Fair Isaac Corporation (FICO)
November 03, 2011 10:30 am ET
Unknown Speaker -
Steve Weber - Head IR
Mark N. Greene - Chief Executive Officer and Director
Andrew N. Jennings - Chief Analytics Officer and Senior Vice President
Michael J. Pung - Chief Financial Officer, Chief Accounting Officer, Senior Vice President and Vice President of Finance & Investor Relations
Unknown Analyst -
Welcome to our Analyst Day and Investor Day and more broadly, to FICO World. I'm Steve Weber, I'm the Head of Investor Relations for FICO. And for those of you on the webcast, our presentation is available on the website, and it will continue to be available there.
I'd like to remind you that we will be, during the course of this meeting, making forward-looking statements and the safe harbor provisions apply.
Our agenda today, we're going to -- we've got several executives here, Mark Greene, our CEO, is going to talk about our -- just kind of give you an overview of the company, talk about the strategy going forward and kind of what the general market climate macro conditions that we're facing and kind of talk a little bit about what he's seeing here at FICO where we've got a lot -- more than 700 customers here and it's been a great event so far. Mark can kind of bring you up to date on that.
Andrew Jennings is our Chief Analytics Officer and he can -- he's going to talk today about the analytics, which really are the underpinnings of our products and it's the innovation that drives growth for us. And he'll give us several specific examples of the analytic innovation he's been working on and what he's been seeing this week and what he's been talking about with our customers.
And Mike Pung is our CFO and he will talk about the financials and he'll talk about where we've been and where we're going. We just announced our earnings yesterday and he can talk a little bit more about that in detail. And then we'll be available to answer questions and there's going to be a lunch in the next room afterwards. So we'll be here to help you with any of that and we we'll walk you through the conference if you'd like and we can kind of set product demonstrations if you're interested in that.
So thanks for coming. We like to thank -- we have several of our largest investors here. We'd like to thank you for your loyalty and for what you've done for us over the years.
So I'll turn it over now to Mark. Mark Greene. Thanks.
Mark N. Greene
Thanks, Steve. I'd thought I'd stand down here because it's kind of impersonal on the podium. I also thank you for coming and I wanted to make 2 introductory comments before we go to a handful of charts.
First, this is an opportunity for us to sort of reintroduce FICO to those of you who maybe have a sort of legacy view of the company, but haven't been current with some of the recent developments. And second, I think it's a timely time for us to sort of do that reintroduction because in the 4.5 half years that I've been here, we've gone through a couple of different chapters. And, I think, we're now entering a really happy chapter in the company's history. The first chapter was about a fair amount of retooling of the company in terms of changing the strategy, narrowing the vision, what we did, sharpening our products that are rotating out the majority of the direct reports that were working for me when I arrived.
And we did that work in 2007, 2008, and we were just about to declare a victory and get ready for a lot of growth when Lehman Brothers happened. In fact, the board meeting where we sort of declared that we were ready to go was the week before Lehman Brothers.
So you'll know what the second chapter looked like. Over the last couple of years was not such much happiness, as we got dragged down along with the economy generally and banks in particular.
But of late, we've been entering a third chapter, really sort of dating back to February of this year when 2 things happened: One, we began to see signs of life in the economy; and two, we somewhat paradoxically decided to further tighten our belt and build a lot more operating leverage into our business.
So the story we'd like to share with you today is a story of growth which says, we feel sitting here today that we have some pretty wind in our sails largely driven by external factors but also helped by the fact that we now have some very good expense discipline, very good cash flow generation capabilities, nice leverage built into the model.
So these days, every dollar that we bring into the top of the business flows nicely to the bottom as you'll see when Mike goes through the numbers. So for those that have sort of been away from the stock for a while or not covered our story, I think this is an interesting time to sort of pay a new attention to us because it feels to me like we have a lot of upside in our story going forward.
I further emphasize in terms of the agenda this morning, that following the lunch, we want to be accessible to any of you and particularly help you sort of navigate around this conference is taking place. We didn't quite get the word out that I intended which was you're all welcome to come for free for the entire week of the conference, which is the way we've done it in the past and we'll do it again in the future. I think, we got that message a little bit garbled.
But the reason we encourage you to do that is partially, you can see our own stuff on display and hear from some of our colleagues about what we're doing. But more powerfully, you talk at will with the 700 attendees who paid to come here.
These are risk managers and banking analysts and so on. By the way, you may not think of risk managers as sort being party animals. But last night, we have been down at the Tribeca House and so if you see some of them today, they'll look a little bit worse for wear because they turn out to be secret party animals.
But the ability to talk to those folks about what do you do at FICO? The ability to eavesdrop on a conversation is taking place right now. We have the #2 guy from the Department of Housing and Urban Development here, engaged in an argument with 3 mortgage lending officers about whether the government is doing the right thing to help out in the housing crisis in this country. That kind of stuff is the stuff of this conference, it's the stuff of this company. And I think there's no more powerful testimony to the value and the relevance that we bring to the industry than hearing first hand from clients.
So we're keen for you to be able to wonder around today. The conference goes on tomorrow if you want come to back, and sort of have that will with those folks going forward.
A final look before I start my handful of remarks. We're a small group. We will pause after each section but being in New York, we're heckling as a fine art form. I encourage interruptions, questions as we go. So just call out if there's something you want to understand better.
Takeaways from today's presentation. Couple of them. We've always been a management team but especially in the last year, a management team that understands the importance of protecting earnings and sort of being bottomed line focused.
We sort of got that as a necessity during the recession because we couldn't do an awful a lot to control our top line. Revenue, as you know, drifted southward for quite some period of time. But even during that time, we had an expense mentality that said, managing expenses, focusing on cash flow, protecting EPS is important.
And as life gets a little bit better for us, you will not see us back off that debt. We maintain a sort of bottom line focus. It's built into our DNA and equally into my board and CNA. This is a regular discussion item. We're very prudent with shareholder's money, we're very focused on returning value to shareholders.
On second, despite the challenges and I would say the challenges are a little bit more in the past than they are today, but we'll acknowledge both kinds in this presentation. I think that we have a very promising future. We've seen it already in the numbers. You're going to hear about our 3 segments called Applications and Tools, those 2 together are what we call our software business and Scores, that's our third segment. All 3 of them are showing signs of life and we'll go through some of that both in the strategy section, and then Mike will do more so in his financials.
And part of our business is dependent on how quickly the economy recovers but another part of our businesses is dependent on how much value and relevance we can demonstrate to clients and we're doing a good job of that.
So the software portfolio is growing nicely already, and the Scoring business is starting to recover as the economy recovers. And more on that later.
Finally, I think we have got a good plan. We're very crisp these days about what we do and equally what we don't do and I'll show that to you in a moment. But we know what we're about, we know our purpose in life. We know the success factors that we need to achieve, both financial and nonfinancial. And sort of unlike when I arrived at the company, which was so creative and so smart that there was never a business problem they didn't seek to solve.
We're kind of really honed in on a very specific operating plan that I'd like to share with you. Okay for set up?
All right. I'll read on the executive team. Three of us are here today. Mike Pung, you're going to hear from, CFO. This is me on top and you're going to hear from Andy Jennings, our Chief Analytics Officer.
The reason for putting this chart up is to show you that, I think, we have a pretty high pedigree executive team. When I swapped out a lot of the leaders of the company upon arriving a couple of years ago, I look for people who had led large organizations and I would argue today that we have an executive team that's probably, I wouldn't use this word loosely, but over skilled relative to the size of our business.
We're a little north of $600 million revenue run rate company, but most of us have come from a leading multibillion dollar businesses.
So we know how to scale. And while you want to be careful about promising how quickly we'll scale and I don't want to sort of over shoot the guidance that we've provided, you should know that there's a lot of management horsepower onboard. Charlie used to run -- who runs our sales organization, used to run worldwide sales of software for IBM. At the time, it was a $14 billion business, it's now $25 million.
I used to run financial services at IBM, and that was a $22 billion business. So we know sides, we know how to scale and we know the disciplines that are needed to get there. It's not sort of throwing more people and more money at problems, it's sort of having focused strategies, focused operating plans, go to market plans and the discipline sort of running a railroad the right way.
And that's what we're becoming. We're becoming sort of a best-of-breed software company that knows how to build products, ship them and it's a pipeline and to have the execution chops that are needed to drive the business forward.
Here's how we describe ourselves in probably too many words. I'm looking to always simplify these charts. We're going to talk a lot -- both me and Andy who follows me about predictive analytics. And exactly what that means to us and why we think it's timely in the marketplace.
But we're a company that excels in predictive analytics for a very particular purpose, which is to help our clients run their business. We call this Decision Management. You'll see over the course of this morning that sort of everything we do is built into a business process of one of our clients in an actionable fashion. Do we approve this loan or not? Do we let this credit card transaction go through? Do we issue this prescription to this patient for that prescription over there? They are always actionable analytics that we have, and that's what sort of set us apart from a lot of other guys who can give you Scores and numbers.
But what do I do with them? And so we're all about sort of embedding analytics in the fiber of how the business is run. That in turn drives a lot off of our intellectual property. Frankly, the thing that attracted me to this company years ago from IBM was the quality of the IP and the fact that there are 100 now, 120 patents, a little bit more than that actually. And a similar number in the process of being registered.
And that speaks to the innovation of our teams and their capacity to sort of push the envelope in hoping our customers solve increasingly complex business problems with increasingly sophisticated analytics. We also use the IP as a way of both protecting and in some cases, sort of litigating against competitors, but that's not the fundamental thought. We're not mostly patenting this stuff because we want to go out and become patent troll or something. We actually want to do this because it helps us drive innovation in our products.
And you'll see some really exciting stuff around. Andrew will walk you through 4 or 5 areas that are all new in the last few months that have all been patented.
As we mentioned, we do 3 things, and you'll see these 3 things sort of increasingly coupled and integrated as we go forward. This Scores is what we're best known for. Everybody in this room has a FICO credit score. It is the benchmark standard by which lenders in the United States and now 22 other countries make lending decisions.
Do we grant you a loan? If so, what size loan or what price? There are $10 billion FICO Scores generated a year in the United States, and I think that drives the best evidence of what I -- I'm an economist, I talk in terms of network effects. The power of FICO Scores as being sort of the standard language of the industry. And I like to talk about an example, an actual experience that happened to me last year. I was standing outside of our offices in Minneapolis waiting for the traffic light to change.
