly can. That's been true for us for decades, and that's not going to change.

21:15Will Lansing: Yeah, I talk about this a lot in the context of McKinsey, and I say, the mission and values, I really think are eternal. The second final question is, If you were to give one bit of advice to someone that's aspiring to be a CEO or might just be starting their journey, what's the one bit of advice you would give them? I think that you got to do a good job at whatever job you're in. But I also think when you master it, you got to move as fast as you can and get broader, get more skill, develop more breadth. but but i think the a good path to a generalist leadership position like ceo is is to get broad skills you know don't don't get stuck in in something figure out what you need to do to get to the next level and go do it um so breadth breadth is important um there's some politics involved i mean you know you gotta you actually you can't get anything done by yourself i mean that's not obvious to people you can't get anything done in the company by yourself And so you need the help of everyone.

22:17You want them rooting for you. You want them helping you to be successful. And so you got to help them and you have to have them want to follow you, those kinds of things.

22:26Will Lansing: Amazing. This has been just an incredible lesson after lesson learned. Thanks, Eric. That was awesome. Thank you. I'm good. Yeah. Thank you. Thank you.