All right, good morning. So welcome, everyone. I guess we'll kick it off. I'm David Scharf, analyst at Citizens. I cover kind of fintech and consumer finance. And it's a pleasure to have Pagaya Technologies with us, which is really one of the more unique kind of lending structures that we've really ever come across. We've got Evangelos Perros, CFO here. He has, thankfully, kind of been referred to as EP. So that'll make this dialogue a lot easier for me. So EP, you know what? Before we get into Q&A, I always like to just give you a minute or whatever, take as much time as you want to sort of introduce the company, yourself, maybe a little bit of the backstory of Pagaya.
Yeah, thanks, David, and thanks for having me. So think about Pagaya as an AI technology company that has created a network connecting, on one side, lending partners, different types of financial institutions on one side, directly connected to the institutional investors on the other side of the network, and through that, providing more credit opportunity to more people. We're today integrated with companies like U.S. Bank, SoFi, Ally, Klarna on the POS side. And we get deeply integrated into their loan originating systems and directly funneling these types of consumer assets to the credit deployment needs of institutional investors. We're integrated with 31 different partners today across multiple different channels. We have a presence in multiple asset classes with a focus on personal loans, auto loans, POS.
Then on the other side, we're integrated with 130+ institutional investors that are looking to get access to this type of asset classes. Since our inception, which was almost eight years ago, we have seen more than 2.6 trillion-2.7 trillion of applications come through our system, through our model. We have underwritten more than $27 billion of loans across the different asset classes. From a financial point of view, we are run-rating over $1 billion or $1.1 billion in revenue, over $250 million of EBITDA. We are also very proud of the fact that we're looking to 2025, for the first time in our history as a public company, we're guiding on achieving GAAP net income profitability as well.
Terrific. So listen, you're CFO. And as much as I kind of want to immediately launch into questions about kind of credit and demand and funding, this is a technology conference. So I do want to ask you specifically about AI. And just as background, there have been a lot of all-digital lenders, like an Enova, LendingClub that has been around for 15-odd years. And they've often talked about aggregating and relying on machine learning and data sets different from FICO. How does Pagaya define AI? I mean, just even at a high level, it's become almost so much of a buzzword or buzz phrase that maybe it would be helpful to kind of inform people just what is differentiated about what you're doing with data, how your lending models are being built?
Sure. So simply put, when you think about AI and how AI is a powerful tool here in consumer lending overall, it's all about the data, the vast amount of data we currently are in position to use, our algorithms can use to actually make more informed credit decisions relative to the more manual processes that traditional financial institutions use and have been using for a long time now. You mentioned FICO. It's something that people rely on to really make decisions in banks and other financial institutions. And it's truly antiquated. When you think about consumer lending and you step back, it's actually a very interesting space because you have a significant amount of data that can be available but can only be used to actually make decisions through a technology solution that we're able to provide.
And by integrating to those institutions that I mentioned before on the lending partner side, we can enable more of that credit origination and provide more opportunity to people. Specifically for Pagaya, ultimately, the key differentiation when we think about it is the integration with 30 different partners. Those partners have 60 million of consumers that they're basically serving. I mentioned before, we've seen more than $2.7 trillion of application over time. And that data is really the data advantage that we see that we incorporate into our algorithms and therefore can make more of those informed decisions.
So just following up on that, to maybe end the technology discussion, playing devil's advocate, if I and some partners in the fintech space wanted to go out on our own and kind of replicate what you're doing or create the kind of credit models that you've had, how long would it take? I mean, what are the barriers? Because it seems like it's more than just analyzing direct lending too.
Yeah, we get that question a lot. I would say, if you think about it, anybody can go out and sort of write the code to even underwrite these types of assets, right? You can hire someone out of college, and they'll be very well equipped to actually write this type of code. I think the difference is, can you really build a business around it, the infrastructure, the vast amount of data that we have? A code or any AI code is as good as the data that it has and continues, a data set that continues to grow over time in order to continuously improve and make these types of more informed decisions.
Even if, let's say, somebody undertook that effort to really build the code, get access to the data, they would have to spend an enormous amount of time and effort and money to buy, first of all, this data and get access to the data, but more importantly, create this infrastructure that we have put in place, the trust that we have with our lending partners, the integration with the lending partners, the integration with the institutional investors. Going back to the lending partners, think about a bank, right, like U.S. Bank that we integrated in our personal loan and then ultimately expanded that relation in POS. You're talking about these banks having to really dedicate resources to really allow somebody to integrate Pagaya solutions into their ecosystem and their loan originating system.