And I overheard a conversation between a woman who apparently was a real estate broker and a man standing next to her was apparently shopping for a home. And here's where I heard, I'm referring to the conversation. Hi, Tom, this is Mary. I've got Mr. Smith with me. Well, we're looking at the such and such house on Lincoln Avenue. He's a 720 FICO, what kind of a rate can you give him?
Something was said back, she whispered in her customer's ear and everybody smiled. In about 10 seconds, an entire commerce transaction taking place because 720 FICO was understood by the broker, by the mortgage banker on the other end of line and the customer to be shorthand for good credit risk, gave this guy a favorable mortgage rate.
And that network effect, having everybody in the loop understand that shorthand and standardize on a FICO Score, that's magic. And so I'm not naive enough to think that the advantages of incumbency, the network effect by itself gives us a durable business. You have to protect it. You have to invest in the product to make it fresh. You have to price it correctly to keep customers happy. You have to serve them well. But if we do those things and I think we are, this is a franchise the likes of which I've never seen.
FICO Scores have really long prominence in legs. There's a bank here in town that we work with. Begins with the letter C. They have our FICO Scores hardwired into 67 mission-critical systems. They estimate that it would take them up to 10 years to replace FICO Scores with something else and tens of millions of dollars. Now to hasten that -- and we would do that if you guys screwed up. So don't rest on your old FICO, keep it fresh, price it appropriately, service well. You do those things, we're happy campers. So that's our mentality. We think we're doing that and you'll hear more on that from Andy.
So Scores. Scores is what gets us indoor, Scores are high marginers, you'll see from Andy. They are the sort of marquee product. I'm not sure that they're be all and end all. And they're probably not the high-growth drivers because Scores will be geared overtime to the level of GDP.
Scores are really a proxy. The number Scores, that's how we get paid, by the value of Scores. The number of Scores is really a proxy for how much business activity is out there. Every time you shop for mortgage or take out a car loan or opening a new credit card, somebody is pulling a FICO Score.
The more of that, that happens, the more GDP there is going on. So the way I model our Scoring business and the way you may wish to as well. It trends overtime like GDP does. It's got seasonality and bumps in the road. But in turns like GDP, at least conventionally defined.
So it's a loan grown business sitting here today, right? That's what? You tell me, 1% or 2% business. That's what GDP is. We're trying to do some things that you'll in the strategy to expand that footprint, and sell Scores in new places, in new industries, in new countries. So we hope to do better than that.
But it's not the big driver of growth in our company. Big driver of growth in our company comes from these 2 others: Applications and Tools. These are software products. Business Applications, that do something like grant a lone or approve a credit card transaction.
And the plumbing, the Tools that we build them on. That business is a higher growth business for us. That feels more like the rate of spending that Gartner Group would tell you is taking place in sort of analytics software. That's a number like 7%, 8%, 9% these days. So we blend those out, you blend the low growth Score business and a higher growth Application Tool business and you come up with a number like 5% or 6%.
Mike is going to show you later than we guided yesterday for our new fiscal year '12, which started last month. We guided only 3% revenue growth. So that's meant to be a cautious conservative guidance number. But from where I sit and what I talk to my board about is I talk to them about 6% growth, but you should only have 3% in your models because that's what we promised the industry.
Another thing that you'll see in future charts and another attraction to me when I joined the company is for a company of our size, we have a remarkable number of products that are best-of-breed or first in their class, and we get ranked well in [indiscernible] ratings but I'm going to show you a category where we are market leaders and not just by a little bit. But we'll have 70%, 80% market share in some categories.
Now something we've gotten much crisper on since the last time somebody -- you may have been in touch with us. We do not do everything under the sun. We used to aspire to be in dozens of industries, but we realized that this is actually an expertise business.
People hire us because we understand their business and you can't understand lots of different businesses. So we picked 4 industries that we think are very common in terms of the work we do. Banking, insurance, retail and healthcare and I'll leave out government. This is just really a sales channel for us. We sell to government to the extent they act like bank or act like an insurer but we do not build specific products for them. But the 4 of them you see is banking, insurance, retail and health.
They have something in common. And that is that they behave very similarly over what you might call the financial life cycle of their relationship with consumers. They all have a need to market to acquire new customers, sell specific products to them or originate relationships with them, manage those relationships, get paid for the business they do and worry about fraud.
So as we work inside of FICO, we think that those 4 industries feel sort of 80% common to us and in fact, our applications straddle those 4 industries with 80% common code. Of course, it's the 20% that's unique about those industries that allows us to sell to them, right?
When you go to a bank, you don't talk to them about what we're doing with a hospital because that wouldn't appeal to them. Under the covers, it's 80% the same. Because who do I want as a customer, how do I sell to them, how do I manage that product relationship, how do I get paid?
So it's kind of a fun footprint for us because our modelers get to work in 4 industries that feel very different on the outside, but look very similar on the inside when you start doing analytic modeling.
Let me pause there for questions or comments before we dive into some details. This is the roadmap of the next couple of charts. Okay so far? Good.
So the prior chart said we do Predictive Analytics and Decision Management and I've become a little unhappy with that description of our company. So we're working on a slightly better one. So this is consistent with our prior chart but a little bit more specific.
What do we actually do that is both a core competence and meaningful to our clients and differentiated the marketplace? This is what we do that fits that test. We use analytics but in a very specific way. We predict how consumers are going to behave. There was a piece of the journal the other day you may have seen about work in healthcare, where I had a quote which is a little unfortunate because it doesn't sound so good but it's kind of true.
We know what you're going to do tomorrow. It turns out a lot of consumer behavior is very predictable, and quite consistent across the domains that you operate in. Let me give you the examples that go back to those 4 industries. In banking, which has been our traditional sweet spot and remains our largest industry, the classic FICO credit Scores predicts whether you're going to pay your bills tomorrow because that's the way of interpreting what our FICO Scores is. In Insurance, we noticed something interesting going on years ago. Insurance companies were using FICO credit Scores to predict whether you're going to be a good driver and help them set your automobile insurance premium. We still do that today. Why? We didn't build it for that purpose. They work really well is the answer.
It turns out that people with good FICO credit Scores are safe drivers. So let's build an insurance-specific score for safe driving, you can do an even better job but they're highly correlated.
Move now over to healthcare. What's the big -- we asked doctors and healthcare insurers, what's the big problem in medicine that we might be able to help with? It turns out that a lot of patients are bad patients. By the way, you're going to hear from Andy the word goods and bad. They sound pejorative but that's a way analyst talk. Is there a favorable outcome or a not favorable outcome. The bad patients don't take their drugs and they get sicker. 2/3 of patients are bad patients. 2/3 of people don't fulfill the prescription the doctor gives them, or they fulfill it but they don't take the meds as prescribed, or they stop taking them the minute they feel better. All these bad behaviors, they end up in adverse outcomes. Can you predict who's going to be a bad patient? Yes, you can, 82% of the time. Andy will show you -- we can you tell a doctor whether the patient standing in front him or her is likely to take their meds.
And then finally, in the retailing context, we work with clients like Sam's Club and we're part of Walmart. They want to know how to sell more products to you. And they want to know from your past buying behavior, can we predict what your likely to buy next and can we then induce to make that purchase? Sam's Club has a lot of information, what you have done in the past and so we'll look at that data. We might find that last week, you're in and bought a toaster oven. This morning, you're in and you bought a iPhone. And that might say to our models, this is a customer that's getting ready to buy a flatscreen TV next Thursday afternoon and it's this particular model of flatscreen TV. And they'll actually make that purchase if we give them a coupon of $101.32. Give it to you, it's just good for you, it's just good for that model on that time of day on that week.
And that coupon will be we redeemed 30% of the time at Sam's Club. Retailers kill anytime they can get coupons redeemed more than 2% of the time. We're running at 30% of the time. So these are 4 examples in banking, insurance, retail and health, where we can predict the consumer behavior and in every case, what we do is we share that insight with a business who can then make a smarter decision as to how to serve that consumer.
The 4 examples I gave you sound completely different. If you crawl underneath the math and the algorithms and the analytics are involved, they're 80% similar. So it's kind of magic. It's kind of -- now, if you don't tell this story well, it smacks the big brother, right? So the risk to our business is a reputational one if we're not super squeaky clean. Not just the legal aspects of this, like in healthcare there are laws that say what can and can't do, HIP laws, right? Totally compliant with all that stuff as we are in financial services what is something called the Fair Credit Reporting Act.
And it's more than that. You have to not only do what's legally right, you have to do sort of what's morally right. And so one of the watchwords in this business is just because we can, doesn't mean we should, right? But we are pushing the envelope here to sort of explore how much of what you're going to do tomorrow can permissively and responsibly be sure of the businesses so they can do a better job of serving you and you as a consumer can get better service. And so there's benefits on both sides. This is 2 sided conversation which is why we're fond of saying and my marketing guys like this more than I do, that our company operates at the intersection of Wall Street and Main Street because we're selling mostly to businesses on Wall Street so that they can serve consumers on main street. And we have to have an understanding of both sides of that relationship. That's what -- by the way, this conference is talking about is how do you sort of balance that out. I'm not going to go to this chart other than to highlight that the kind of analyst we're talking about here are actually pretty special and I would have you distinguish some of these characteristics from what you see some of the competition doing. I come from IBM, I have the highest regard for IBM. I actually started the analytics practice at IBM when I was working in the banking industry there. They don't do most of this stuff, right? They just sort of what you might call business intelligence analytics where you mine the data, and you find out who is my most profitable customer over the last month. That's characteristic of what many in firms are doing in analytics. That's not us. We're trying to step in the middle of a high-value, high-value business problem in real time and tell you whether you should say yes or no to this question right now. And that's the design point for our business. You'll have these slides available to you. We're going to send out in PDF format. But crawl through some of these keywords here and you'll find that this is a pretty special space.
It's special but it's not a small space. This is a large market that many, many business problems pass this test here. Now one overview chart and what we actually sell. So I gave you the high-level story that said, we have applications. We have Scores and a related set of things called analytics that we embed in the applications and we have Tools.
And I introduced the idea that we set ourselves up according to our financial life cycle that begins with the need to acquire new customers to what we call, precision marketing, being smart about who we market to. You can actually recognize a part of this in your everyday life. This is the part of our business that prompts credit card organizations to know who they send new credit mailing solicitations to. And they're starting to show up back in your mailbox. Anybody getting news in their mailbox now? That's a good sign. When you see that happening, that's saying that they're using our Scores to do pinpoint marketing from new credit cards. So that's the kind of stuff that goes on here.
This is also the place where the Sam's Club example that I talked to you about happens. Where we have Sam's Club figure out who do they want as a customer and what should they sell to them? And the thing that we're learning about this marketing area is that it's beginning to be more and more relevant outside of the place where we started it. We started in retail, banks now want to do marketing, right? And healthcare companies want to do marketing, pharmaceutical companies want to know how to market their drugs more effectively to physicians.
So this business has legs far outside of the place that we started which was retail. Then -- okay, so now I've got a customer interested in doing business with me. Now I need to originate some products. So in the case of a bank, which credit cards are we going to give you now that you have applied? At what price point but how much of a credit line? But there are similar set -- things that I could say about the other industries as well. It's not unique to banking. This thought about starting a relationship and figuring out the terms of service, the pricing, how much of whatever it is that you sell and to give you. That's really what goes on in the origination space. Than -- okay, now you're a customer and I have to sort of manage that relationship over time for profitability, I have to look for signs that you might be going to defect to a competitor. So is there a retention risk here, is there an opportunity to sell you more? Is it time to change the terms of service in the credit card world? They call this credit line increase and decrease, there's a lot of that going on these days as banks are trying to sort of count down on their capital needs and sort of only give you as much credit as you really need and use.
Things go bump in the night and not everybody pays on time specially here in U.S. So we need to help organizations collect money. Big business for us especially in distressed economies.
And then finally, protecting against fraud. And this is actually our largest business in the application space and one with the highest growth rates. Historically, it's been in banking. And we are very, very good, exceptionally good at squeezing out fraud in banks. And we've done so much of that, that market is not tapped out of the U.S. but fraud rates in the U.S. are running significantly lower than they were 10 years ago thanks to our Falcon product.
So we do 2 things to achieve further growth. We take that trick to other markets that are less developed so big opportunities across Asia now given the explosive growth in the financial service industry where they haven't yet worried about fraud but they need to. So that's one place we go. The other thing that we do with fraud is we take our insight about fraud in banking to a market that's even much larger and that's fraud in insurance. And those of you who have been following the healthcare debate in Washington will know that the Fraud, waste and abuse component of Medicare are Medicaid payments is a huge -- it [indiscernible] by at least 5, maybe 10 what goes on in banking.
So we have a relatively new offering called Insurance Fraud Manager. 80% of the same logic that we had from banking apply it to the business domain of Healthcare. Now how does this discussion square with my earlier conversation about predicting how consumers behave? How is Fraud related to predicting consumer behavior? This is the light bulb moment for me.
Fraud is consumers behaving in a non-predictive manner. Every time you go to the doctor, certain things happen, certain things do not happen. But then all of a sudden, you have to stop regions claim for an X-ray on your knee and you've never before and had a visit on your knee. Flag that as an anomaly and an abnormal transaction that's not something that was predicted. It doesn't guarantee by itself that it's Fraud, but it's worth further inspection. And then you run further analytics on it and find it's actually [indiscernible] You never had a visit to the knee doctor, your just submitting a bogus claim. So we're -- as would, you're picking our healthcare Fraud as we are picking out banking Fraud. And we are just getting started here. We announced yesterday that our business partner in this space, Emdeon, who is the largest healthcare payment processor has signed up their first aid paying customers of our service. So they take our product to market, we provide the IP, they run it. And they know how they pay -- healthcare companies signed up and that's the tip of the iceberg. We're going to have dozens more appear in the period ahead. So a lot of growth is the point and a lot of growth, as we expand what we're conventionally banking products into these other industries called Insurance, retail and healthcare across Application, Scores and Tools. I don't talk too much about Tools, it's the unglamorous parts of our business but it's essential plumbing. And we do 2 things with it. We use it primarily to build our own applications which are now all built on this common platform. They all use the same rules engine, they all use the same modeling capabilities and they all use the same optimization. That let's applications talk to each other. So it allows our clients to have a consistent end view of their customers over the life cycle. If they thought you were going to be a very profitability credit card customer and I brought you in the door and I issued your card according to that profile, I want to see over time, whether that paid off. And we sort of have these feedback loops that are built in now to our applications because they're all talking to each other. So that's one use of the Tools. The other use is we sell the Tools sort of ala carte to businesses that want to build their own apps and some do. Some actually will use both. They'll buy our applications and they'll buy our toolings so that can further modify or extend their applications. Too much growth on one page but I'll stop and see if you have any questions about it.
Unknown Analyst -
Mark N. Greene
Wait. Just because there's some folks online, we can do this.
Unknown Analyst -
How many of these products, Mark, are integrated with the clients' applications versus just being a dedicated servers, say, at the banks?
Mark N. Greene
Pretty much everything we do is integrated. So as a comprehensive as this is, it all needs to talk to some backing system. So if you're a bank, you have a core banking system, that's not shown on this page. But pretty much everything on this page, we'll need to talk through that. So the design point for everything we do has lots of interfaces, lot's of APIs, and it's a rich opportunity for our professional services teams go and install what you see here and wire it up back to the back office. So every industry we serve will special roundness that we need to connect to, okay? Other questions? Yes? Sir?
Unknown Analyst -
Why do you separate the Applications and the Tools segment because it, here, seem to feed each other.
Mark N. Greene
They feed each other and we've done a lot to link them. They square off against different competition in the marketplace. So the features, the timing of those features, the price points are quite separate because there are different competitors out there. And some customers, many customers still buy those as separate things. Tools tend to be bought on the IT side of the house, the apps tend to be bought on the business side of the house. That changing over time as we're doing more integration and frankly, so are the competitors. So the market expectations are beginning to move in the direction of your question but at least historically, there have been different markets. All right. Just a couple more turns. I mention that we have a leadership products. This is one deal that -- So Scores is #1 in the category, customer management, #1; Fraud, #1. You see some of the numbers there. The thing that I like is the market share is like 65%, 75%. That's kind of magic and you don't often see particularly a mid-sized company with many products that are all sort of market leaders. And they're not just leadership products. I tired -- our company has been accused in the past of being arrogant, so I try to keep this up a little bit humble. But people wouldn't like this stuff, right? If you were to wonder around this floor and talk to some our Fraud customers. We rolled out a year ago a new version of our part product that has adapted analytics. So to think about this? Many of you will have antivirus software on your PC and Symantec or Norton will figure out for you when there's new kinds of viruses out there and they'll ship you an update. Our software does something similar for Fraud but it figures out automatically. When there's new types of attacks that a bank hasn't sustained before, we detect that and immunize them against that and self update, if you will, and it's magic. I mean, there's clients out here, we have about 18 clients now up and running on what we call Falcon 6 that does this and they think it's just fantastic stuff because it's -- well, we're already pretty low rates of Fraud even further. So it's not just the #1, they're really good, these products. These are sort of leadership products in terms of their capabilities in each of the areas. Yes?
Unknown Analyst -
[indiscernible] what it be for credit cards?
Mark N. Greene
92%, something like that. Yes. Mortgages is actually not the highest market share. One more question here.
Unknown Analyst -
The 25% you don't have for mortgage loan?
Mark N. Greene
Yes, mostly in-house. We try to take the big view of the market and where we don't have the market share, it's almost always, in the case of Scoring, in-house. We'll come eventually to question of VantageScore which, some of you will know the name, it's the consortium of the 3 credit bureaus that launched several years ago to compete with us. We can't see them on the radar screen. In terms of market share, the needle doesn't move. We do know they're out marketing and testing and they have deep discussions underway. So I don't count them out as a worthy competitor but there's no measurable market share that we've been hearing.
Unknown Analyst -
[indiscernible] concentrate that the mortgage loan market is, can you tell us who that 25% is? Is it -- you already mentioned C, and then could it be a BAC? Is it [indiscernible]
Mark N. Greene
We do, yes. And in some spaces, I probably should have shown you the whole spectrum. So in other spaces like the auto industry and there's all whole auto finance panel taking place over these -- a couple of days of this conference. So we have 100% market share. Every auto finance dealer runs off of FICO Score. They need to because they talk to each other and customers comparison shop between different auto dealers. And so if they are getting different loan treatments, that's the great thing about this early discussion on network effects. It also benefits from having a highly competitive economy because you want common standards to be applied across the different participants in the industry and the auto industry was not always all standardized in FICO Scores but they found that it -- most of them were and those that were not were disadvantaged because consumers doing comparison shopping were getting inferior quotes from the places that weren't using the FICO Score. So they move pretty quickly to standardization and they're now on the process of upgrading to the latest version which we call FICO 8.
Unknown Analyst -
What's your position in Fraud prevention in debit card other forms of electronic banking that don't necessarily relate to credit?
Mark N. Greene
Yes. So great question. You're exactly right that historically, we were all about credit, we read the handwriting on the walls well before the CARD Act that said that's not the best place to meet your business. So for some time now, we've been indebted in DDA as well. We're not all things under the sun yet, so have a little bit more work to do for instance, HELOCs and other financial products, although we're getting there. The nice thing about the Falcon platform is it's really that, it's sort of a platform that's extensible. So we are now solid in credit debit and DDA and we're moving to other products as well. And that's getting to the majority of what our customer needs, but they are pushing us to go further. One more? No, no, keep going.
Unknown Analyst -
Can you talk about the relative size of the 4 main groups and the growth trajectory in each of them?
Mark N. Greene
Mike, you're going to, yes? So can you hold off on that? Just a word on where people sit, we're about 2,000 employees these days. See, the largest office is actually in Bangalore, where we have a little over 300 people. The majority of our workforce you can think of as being California-based, both Northern and Southern California in terms of the technology and research teams. But the majority of the field and resources are close to where the customers are that's why you see sprinkled out around us. As distributed as we are, we're considerably more concentrated than when we once were. Have about half the number of offices today that we did a few years ago. So we've been -- and I think it's about steady state for us. We're trying to concentrate resources so that we get critical mass in each location but we're also trying to stay reasonably close to the customers. And so this feels like about the right balance point between those competing needs. We have gotten pretty good in the last few years under the leadership of our technology guru, Deborah Kerr, our CTO at distributed development. So we have a lot of product stocking among them that are designed and architected into some -- and built in California. But completed and maintained and serviced out of Bangalore. And I'll acknowledge that was an easy transition there. People talk about follow the sun development models and we had a few stumbles but we're well past those and this is a pretty efficient, well-integrated global operation at this point. Questions on this? Workforce is likely to remain essentially stable. Mike will give you some guidance, we're adding an order 100 employees, something like that going forward but you're not going to see us -- we were once up close to 3,000. That was sort of before we did this restructuring work. This fuels -- this number of 2,000 might become 2,100, 2,150 but in that range, we're not going to get back up in the high-2,000s.