That takes a lot of time and effort on both sides, from us as well as from them. So the idea that somebody else can come in and create AI code, even buy the data, get access to the data, and replicate the type of solution, white label solution that we have put in place would be extremely challenging and would take an enormous amount of time. And the types of dollars that it'll be very difficult to actually raise and be able to spend over time.
It's an interesting concept, EP, that you've got to get a heavily regulated institution, a bank, to agree to give access of its data to you. What is the biggest barrier at this point? Is it just the inertia, the regulatory trust issues when you're trying to get a new bank signed up? Or is it more kind of just proving that the product works and it's helpful?
There are a couple of elements that I want to highlight. But going specifically to the lending partner side, what I mentioned before is we integrate to the loan originating systems of our lending partners. But we're not the originator on record. We are not the lender. The lender on record is the bank itself. So from that perspective, those banks, those lending institutions have to get very comfortable with our technology, our infrastructure from a controls perspective, compliance, reporting. They're heavily regulated by the FDIC, the OCC. And as a result, in order for us to be a trusted partner, a trusted service provider for them on their behalf, and them getting comfortable with our technology, it does take a lot of time and effort. And more importantly, it relies on building the infrastructure that we have in place, which took a lot of time to build.
We are a technology company, but we have a very strong balance when you look at our executive management, executive leadership team between sort of traditional technology, the research and development DNA that we have, and how that combines with seasoned executives and professionals from Wall Street that really brings together this type of solution for these lending partners. But there are more barriers to that, right? The other piece is on the investor side, building a network that has been growing quarter after quarter, a network of over 130 institutional investors, building that trust for them to get comfortable with the flow, the type of underwriting that we do. That's another major barrier. And then, as I mentioned, the data. As we integrate more partners into our network, more lending partners, we get access to more flow. That data advantage, it's quite unique.
We have built out what we think is probably the largest data research and engineering team in Israel of over 150 people that's dedicated to consumer lending algorithm development, but overall, I would say all of this infrastructure combination, the trust that we built with the lending partners, the trust that we built with the institutional investors is really basically the key barriers to entry for somebody else to come in and replicate that.
Got it. So you know what, EP? Let's move on to more of the fun stuff, which is kind of how your business grows, right? And clearly signing up banks, lending partners one at a time. But for those that are unfamiliar with the company, curious if you can talk a little more about the value proposition that you're providing to these lenders as a B2B2C model. And I always think of it. Listen, if I go on LendingClub, Credit Karma, or LendingTree, and I just type in, I want a personal loan, and a bunch of names pop up. And candidly, most of them are direct lenders. That's who I'm borrowing from. I never see the name Pagaya.
So talk a little bit about what value prop you're providing to me as a borrower, to the bank partner who I think is the borrower, and ultimately to the investor. And what do you charge for that? How do you get paid?
So I think that's what we're really excited about when you think about the business model of Pagaya. It's the fact that we're a B2B2C company rather than B2C. We don't have any marketing costs. We don't have any customer acquisition costs, right? Remember, the way I explained our business model is we get more access to more flow, but the customer acquisition costs to create that flow are being borne by our lending partners, not by us. They spend the marketing dollars to bring more people into their flow, into their funnel. And then effectively, their funnel becomes our funnel. So we have none of that. And as a result, obviously, that creates significant benefits to us, which I'll talk about in a minute. But to your point, let's take a simple example.
If a consumer walks in a branch of one of our lending partners, and let's say they have deposits with that specific financial institution, right? And they're there to apply for a personal loan. Well, let's say that consumer has a FICO score as an example that's below the cutoff for that financial institution, right? They walk into the branch. They apply for personal loans. They would otherwise be denied, let's say, because their cutoff on the FICO is below the threshold for that financial institution. Before they get that, let's say, decline, all the information, all the data from that application, including all the other bureau data, will come directly to our system through the API technology integration that we have. And within a millisecond, we can actually extend an offer back to that consumer through the lending partner's website.