Unknown Analyst -
I'm very dated on your company, but years ago, there was a consulting business. Do you still have that or what happened to it?
Mark N. Greene
So, that's right. We did an acquisition with a company called Bong and my predecessor came from Accenture. So his ambition for FICO was to be the next Accenture. When I walked in the door, we had a team consulting a Romanian bank on their international expansion strategies. And I asked, one question, what do know about that issue? And the answer turned out to be nada, all right? But we took the business. So I did 2 things with a consulting and more generally, with a professional services business. I significantly enlarged the number of people. We now have 800 people thereabouts in our PS organization and I simultaneously shrank their turf in terms of things they do. Our PST now is there to help sell, install and customize and integrate our software. If the Applications and Tools and Scores that you saw on the previous page don't figure prominently in the engagement, we don't take that piece of business. We're not management consultants, we're not strategists. We have thoughts about those things but we have deployed them only in the context of selling our stuff, right? And so the fundamental product is software-led consulting. It's -- those -- it's in my IBM DNA, it's in many of my colleagues as well. So this has modeled the company very naturally to us. And by the way, there was a little bit of cultural heartburn, in fact, some of those consultants, the business consultants sort of left when we changed. So -- I'm too pristine to sell software. I don't want to touch stuff, I want to think, right? Okay, well, then this is not the company for you. We fought those battles a few years ago, we're well past that. And now, everybody that's on board is actually kind of psyched because as good as our consultants are, it's often the software that gets them in the door. And so they understand the value of that model, okay? So where do we sit today? I think I skipped over -- I did, okay. One last comment of this before I sort of wrap up on 2 charts. The good thing that's nice about this company, we have a lot of sort of special qualities that haven't always properly appreciated. This was of clients is like gold. There are many, many software firms our size that would kill to work with the companies that we work with. And we work with them every day. And I won't tell you that every relationship is smooth and happy every day but these are very durable relationships. Essentially, every name on this page we've been at for years and years and we get wired into their systems in such a way that we're invited back every day. And so not only are they quality companies we work with, but we have these sort of deeply embedded relationships and very high renewal rates. Mike can speak to you to some of this. But it leads in part to this very high level of recurring revenue, right? That these companies who've been around for a long time have been doing business for us with a long time, allow us to have about 75% of our revenues. So in the bag, the first day of the quarter is recurring revenue because we know what's going to happen. We've been -- we signed long-term engagements with them, we have transactional revenue that we can model and predict, and 75% of the hill we have to climb every quarter is already climbed on day 1. So 2 last charts. Where do sit sort of today? Well, it's kind of a mix picture. A lot of what we do is geared by how healthy the bank is or the banks' consumers. Here in the U.S., that's the sight of a mixed picture and the word housing figures prominently in the middle of this discussion is that's a central topic of this conference this week is, how, when and if do we get out of the housing sort of down spiral? Even downturn, there's some good business for us. I'd like to see us in growth mode, I'd like to see the housing industry booming because we do even better then. But even in a downturn, we have opportunities -- we recently launched something called strategic defaults predictions service. It turns out in housing. A big problem these days is people who have the ability to pay for their home but choose not to, they walk away from their home. And that's about -- that amounts for about 1/3 in foreclosures taking place in the U.S. right now. Andy's going to talk you about this but we're really good at figuring out who those people are. So that's medicine we'd rather not having to be delivered. I'd rather that the economy didn't have this problem. But even in the phase of adversity, we can find opportunities for us. Yes? Sorry, wait for the mic.
Unknown Analyst -
What does a lender do with that information? I suppose you say [indiscernible]
Mark N. Greene
[indiscernible] he can tell you the phone calls they make as a result, okay? But those sort of negative macro effects are being -- which are not universal, right? We're big in China, and China's all about growth and happiness and how many homes can we sell, not how many home are under water. But even in places where we have those negative macro effects, there are sort of good things happening in technology spending. And if you go to the Gartners', in their towers, and by the way, they're here as well this week. They will confirm this 8 or 9% growth rate that I talked about in IT for things like Applications and Tools. So there's packets of happiness there. And we do see that this whole area of analytics who we've been talking about is the -- it goes by different names. You're probably reading a lot these days about big data. I don't really know what big data or what it's useful for but I do know what we do with it, which is mine it for insights and actions, right? And so analytics on top of big data seems interesting to me. Big data by itself is like [indiscernible] disk drive or something, I'm not sure. So we see, as you sort of bounce us all out, we see good growth prospects. An actual growth recently on this kind of range of 7%, 8% in Europe and Asia. Actually, our Asian business last quarter, did 18% growth. I'm not sure that, that's sustainable but it's certainly a strong growth. Whereas we're much flatter here in the U.S. and when Mike shows you the numbers, that's a concern because America is the largest part of our business. So we're doing well outside the U.S., the question is can we get the U.S. engine revved up to similar levels? And this growing business is not just stable and the results we announced yesterday that you'll see again from Mike, Scoring business is growing but we're cautious to say that 1 or 2 data points does not yet constitute a trend. We were up 8% last quarter, both sequentially and year-over-year. I'd like to see that happen another couple of quarters before we declare victory because there's still risk out there. But everything has turned. A big part of our business grew last quarter and the wind is slowly building in our sails. So final chart for me. To sort of setup what Andy's going to talk about around innovation. There's lots of room for us innovate even in the places where we are market leaders because I talked to you about the number places where our market share rank is #1 or #2 but even where we have that kind of rank, our share relative to some high-growth markets is pretty low. If you take sort of the bigger, bigger definition. So how does this square with my 70% market share charts earlier? I'm taking a large view of everything you might do with Fraud or everything you might do around Originations. We have headroom is the point. When I show those 70% market share numbers, some people are [indiscernible] growth. You guys are matched up, there's no room for growth. If you enlarge the definition market opportunities, the addressable markets as we've done here, you can see there's plenty of room for growth. And so the challenge will be how do we go after through innovation and new forms of analytics and that's what Andy's going to talk about. Anything for me before I step off and let him follow ahead?
Unknown Analyst -
Mark, is there a way to characterize your revenues between the analytics that helped mitigate risk, to protect against loss versus the analytic, the revenue to drive from helping your customers acquire new customers?
Mark N. Greene
So I'll answer that by going back to my life cycle chart and then Mike, will have to give you the numbers behind us. But your question, I think, can be posed as follows: Risk -- Analytics for Risk Management tends to show up mostly in the later stages of this life cycle. When something's is bad , late life cycle activities and we're seeing a lot of that in any economy that's depressed. So the U.S., many European countries at the moment. Whereas, analytics for growth tend to be an early-stage start. By the way, that's why I'm happy to see credit card marketing resuming here in the U.S. That's the first harbinger of our recovery. But this is mostly stuff that's going on these days in China, in Brazil, other parts of Latin America. And so what you'll want -- to answer your question, what you'll want to do is you want to map Mike's revenue decomposition by each of these areas to understand how much we're reselling here versus how much you're selling there. But it depends where a given economy is in terms of this life cycle it needs.
Unknown Analyst -
Are you kind of [indiscernible]
Mark N. Greene
No. We sort of have a built-in hedge here, which is a nice aspect, right? So we're selling a lot of this stuff in the U.S. right now. We're selling a lot of this staff in places like China. And so long as the global economy is sort of rolling recession stuff, that's a good place for us to be, the build-in hedge.
Unknown Analyst -
And the reason I ask, I would think, customers would pay more for new customer than maybe than [indiscernible]
Mark N. Greene
If you have this kind of losses that some U.S. home lenders have over here, you'll pay a lot of money for help. So you might think that growth commands a higher premium than risk, but I don't think that's the case. I think it depends on the business needs of the client. Okay, thank you for your time. And over to Andy Jennings.
Andrew N. Jennings
About 80% of our business is risk management, credit risk management, the other 20% is not. Rough numbers. We're all about credit risk management. Okay? Thank you, good.
Andrew N. Jennings
So my name is Andy Jennings. And what I'm going to do for the next few minutes is to talk about 4 of the examples that Mark actually mentioned in passing. And try to illustrate what the business problem is that we're solving and exactly how we go about solving that issue and then the sort of a value proposition that, that place adds to the -- to our client. And eventually, actually to U.S. consumers. This is a bit of an I Chart and one for consumption in a private moment, probably, rather than in a public moment like this. But we this up just to illustrate that we are continuing to innovate across all of these different sub segments, okay? And once we talk about things like Customer Management and there's a tendency to connect that only to financial services, the concept of something like Customer Management plays out across all of the industries that we serve. So it shouldn't be thought of just narrowly as something to do with credit cards or to do with banks. And I'll illustrate that in the 4 examples that I'm going to talk through and these are all concepts and products that we brought to market in roughly the last 12 months. They're all concepts and inventions that have come out of the research group at FICO. And they're all areas that have received, I would say, received great receptions in the marketplace. So they're all things that we out there actively selling and being bought. I'm going to talk about 4 things in the order which they flow down through that chart. And I'll tell you a little bit more about what we do in retail. Mark touched on Sam's Club as an example. Sam's Club is not the only client that we have there. We also work with the Best Buy. And we have a client, a big client in Canada that we signed a quarter ago. And so it describe the scale of the problem because the issue in retailing is the scale of the problem to be able to solve it efficiently, okay? I'm going to tell you a little bit about medical adherence which Mark also touched on. And there, the challenge in medical adherence isn't so much a modeling challenge. It's a different sort of challenge because of HIPAA regulations and, ironically, the patent may be ironically, but it -- not to me, but it might be to you, but the patent that we have actually isn't on analytic itself, it's on how you assemble the data in order to be able to create the analytic, which actually, in many ways is more important because if you can't do that, then you can't create the downstream analytic.
I'm going to talk about capital allocation, which is all related to that infamous thing called Basel II and Basel III. And then, I'm going to finish on strategic default. And what's the common thing -- theme that flows through these 4 examples is all about making a decision and making that decision efficiently.
If you think about what the credit Scores does, it helps that credit market operate in a more efficient way. So how can we help retailers operate in a more efficient way? So retailers spend an awful lot of money on you and me, either their own money or the money that they get from the manufactures given as coupons. And it tends to be a -- everybody gets the same coupon until that's highly inefficient because either the coupon is not relevant to me. I have no interest in the thing that's being put in front of me or almost, as important, the coupon isn't necessary. It's wasteful because I'm going to buy whatever it is that's on that coupon anyway.
So that's a misallocation of resources. So how do you tackle that problem? Well, the way -- one way to tackle that problem is to look at what people buy and to be able to build models to predict what they will buy in the future, not just what, but when. And so in the case of Sam's Club, we have many millions of customers. And because of the size of their operation, many thousands of different products or SKUs have an added complexity because people tend to buy things together. Classic example, milk and cookies, let's say, or a DVD player and DVDs. And so on and so on. So there's an added complexity in the way that the incentives work because manufacturers and retailers are looking to bundle products together in the way that they make offers. So just to give you a sense of the scale problem here.
If I've got even 5 million customers eligible at any one time and 2,000 SKUs and you can do the arithmetic, all of a sudden, you have a billion decision variables. Mainly a decision, a billion observations, probability observations of what I might buy at any 1 point in time. And you have to be able to create many hundreds of models in order to be able to create that matrix of observations.
And then you've got to be smart enough to turn it into a marketing campaign. And the way that we turn that into a marketing campaign is by talking to Sam's as a client, and then using our optimization technology to convert all of that information into a personalized version of a set of coupons that will go to me or to you or whoever it may be, okay?
Now I touched on the wasteful aspect of that because the other thing that happens in this solution through market testing, which is not anything new, everybody does market testing but there's a discipline that comes with it that we've perfected in all the work that we've done with banks down through the years is concept of champions in challenges and so on. And through that market testing, it's possible to work out what type of coupon should go to what type of individual in order to create the greatest lift in the purchase volume, okay? And not surprisingly, the efficient allocation there is not to give everybody who's got the highest probability of buying something the biggest coupon because that turns out to be a waste of money. What you need to do is to allocate those coupons in a more effective way.
And that prediction and allocation, and therefore, and all the way through to a marketing campaign is essentially the business problem that we solve for Sam's. And you can read these quotes at your leisure and there was a story in the New York Times about this. It was a few months ago now and they had a quote from Sam's customer. And it said something like, Sam seems to know me. And that was based on the offers that we were generating for that customer.
Questions before I move on?
Unknown Analyst -
I guess, I'm just wondering how you look at the data in the sense that, there are probably a lot of people that walked out with a product that they bought with a coupon. And they say to themselves this is great because I was going to buy this anyway but I got this $300 off coupon. So I just saved $300. Yet, perhaps your analytics are saying to the retailer, he thinks that. But we can prove that if we didn't give them the $300 off card, he wouldn't have bought it. How do you reconcile those 2 kind of conflicting descriptions of reality?
Andrew N. Jennings
So there's always this conundrum, right? What they would have done, and what you can induce somebody to do. And really, the only way to unravel that is through some -- through a sensible testing regime. So you have people that look alike and you give them, one person an offer, and you know that you might be giving it to them because there were going to -- despite the fact they we're going to do it anyway. And you have another that you didn't give the offer to and did they or didn't they, and then you could compare the two. And as you do that, of course, you create more variety in the data and therefore, you create better models in the future. The other aspect of it, which is important, in the Sam's case is that they are a rewards-based company as well. In fact, they make most of their money from your annual fees, not from the merchandise. And so, there are specific campaigns where the objective function of the campaign is different. So the objective function might be to reward members with relevant offers. So I'm going to give you an offer for something that I know you're going to do anyway, but I want to make it relevant because it's going to make you feel good about Sam's Club and come back and renew your membership for another $100 for another year.
So what the objective -- the business challenges that this data and this insight could be turned to can vary from one campaign to the next. Sometimes it can be to sell. We want to sell more breakfast cereal and the next month, it can be we want to reward members, and we're going to do it in a particular way, and these are the manufacturers that we have lined up, right. And the other thing that this then goes towards, which I -- which will I think of it, which is actually very, very important is the alignment between the manufacturer incentives and what people actually want to buy. Rather than turning up and saying, hey, I'm General Mills, please get rid of this 5,000 vouchers. You can have a conversation back the other way that says, you know what, given the insight we have on the customer base, it looks like we only really need 3,000 of those.
And the market research for, you believe you've got 2 groups of people that are exactly the same and you give 1 the coupon you -- how do you wrestle with the other conundrum which is, you think these people are all the same, but they're actually different in a way that you just don't know.
Andrew N. Jennings
Well, there's always a possibility of confounding variables, right? So there's another, actually the way we tackle that is through another statistical technique, which, I don't -- I'd love to talk to you about it for longer. But it's a fairly technical conversation. It's something called causal modeling. And what causal modeling allows you to do is to compare sets of data through statistical analysis. Even when you don't know for sure that you, how to create samples of people that are actually comparable to one another, it's a technique for plowing through that information to do. The best job that you can to be able to create 2 samples of people whether data isn't confounded, right? Because there's -- you might think that they are the same, but there's some other thing that you didn't really know about them, right? And so we have a way of dealing with that problem. And that -- and the interesting thing about that is that problem occurs just about in every industry that we serve, and so when we invent something like that, it's applicable across banking and then retail, and so on and so forth.Okay. Medical adherence. So Mark touched on this one as well. And here the problem is, can I predict whether somebody will take the medication or not? The answer is, yes, I can. Who's interested in that? Well, pharmaceutical companies are clearly interested in it because they want to know whether you're going to keep taking it, and therefore, they will keep selling. Payers are interested because they want to know whether you're going to keep taking it and if you do, hopefully, you don't get as sick as you might otherwise would, and you're eventual medical bill is lower than it might otherwise have been. So there's an opportunity to apply this type of insight across the value chain in healthcare. It's not just about pharmaceutical companies or it's not just about payers. This is a topic which is relevant to both ends of the spectrum, if you will, both ends of the industry.
The chart just happens to show areas of high adherence and low adherence. And this is sort of then starts to play into the same sort of problem as the Sam's Club problem because the issue here is -- all right, now that I've predicted that your adherence is low, what do I do about that? Because that's where the billions of dollars get spent, in nursing care or phone messages or whatever else it may be. And so this is another efficiency play because this is all about, how do I allocate those dollars more efficiently, so that I spend more money on those people that have more serious diseases and lower adherence prediction than those that have less serious diseases or ailments and predicted to have higher adherence, and they're likely just to look after themselves. And as I said, they actually -- the challenging part here, isn't in so much building the model. The challenging part is to be able to bring together prescription data, because the way that you measure adherence is through refills of prescriptions with the other types of data that we use to predict prescription renewals, which is general demographic information, aggregated credit information, because you can't use individual credit information in this type of situation. And a realm of other data sources that we pull from third parties. And the trick is to be able to assemble that data set in a way that's compliant with HIPAA. And we solved that problem and that, as I said earlier on, is where the patent is. It's sort of further upstream, and so we more protection from that.
Who is your actual customer? Is it a PBM or who are you selling this to?
Andrew N. Jennings
At the moment, we're selling this to pharmaceutical companies at the moment. So they're using it, they're using it for marketing. They're using it for contacting users of their drugs in order to, to try to increase adherence, and from there point of view, increasing sales.
Okay. Why not target the PBMs as a potential...
Andrew N. Jennings
There's no reason not to. But the initial take up here is with pharmaceutical companies. Okay?So this third example is what we call capital allocation. This one's slightly different although, the underpinnings are still predictions of consumer behavior because the underpinning here is the risk score. I should imagine everybody in the room will be familiar with capitalization of banks, Basel II and then subsequently, Basel III. One of the problems that Basel II created in all its wisdom is it basically, baked in procyclicality, as it's called, into the capital allocation calculation.
So you look at your book and you predict risk, and times are good, risk looks low, capital will follow through the arithmetic, capital allocation goes down. The reverse is true, of course, when things are bad. You do your calculations, you predict the risk, risk is high, you follow through the calculations and the capital requirement goes up.
And the question is, what do you about that? Well to be able to do something about that, you have to understand the relationship between capital risk or capital -- sorry, credit risk and the macro economy. And so, that's essentially the problem that we've solved here, is to be able to create a modeling framework that relates movements in credit risk to movements in the macro economy.
So for example, forecast of unemployment, forecasts of changes in the Housing Price Index, forecasts in changes in GDP and so on. So it's a macroeconomic modeling exercise. We're not in the -- we're in the macro economic forecasting business, right? We're creating a framework for banks in this particular case to model how risk changes as measured by some scorecard or set of scorecards that they have, and one of those might be the FICO score, is the FICO score. To relate the macro economy to movements in the meaning of those scores and from that through, into a capital allocation and -- a capital calculation on the capital allocation, right?
And not only does this then apply in the, if you will, in the finance department, where that calculation has to take place. But it also applies in the individual decisions that are made by the consumers because the one thing that Basel III will cause and is already causing is for banks to be much more efficient in their allocation of capital.
So I need to understand the future risk of the consumer, how that consumer might, how that risk might change under different economic circumstances and therefore, be able to plug that into an application that we sell, like TRIAD in order to allocate credit lines, just the way I end up sort of process way that I did before, but now in a more intelligent way in order to be able to optimize my use of capital. So that's the third one.
And the last one is the foreclosure problem. This thing called strategic default. So a strategic default is a person who stops paying their mortgage and continues to pay everything else. And so it's the purest form of a strategic defaulter. I don't like the deal anymore. I'm going to walk away, keep paying everything else. It's not, on the face of it, it's not about credit hardship, okay? The challenge that, that represents because it then tells us that this is not a normal credit risk event, right? And so, whenever I go to my traditional data sources or I go to my traditional tools, like the FICO score, it doesn't predict this, because it's not the same types of behavior.
So that's, as you know, you got to come up with something else, and that something else needs to include some other ingredient in it in order to make it work. And so the 2 ingredients that we bring together here in order to be able to predict this efficiently and effectively is not just credit information, but also loan-to-value information, not surprisingly, right? Because the root to the problem here is that the property is underwater.
Well, it turns that, that of course looking at the current loan-to-value ratio is sort of interesting, but not particularly interesting. You have to get into the mind of the consumer in order to be able to model this problem correctly. And what's in the mind of the consumer is that a consumer is looking forward as to what that future loan-to-value ratio might be. And so, when you do the statistical analysis and the arithmetic, what turns out to be important is the projection of the loan-to-value ratio, not the actual loan-to-value ratio, okay? And then you combine that with other pieces of information, and it turns out, we can then predict basically, the people at the bottom of the scale and those at the top of the scale. The top of the scale are those that are most likely to be strategic defaulters are 110x more likely to be a strategic defaulter than the people at the bottom of the scale.