The consumer does not know Pagaya exists, does not interact with Pagaya. They see an offer from U.S. Bank. So the benefit to the consumer, and if they choose to take it, the benefit of that consumer is that they banked with their bank of choice. They're happy about getting that credit from their bank. It's a much more convenient factor for them. They keep their deposits there. From the bank's perspective or the lending institution perspective, they're creating a more stickier relationship with the consumer. They make fees along the way, which for the origination of that loan, which they share with us. They actually do the servicing throughout. So the consumer will actually, from an experience perspective, they will get bank statements and loan statements every month from their institution, and obviously, they will do all of that without putting any capital to work.
We offer that solution on the back end, on the other side of the network. And for Pagaya, we effectively make fees from both sides of the network. Most of those fees today come from the lending partner side as a result of that white label solution. So when you think about how Pagaya grows, and that's what's quite unique, it's not about opening up the credit box or spending more marketing dollars like a B2C lender would do or a traditional consumer lending originator. We grow by adding more partners to the network. And as a result of that addition every time, we basically get more access to more flow. On average, we plan, from a planning perspective, we plan to add two to four enterprise-scale partners every year. And we have delivered on that over multiple years.
However, the other thing I want to highlight as you think about the technology companies, so we grow by adding more partners and then ultimately getting access to more flow, more data that improves our models, and we can continue to grow the top line. By not having the customer acquisition cost or the marketing cost, we have significant operating leverage. The business is very scalable, right? As we add more partners, more flow, and we approve more, any incremental dollars basically come straight to the bottom line from a financial point of view and drive profitability. Whereas other partners will have to, again, spend more marketing dollars to really expand that growth. I think that's the key differentiator.
Why, as I mentioned in the beginning, we now have visibility within a very short time since our inception to be GAAP net income positive is because we're now at the point where any incremental dollars that come through the top line, it's basically feeding and driving profitability.
So this is where I have to ask my second devil's advocate question, right? So when I describe to an investor who knows nothing about Pagaya who they're underwriting, and I say, well, you know they're getting this flow of applications that were turned down by the bank lending partner. These are all, quote unquote, turn downs. So you're sort of a second look provider in a sense. And I think someone who may not be familiar with all the in the weeds credit nuances may just say, oh, this must be the deepest of deepest subprime, and it must be the riskiest credits. Can you talk a little bit more about who actually ultimately the target borrower or credit profile is of the borrowers you're underwriting?
Yeah. So it's not subprime. The way we, if you think about through the integrations with banks and what we're actually solving for is effectively connecting Wall Street, let's say, to Main Street, right? And Wall Street is not, like private credit as we know, is not really focusing on the type of risk. It's more of a risk-reward combination profile, right? So we are solving for returns. If we're meeting the return threshold of our investors, then effectively that's the most important piece. And that's what basically we're here to solve for our investor side. So that's point number one. Whereas, again, a lot of these banking institutions have shied away from this type of consumer credit because of how it's being viewed from the regulators and internally from a risk perspective, from a purely risk perspective rather than the return.
But I want to dispel the notion that it is subprime. When you think about the consumer that we're actually targeting or serving indirectly is take personal loan, which is a more mature product. The average FICO score is 660-680. The average income that we extend credit to is $110,000-$120,000 of income over 15, 16, 17 years of credit history. So these are wealthy sort of Americans with healthy credit profile that have been, for multiple reasons, being excluded from the financial ecosystem and have been denied credit as a result of regulatory environment and things that have changed in the marketplace. So that's think about those consumers. And obviously, those are not subprime.
The other thing I would say is because of how we do business, and we mentioned that a little bit before, we have this significant flow that comes our way. We look at over $200 billion of application flow every quarter, and we convert less than 1% of that, so we can be highly selective, again, making sure we meet the return requirements of our investors on the other side of the network. When you look at credit performance, significant improvement over the last two years relative, let's say, to the peaks in 2021, where the entire industry got hit as a result of multiple different drivers then, and I think that's how to think a little bit about the clientele, and as we're starting to serve more banks, then obviously the mix will continue to go up or continue to improve in terms of the credit profile.
Let me give you two examples. We have one partner in place, multiple partners, but like as an example, that somebody has a low 700 FICO cutoff. So we can come in and offer our white label solutions to them and serve the consumers that are actually right below that threshold. We have another fintech partner who is actually serving subprime consumers. And they use our solution to actually extend their credit for people above 640 and above because they're serving something deeper than that. That's how a little bit to think about the consumers that we have been serving, which you mentioned as sort of, call it a decline. The other thing I would highlight, we're actually developing new products as well. We have our pre-screen product. This is effectively a new product that we developed over the last, call it 12 months or so.
It's now already in place. We commercialized it. It's already in place with three partners. We expect to expand that to other partners. The Prescreen product is effectively using our technology and reaching out to the consumers that are customers of those lending institutions. Through that, pre-screening who we think has the appropriate credit profile, meet the risk-reward requirements that we mentioned for our investors, and effectively improve retention for the bank, expand more credit to people, but more importantly, think about it effectively as a first look program. It's not all second look.
Got it. No, that's helpful. And I think it dispels maybe some kind of trigger assumptions about who you're serving. So we spend most of the time talking about the lending side. So let's wrap up focusing more on the funding side, the other side of the marketplace. And just very simply, how do you get paid? Who's paying you? How are you earning money by the other side of the marketplace?
So the business has evolved quite a bit over the last few years. I mentioned before we make fees from both sides of the network. Most of the fees today come from the lending partner side, but we still make fees from the investor side. And we have different types of vehicles that we do. We have ABS, where we basically earn administration fees mostly or capital markets execution fees. Obviously, in today's environment with interest rates still elevated, it's less of those fees coming from that side. We have other privately managed funds where we earn management fees. And ultimately, the continued administration of these types of vehicles allows us to extract those fees from the investor side. In the good old days back in 2021, 2020, we used to be able to extract fees in the forms of premiums that we got.
But obviously, those days are thankfully gone for a lot of us in the marketplace. And we're a much more steady environment. So we utilize ABS, where the largest ABS is here on the personal loan side in the marketplace. And within that, we have a AAA rating on our personal loans, AA rating on our auto loan ABS. We have whole loan programs in place in the form of forward flows as well as pass-throughs. We recently announced a $2.4 billion forward flow with Blue Owl, one of the largest alternative asset managers out there. On the back of other forward flows that we announced last year, we have pass-through structures where we have effectively bespoke funding structures with select investors who are looking to get access to our funding and then obviously some other privately managed funds.
It's a good mix that we managed to work through over the last 12 months. About 25%-50% is basically in these alternative structures and about 50%-75% is in the form of ABS.
Got it. Obviously, a big focus of investors in the last year is not just credit performance, as would be predicted, but risk retention and the impact. So can you, and as an ABS sponsor, you obviously are required by the SEC to retain a certain amount of securities. Can you talk about longer term where you want the company to be in terms of risk retention? Because it's always been a hot button issue and it's going to continue to be. And are private credit sources always going to be there for you?
Think about ABS. To your point, there is a regulatory requirement to hold, call it 5% of the notional amount of the ABS. So call it, as I mentioned before, 50%-75% will come in that form of funding over the cycle or through the cycle. And then the other 25%-50% comes in these other structures where we have zero or minimal risk retention. So on a blended basis for the volume that we are actually underwriting, think about risk retention requirements in the form of, call it 1.5% today. Obviously, that will fluctuate over the cycle, but we're now, I feel, at the right balance and mix between the different funding sources.
But on top of that, what has changed obviously over the last few years as a result of improved credit performance and continued growth and scale in our business and institutional investor side of the network, we have the quality of the asset that we also retain has improved. And obviously coming out of a period where there were a lot of losses across the industry for us as well. But I think I would go back and highlight that think of us a little bit as a company that we're trying to build that has a fee generating engine on the top. You asked me the question, we earn fees 3.5%-4.5% for every $100 that we underwrite at the top of the house with significant operating leverage. That fee generating business is what we continue to expand and grow by adding more partners to the network.
And then there is obviously a capital engine underneath as a result of our reliance to ABS. So you always have to have some capital that you put to work. But right now we're at the point where those fees that come through and obviously the operating leverage that I mentioned from a cash flow perspective far outpaces the amount of capital that we need to put to work, which obviously we expect that to come back over time.
Perfect. All right. We've run a little over, but terrific recap. So appreciate it once again.
Thank you very much. Appreciate it for having me. Thank you.