And this is before they stop paying. There's no point in doing this after the event because it's done. I know, I figured it all out, all that I got to do is look at the data and it's done. And so this is before anybody's missed any payments, okay? So the question was asked earlier on, so what do you do with that? Well, it's a bit -- the analogy is sort of like bankruptcy in a way, because you can't phone somebody up and say, hey, I think you're going to become a bankrupt. I can't phone somebody up and say, hey, I think you're about to strategically default. You can and banks are, and we have 5 I think at the moment that are in testing with this solution, which we recently brought to the market. You can phone somebody up and say, is everything okay? This whole concept of pre-collections calling is now very, very widespread across the banking industry and strategic default is just another example of that. You can talk to them about the implications of not paying.
And then there's another point in the decision cycle, which is, let's suppose they've actually missed one payment on the mortgage and the other stuff is still current, and you see that from the credit bureau report. If their strategic default probability is high, then you have a different approach to them in collections than you would otherwise have done. You might be less forgiving, less tolerant, you might have a different script that you would use with that individual.
The other place where this gets used is in the, sort of, back end of the capital allocation, bad and doubtful calculations because even if I don't want to actually start calling people, I can have a better calculation of my potential, my risk profile of my portfolio by marrying this together with the more traditional credit measures.
Okay. So is there any other questions on that one?
Just taking that a step further, do you help them decide, to help the banks decide, should we offer these guys some sort of incentive program, so maybe we'll cut the principal to the value of your house, that would then put them, lower their risk for strategically defaulting in the future. Is there some sort of process that you have?
Andrew N. Jennings
We don't -- in the sort of explicit sort of consulting sense maybe that the question was asked. But we do, do something that helps them make that decision. So it's possible to model that problem and it's all about what's the net present value of the loan. So you can construct an analytic model that says, given the relationship between principal and their propensity to continue to repay, and so on and so forth. Which you can then optimize and then for any one individual, you can come up with a set of actions that, that look like they would be the appropriate ones in order to maximize their net present value of that loan, assuming that, that was the business objective. So something like that model feeds into that determination. We're not -- We don't make the decision on behalf of the bank, but we can build the analytic framework that helps them make that decision more effectively and efficiently.
Yes, a quick question. Just trying to, for each of the 4 different analytics you've mentioned, just trying to understand the sources and the breadth of data and where you'd get them from? I mean, for Sam's Club, I presume it's pretty much just their..
Andrew N. Jennings
It's their transaction data from the club card.
Yes, but for, for medication adherence and the capital allocation stuff, like where are you getting the data, and to what extent is that?
Andrew N. Jennings
Okay. So Sam's is transaction data-based, that's an added level of complexity because there's a heck of a lot of it. The medical adherence is marrying prescription data, which is the, in the analytic lingo is the performance variable, and we get that from a partner because we're not in that business. We have a data partner that is in that business collecting that information. And then the data that we use to predict adherence is, comes from a whole range of third-party data sources, demographic information and asset information and all that kind of stuff. There's lots of it. And there's lots of people that sell that information. So there's no, there isn't sort of are risk in that kind of stuff being available. The capital problem is largely generated by the data that the banks have themselves, because by just managing their portfolio and managing their existing risk tools. They collect information about bad rates and so on and so forth. Then we just build through publicly available sources to get the macroeconomic data that we need. We're not doing that exclusively in the U.S., we actually sold, actually the first sales of that tool were in, to Raiffeisen, Raiffeisen Bank who's speaking about this topic today. They're headquartered in Austria and then they sort of operate through Romania and all of that piece of Eastern Europe, then towards Greece. So that's the 2 data sources that come together there and then in the strategic default case, it's credit bureau data and its data on forecasted AVMs as it's called, automated value models.
Traditionally how would a FICO score -- how significant was it in a FICO score if someone defaulted on this mortgage and now that you can identify, make the distinction between a traditional default and a strategic default, has that been incorporated into your calculation of FICO scores?
Andrew N. Jennings
Yes. So in the versions that were prior to FICO 8, a bad was a bad was a bad. And so if you missed anything, you were classified as a bad. One of the improvements that we made in the most recent version of the FICO score was to separate out degrees of badness, if you will. And so, somebody that's gone bad on a low balance credit card isn't treated in the statistical pot that comes together to create the model. There's a differentiation between a person that went bad on a mortgage, a person who went only on a credit card and a person that went bad on both. And it's also related to some degree to the amount of money that was involved. So yes, we do take that into consideration. Have you got one more? I don't know, how we're doing with the time. One more?
Just on the list of your retail customers. I think you had Sam's Club, Target...
Andrew N. Jennings
Best Buy, I said. Best Buy and then...
Right. But I think in Mark's presentation, he had Target on there too. What are companies such as Wal-Mart doing and what vendors, if anything outside are they utilizing, and are those real and practical opportunities for you?
Andrew N. Jennings
Yes. So retailers are sometimes also finance companies. So the illustration that I gave was really about retailing their core business, if you will. And then Target has as a card, and they finance that card. So they're a bit like a credit card lender in that respect. And so target would be a user of tools like TRIAD or our Scores, FICO Scores, and so on and so forth.
Mark N. Greene
Just for clarity though, the Sam's Club example, Sam's is part of Wal-Mart, so that's Wal-Mart as our customer.
Andrew N. Jennings
But we also have Wal-Mart as a client in its own right doing finance related.
You have Wal-Mart...
Andrew N. Jennings
Yes, yes, Okay. Thank you.
Michael J. Pung
All right, thank you, Andy. You'll find my medication adherence score is low because I have a bit of a cough this morning, so I apologize for that. But thank you for showing up today. What I'm going to do here for the next 30 minutes or so is I'm going to tie together a lot what Mark and Andy have been saying with respect to product development and innovation. And some of the financial structuring turnaround that we've done probably over the last 1 year to 1.5 years.
We've taken some pretty significant steps to improve our operating performance and leverage, to enhance our products with additional analytics and value add, and we're starting to see some of those benefits following through into the financials. And I think as I lay the picture out for you over where we've been and where we are headed, hopefully, it will bring this together in a more constructive way. So just to kind of kick it off. We announced earnings as probably all of you know last night for the fiscal year that just ended September 30, we had a decent year. We did some restructuring, both of our balance sheet and more fundamentally of our operating expenses and our operating model.
And the way we have built the organization, virtually across the board, is focused on growing top line revenue, and we see most of our growth last year and going forward coming in our software businesses. We are very focused on expansion of the margin and pulling leverage out of that software business of ours, probably more than we have in the last 7 years that I've been part of the company. And even taking a further step in restructuring some of our contracts or structuring new contracts in a way to drive cash flow, free cash flow as efficiently and effectively as we can.
So what you will see in our numbers is an emerging picture around the revenue growth, the margin growth and the free cash flow growth that is embedded now and how we structure our contracts with our clients. How we manage our balance sheet and fundamentally how we're trying to focus our people on running the business.
In a nutshell for 2011, we finished relatively well. We, for the first time in 3 or 4 years, grew our top line, mainly almost exclusively in our software businesses. We grew our software business by 4% revenue-wise, 5% bookings, I'll expand on that in a minute.
We took our operating margins within our software business with a modest growth, up 400 basis points. Scores has remained relatively stable, slightly down year-over-year. But for the last 2 years, maybe 2.5 years, we've had some slight uptick, slight downticks on our Scoring business. This quarter was particularly good, and Mark already mentioned, we're up 8% year-over-year and from the prior quarter.
We've continued to do this through driving our recurring revenue stream in the company of about 3/4 of our business. Again, that fluctuates a little bit. But we have a very large installation base, as many of you know, who have been with us a while in our Falcon business, in our TRIAD business and then our Scores business. And that drives a very stable recurring revenue stream when the market is stable. When the market is unstable, which it was as we exited our fiscal 2008, you see a lot of volatility from the recurring revenue, but now that the market has stabilized substantially since then, so has that baseline of revenue.
The volatility in our revenue, frankly, right now is driven from the timing of license deals. And we signed some pretty significant deals over the last few quarters that on any given quarter might spike the number up or down by $5 million.
As I mentioned, we have done all this and we are focusing very thoroughly on free cash flow. We exited the year with about $120 million of free cash flow, very strong. Some of it came off the balance by managing our working capital. Much of it though came through the operating margin improvements that we announced back in early part of the year.
We also came forward with some guidance for 2012. Revenue, we guided at about 3% to 4%, all of which is coming from our software businesses or assuming our Scores businesses is going to stay the course for immediate future. That would put software growth of roughly 5% to 6%, netting down to the 3% to 4%.
Net income. This is GAAP net income. $86 million to $89 million. We took some charges last year, if you back the charges out, we did $80 million out of non-GAAP net income, which would be more apples-and-apples with the $86 million to $89 million. So we're growing our net income somewhere around 5% to 10%.
Free cash flow. We're expecting to grow 5% to 10%, and earnings per share growing a whopping 37% to 40%, much of it driven from a pretty aggressive buyback program we've done over last year. And we also took a lot of shares out of the market in the month of October. We bought back 1.4 million shares, when we were trading roughly at $23. We saw that with the cash on the balance sheet as a great opportunity to start out the fiscal 2012.
So that, that being said, if you go back and look at where we've been and where we're trying to head. Mark mentioned that in our fourth quarter of '08, when Lehman Brothers collapsed and the world around us started to collapse, we were about a $700 million company. This is apples-and-apples, we divested some products, but this is generally an apples-and-apples view.
And in the quarter between our fourth quarter 2008 and our first fiscal quarter of 2009, we saw a dramatic decline in the baseline revenue. Everything dried up for us and general volume started to decline. A lot of it initially was driven from the Scores business and then it expanded across our portfolio.
And we went into a dark period, I would say, for about 1 year to 1.5 year, while we were working very hard to stabilize the company, ensure that we were continuing to drive the earnings and cash flow that were expected by the investors of the business, and we did it while revenue came down roughly $80 million to $100 million over a 1-year period.
We started to see, in the latter part of 2009 and 2010 some positive signs, mainly in our Scores business. You can see our Scores business came down quite a bit when the recession began and it started to stabilize. And as I said, it's been running at a pretty consistent clip since 2010.
And during that timeframe, the '09, '10 timeframe as we were dealing with the economic conditions, we made a very conscious effort to do 3 things. The first thing we made a conscious effort to do, was to manage our cost down to as tight of a level as we deemed necessary. But at the same time, more importantly, we were in the process of upgrading a very large portion of our portfolio. And we did a major rewrite of virtually all of our applications. We upgraded the technology that underpinned it all, the architecture. But we also added a number of features and functionalities to software that has got a large installation base, but just simply had not been upgraded for many years. So we put most of our free cash flow internally into the development of our software. And as we were doing that, Andy described some situations where we started to identify other areas around predictive analytics that were either built into the technology at the time we were creating it, or as I'll call it, attached to the technology in the form of models in predictive modeling. And between the rebuild of the products and between the modeling exercises, much of which was delivered in the 2010, early 2011 timeframe. We virtually rewrote our entire set of banking applications. We also, at that timeframe introduced our Retail Action Manager product. That's the product Andy was describing that's used at Sam's Club, that is focused on consumer marketing. And we also released our Insurance Fraud Manager product. That's the product Mark was describing, which is kind of an adjunct to our Falcon banking product aimed at the insurance industry. So we poured a lot of our internal energy and time into getting those products out, getting beta clients implemented, taking the learnings from that, fixing the bugs, reworking some of the aspects of the products. To the point now where in 2011, we have generally speaking, a brand-new refreshed version of software almost wall-to-wall.
2012, we have a lot of, I'll call it, write a dot releases that we're still working on, where we're adding functionality to the products that didn't make it into an earlier round. But a lot of the hard heavy lifting product-wise got done during those dark periods when our revenue was on the decline.
So as we started looking at our business in 2011, and we started seeing opportunities, so at least begin the growth, the growth pattern again of a net 2% to 3%. Again, all of it coming in Scores -- I'm sorry, all are coming at our Software business. we went forward and we did a reallocation of resource exercise between December and February and that culminated in a restructuring announcement, where we took about $30 million of costs out of our business and that effectively drove our operating margins of about 400 basis points.
We took that exercise in February in order to do a couple of things. One was to acknowledge the fact that a lot of the heavy investment that was being done during these earlier years were not complete but were near the end of.
And the second exercise -- the second reason we undertook that exercise is we wanted to free up resources. And free up margin, both on the income statement and on the balance sheet for possible investments, as we start to see the markets stabilize more and as we see more of an indicative growth trend that we believe still exists within this company despite, despite our broad reach in our products.
So we ended the year, 2011 with roughly $620 million of revenue. We guided about 3% to 4% growth. So $640 million to $645 million. I mentioned all of that is coming in our software business, any upside in the company will come in the form of expansion in the U.S. markets and Scoring and some of the analytics that Andy described. And any upside will come frankly, in the form of license sales that we have in our pipeline across the portfolio. So that's kind of the most recent pattern in terms of where we came from and where we've been and how the turnaround on the top line is, as being envisioned by the team.
In terms of bookings, which is a bit of an elusive topic for some. But in a nutshell, what bookings is, this is a schedule that shows year-over-year bookings. Bookings meaning contracts that we have signed in a particular year that represents new revenue that's being created from those contracts. It doesn't represent renewal business, which we have a lot of. It represents just brand-new business to us. Much of it is transactional business, but there's also licenses, implementation costs, et cetera.
And then the yellow bar is the amount of revenue that we derived from these bookings in the particular year. So in the case of 2008, we signed $330 million of new bookings, 44% of it we've already recognized and claimed revenue on, and the other 50% will be recognized in future periods in our income statement.
And so what this really represents to us as we manage our business internally, is we balance, as we managed the amount of net new revenue that we are generating on a year-to-year basis with the amount of, I'll call it backlog that's being created from these bookings. And what fundamentally happens is, if the average term of a booking, this $300,000 -- $300 million is 25 months, all of this revenue is literally gone in 2 years and needs to be replaced. And so, what's important to us from a trend perspective, is to drive an increase in the bookings over the long term, because those bookings that are falling off, either on implementations been complete or our license revenue has already been fully recognized, it needs to be replaced with the new business.
And over the last several years, in particular, 2011 and 2010, we have started to grow this baseline, if you will, of business by 5% to 6%. Again, most of it's coming in our software businesses. And as we have been doing that, as you can see from the mix of the business, less of that revenue has been recognized in the year in which the business has been booked, which means there is more of a backlog flowing into future periods. And when you sum all this up together, it becomes a core basis for how we internally develop and build out our internal forecast and then ultimately, the guidance.
So bookings are growing 5% to 6%, similar to the top line revenue on an expense side. As you can see, this is a pretty traditional chart we show you each quarter. We just annualized it for the benefit of the group here. We brought our expenses down pretty substantially from almost $600 million, down to a roughly, a run rate of $480 million -- $470 million, $480 million of expenses. 3/4 of our expenses, maybe 70% of it are in personnel line items. So this would include all of our headcount, the 2,000 people. All the benefits associated with it, the incentives and so on and so forth.
And we have effectively managed the operating expense run rate of our business now down to a level that we think is sustainable for the investment opportunities that we are currently committed to. We think that if you want to commit to new investment opportunities, we have room in the margin to be able to take some of the margin expansion and may be reinvest it back into the business. And we also have a fair amount of cash yet on the balance sheet that gives us other options for inorganic growth, and we consider all of that together as we develop our 5-year operating plan, and the current year business budget. So Mark, I if you want to flip me to, how to sum all this up.
So the way to sum all this up, net income and free cash flow and then GAAP EPS is, free cash flow net income, this is all on a GAAP basis. So if there's any restructuring charges, they're embedded in here. But you can see free cash flow and net income as revenue dropped, we dropped down to level in '09 and '10 in the mid-60s. We are starting to climb out of that through modest revenue growth and through the restructuring efforts, and our plan is to continue to drive the bottom line up around 10%, along with the free cash flow. And what we have done in the last 2 years is we've retired about 27%, 28% of our stock, with all the cash that we've had sitting on the balance sheet. And as you can see that retirement of the stock, when you couple it with increased operating performance and cash flow, drive a pretty phenomenal opportunity to move earnings per share, both from operating activities and from effective use of the balance sheet. And that's how the numbers are starting to look in terms of the financial turnaround of the business.
This is just kind of a pretty chart, it takes revenue and net income and our EPS. Our guided numbers, revenue net income and EPS growth drives us down to the Street guidance that we provided yesterday. We feel very comfortable with these numbers. There's challenges, I think we acknowledged yesterday on the phone call, in Europe, with all the uncertainty happening there. Europe represents slightly less than 20% of our business. So we're having challenges there. We've had significant challenges in the Americas. When the U.S. economy went bad a couple of years ago, and we're starting to see signs of life back in the U.S. But there's still risk out there, and we developed what we believe is a fairly prudent view of a set of numbers that we can manage towards in fiscal 2012, and that ultimately became the basis of the guidance.
Mark, if you want to take me down to one last page here. So with respect to the balance sheet, the story is probably similar to what maybe some of you have seen in the past. But it's slightly different. We have done quite a bit, as I mentioned, to drive free cash flow. This happens to be free cash flow per share and that's using our average shares outstanding at the end of the year, if I'd use our actually shares, free cash flow would be a well over $3.30. But we've been taking our cash flow, and we have slowly paid down some of our debt. Most of our debt is fixed, and our major maturity, we have a $40 million maturity in 2013. So there's some activity on the debt, but little. And our equity has decreased 2%, primarily due to the share buyback offset by net income.
All the while, we've strengthened the assets of the company. We've grown the asset base. We've decreased the capitalization. We're getting a better return on our net assets. And we continue to manage the balance sheet pretty tightly. And as I mentioned before, we're wringing a fair amount of cash out of our working capital.
Mark? So to kind of sum it up. I mean, coming into fiscal 2012, I think I said on the call yesterday, we feel very good about where we're headed. We feel very good about how we have weathered what's been a very tough storm. We've identified some risks that are still out there in the markets that we're serving. But we're coming into fiscal 2012 very solidly profitable, balance sheet is stronger, cash flow is stronger, our debt's very manageable. We have stable financing. We have a revolving line of credit we refinanced for $200 million. We have not tapped it. There's not an immediate need to tap it of course, but it provides us with the means to increase the buyback, if we choose to do that, or go out and do some light M&A if that is where the best of return is.
We've been able to do that by maintaining a very solid business model off that large installation base of products we have, 75% recurring revenue, very profitable. Strong operating leverage, we grew our operating leverage 600 basis points from the first quarter to the fourth quarter this year, and we think we're at a point where, with some even modest revenue growth that there's a lot of upside on our leverage model. And in terms of the balance sheet, we're generally pretty financially conservative across the board. We don't have any anything funky sitting on our balance sheet. We follow some pretty standard accounting practices. We think we put guidance out there that is prudent in light of the economy and that's the business that we're operating. It's sometimes not all the most glamorous business, but it's been a business that's been pretty solid, pretty protectable and at least as of recently have been providing returns that I think a lot of the shareholders in the room here have been looking for. So that's generally the financial story. And I'm happy to take some questions as they come around here.
The first one is just sort of your strategy now going forward in terms of doing the capital allocation. Clearly, you guys have done a phenomenal job with the share buybacks. The stock has obviously run up nicely. How you guys think about, with his consistent stream of cash flow in terms of buying back shares? Clearly you've said you're paying down some debt and the maturity you mentioned. But just thoughts around your continuation of share buybacks, acquisitions and then maybe even stepping up that dividend a bit.
Michael J. Pung
So the dividend generally hasn't been a big priority for the company. The use of our cash has been, at least, as of recent around the buyback and the repurchase plan and it will continue to be probably a predictable use of our cash flow. We've taken a number of looks at a variety of M&A opportunities over the past several years. And there were 2 things that frankly we have to do before we proceeded forward with a more aggressive M&A approach and an inorganic approach. The first is we had to do a lot of our own internal housekeeping around improving the operating cadence of the business. I think you all know about a year ago we had a big stumble in sales and we were surprised by our revenue one quarter. We've also been very, very deeply involved in development of our new products and that's consumed a lot of the time of an important group of people within the company. And though we've looked at doing some deals the time wasn't right, because we had other internal priorities that we were working on that we thought had a more immediate payback. And at the same time, we were also in a very uncertain market and running out and doing a deal didn't seem as prudent in '09 and '10 as maybe things could look in the future. So we're trying to leave our options wide open. We're trying to leave our balance sheet as strong as we can in the event that the right deal comes along. And it's very likely that we'll find something at the right price that will be a good expansion of some of the capabilities of our technology to allow us to expand beyond let's say, fraud, debit and credit, into other areas of fraud that are, that our customers are spending money on today.
And somewhat related to that M&A strategy and maybe a question for Mark, but like big picture. I know you guys have talked about before trying to apply complementary tools, sort of the technology aspect to what you guys have. What are your thoughts around -- you guys clearly partner with a ton of different third-party data sources and stuff of that. Your got thoughts around potentially or at some point in time actually owning some data assets as opposed to being purely dependent on other people's data?
Mark N. Greene
I never say never. But data ownership is a very good for business and we think that [indiscernible] a different skill set, different operational challenges, different financial profile. My preference and so far, we've been largely successful in this regard is to achieve access to other people's data through appropriate licensing, revenue-sharing deals or to go to situations where the customer themselves can provide the data for free. That's the Sam's Club example, right? We work off of their in-house data. We don't think we're close to exhausting that strategy of partnering with people for data and going to clients who have data. If we find that we run out of runway there, data acquisition, data ownership could become an option, but it's not the preference. We have enough going on, that I'd like to focus our energies on building world-class analytics software and not also have to become a world-class data operator, but we could go there if we need to.
Michael J. Pung
And practically our return on investment on what we're doing is higher than it would be if we were to go and invest in data sources.
If you were to look at the graph of the average price per score for FICO score since the inception of the company, what would that graph look like?
Mark N. Greene
It's a flat line. I have that graph sitting on my desk. It will wander up and wander down minisculely based, actually not on long term trends, but based on the volume of scores that people are using, because many of our clients are on pricing grid, where when they use smaller scores, the average price goes up and vice versa. So we see some business cycle variation in the price that we get on average. But over a 10 year period, that is a perfectly flat line. There's been 0 degradation in prices.
And that's in real dollars or nominal dollars?
Mark N. Greene
So in real dollars it's going down?
Mark N. Greene
In real dollars it would be going down.
And you talked about network effects, some companies with network effects price their products such that there's actually a huge discrepancy between the high volume users and the low volume users, would you have a similar philosophy?
Mark N. Greene
We have that notion. I wouldn't characterize the range as huge. But we do stretch it in a way you describe, but probably more modest variation as opposed to huge.
And over the years, has the company -- you said that it would be pretty flat line, have there been attempts to raise prices over the year that failed or?
Mark N. Greene
No, so this is question we're actually actively looking at as we speak. I don't think, relatively not we attempted to do anything. I'm not sure that we will attempt to change the price of the base score. The path we're on these days with reasonable success is supplementing the revenue we get from base scores with ancillary scores. So we haven't talked in detail, but we have a lot of sort of add-ons to the FICO score, things called Credit Capacity Index and Economic Impact Index. They all sort of prereq the base score who's price we're going to maintain. But we're going to get richer off those clients by selling additional add-on scores around the FICO score. That seems to be the more promising path for us.
And lastly, could you just sum up for us, if we were to compare the previous management and their strategy with how you look at things. How would you compare or contrast?
Mark N. Greene
One of the questions earlier, maybe it was yours, there was a lot consulting -- management consulting going on. We do, actually arguably more consulting and services today than we did back then, but the nature of it is very different. We're trying to be an IP-driven analytics software house for which services are an enabler. And so the point of differentiation is, can we build smarter software than the other guy? As opposed, can we have smarter people in your office than the other guy. That's the way I would characterize the difference.
And how about, in any other way, did you...
Mark N. Greene
And the other very important thing from my perspective is limiting it these 4 industries that we've talking about this morning. So that we're not all of it in there. We say no very regularly. The discipline we have now is, I to hear your story about -- I didn't give you some point, sorry, so it has to be consumer-oriented, it has to be in these 4 industries, it has to speak to our application scores or tools, end of story. Last year, some investors came to us when it was obvious that the rating agencies, the Moody's, S&Ps and so on, were not doing a sterling job and the proposition that the investors made to me was, hey, that's just math. You guys are good at math, why don't you get into the business. I actually taught about it, seriously, but then I realized that it didn't speak to this core competence of predicting what you as a consumer are going to do tomorrow. It's a different kind of math. It's about bonds and equity, securitized assets and so on. We don't know how those things behave. We think we do know how people behave. So the strategy that we've been talking about this morning is useful for not only defining what we do, but equally what we do not do. So we deselect a lot of that.
I was familiar of the company many years ago when you bought HNC, the fraud detection. I'm curious, today, to what extent is that operator is going let a separate line of business...
Mark N. Greene
No, it's very deeply integrated, actually that's a good question. When I with arrived that acquisition was already 4 or 5 years old. And a lot of people, a lot of our FICO employees in California were wandering around wearing HNC t-shirts and they would introduce themselves as HNC employees. I said, I don't think this is going to work very well. So have one brand in the company these days, it's called FICO.
Michael J. Pung
Businesses are very well integrated as well. I mean, there's a lot commonality across the application suite, and there's a lot of sharing of architecture, though we have clearly, experts in Fraud that are very different than experts in credit score production, if you will.
Mark N. Greene
Right. But it's one company.
For 2012, I guess it's easy to understand the operating leverage in the business. I mean, you have 3% to 4% revenue growth, 29%-ish operating margins, kind of 10%, 11% EBIT growth. How do we think about kind of the flow-through going forward beyond that? Do you have larger operating margin goals or think about okay, for every kind of 3% of revenue growth, you're going to grow EBIT 5%, 7%, just when you kind of anniversary the benefits of the restructuring that was very successful that you did in 2011.
Michael J. Pung
We have built into our plans, 5-year plans, I'll call it conservative and modest margin expansion. And practically speaking, though we spent a lot of money investing in the application suite, there are other ways in which we can invest in the product suite to grow in areas that are not in our addressable market. And the intention that we have is that, once we'd start seeing more stability in the quarter-to-quarter flow of our business, we'll take some of that margin and we'll reinvest it. Into the business in order to grow the footprint, if you will. And so right now, our plan is to grow the margins, but not as dramatically as we did this year.
As you think about the nonbanking, the insurance, healthcare and retail, how significant do you hope those would be over the next 3 to 5 years? I mean, obviously they've been on the radar for -- we've heard about the opportunity for the last 3 or 5 years, and what's different today? Is it that we've got new products, is it that we've got new salespeople? I mean, how should we think about what's different about the nonbanking segments today versus in 2007 and 2008 when we sort of saw those as opportunities on the horizon as well.
Michael J. Pung
So 2004, when I joined the company, 73% of our business was banking. And we successfully closed the year this year with 74% of our business in banking. So you're right on. The approach we were taking previous to Mark was to expand, expand the industry vertical, and we went overboard in terms of where we were trying to expand it. Our focus now, in my opinion, and I think this is the opinion of the full, full board and management team is that, we needed to refocus our efforts against areas where we had the best opportunity to succeed. And there are a couple of areas right now that are emerging that we believe could be the fifth franchise of the company, above and beyond the 4 that Mark described earlier. And that's in the insurance fraud industry, in the insurance fraud product of ours. It's still a very small piece of the business. But it has similar characteristics to what Falcon had back when HNC introduced that back in the '90s. It has a distribution platform that can scale pretty rapidly and it's got a, maybe not at Falcon-like profitability model, but it's got very handsome profitability model to it as well. And selling that product, both directly to our customers and then indirectly to a couple other channels gives it the ability to become that third, become that fifth franchise probably 3 to 4 years down the road, depending upon how rapidly the reseller opportunity exists. On the other hand, Retail Action Manager, which is the product that is codeveloped with Sam's has more of a near-term opportunity to become that fifth franchise. The 3 customers that we've sold, the third being the one we had this quarter are all about the equal in size. They're very big mega customers with very big names. They're all North American customers. And we see our opportunity to grow the retail business and make that a major franchise very quickly, and then we have a lot of focus on that right now. But that's also a very challenging environment for us because we are known in banking. We're not necessarily known in retail. So practically speaking, getting these customers up and live and successful is a big part of the marketing that we're doing on those products. So I would say, different than maybe it was 7 years ago. We have a couple of new products that are natural extensions of something that we have done in an industry that's adjacent enough to banking, where we have the ability, in a couple of years, talk about a fifth franchise similar to what we've been talking about.
Mark N. Greene
I think that focus that Mike was on is the only additional comment I'd offer, while the percentage of nonbanking revenue is the same today as it was 7 years ago, back then it was in 22 different industries and there was no [indiscernible] scalable asset, right. We were in aerospace and transportation, things that frankly, we don't know anything about. But now we're in 4 industries we do know something about, and we have the same percentage of revenue coming from a much smaller footprint. So -- in markets that have high growth and high opportunities. So you're right to ask how quickly does it scale. I want to be cautious about predicting that. But those 3 industries outside of banking that we're now in, will all grow much faster than banking. And so, clearly, our share of revenue from them as a percentage of total should be growing pretty nicely in the coming years.
The whole debit card fee fraygo that took place suggests that regulation may drive a lot more change in the internal economics of retail banking. And I wonder if sort of the overlap between your traditional banking business and you retail business have products that are aimed at easing that change for the bank.
Mark N. Greene
So we should invite you back. That's actually a major theme of this conference, and there's 700 people talking about that question here. We should invite you back to tomorrow morning's keynote from the #2 executive at PNC Bank, which was one of the early banks to buy our retail marketing product. We didn't -- credit to them, because we actually didn't see that fit, but they did. It's been very successful for them and helping them to cross-sell and diversify away from their card. They call it there card-centric strategy, but the intent there is, your intent behind your question which is, we don't like the card business so much, so how are going to grow elsewhere, right. So what else can we sell to these card customers to keep them as profitable customers? So they're sort of the forerunner, there's now a couple of other banks signed up behind them. But banks are getting pretty sophisticated about retail style cross-selling and up selling, and we see a good fit for what we call Precision Marketing Manager and Retail Action Manager products in banking.
Michael J. Pung
Any other final questions? All right. Well, again, thank you all for spending your morning with us. We really appreciate it. We haven't done a meeting like this now for several years. And in part, you could probably tell why we haven't. It's been a tough couple of years, but we've think we've made a couple of really important and smart moves to grow the business and to bring some return back to the shareholders. We have lunch set up down the hall, for any of you who'd want to stay and join us for lunch, and we'll answer any other questions you have informally. We also have the show going on for the next day and a half, if you have any interest in any aspect to that. We can talk about that in the lunch room otherwise, I appreciate your time this morning and...
Mark N. Greene
And was just mentioned, the show consists not only of sessions, but there's also a product exhibit area of ours. And I think, 12 or 13 partners as well. So you're welcome to wander around on that. That's all on the second floor, one floor below to where we are now.
Michael J. Pung
That's correct. All right. Thank you, again.
Mark N. Greene
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