Thank you. Hello, everyone. My name is Rahul Jindal. I'm the FinTech Strategist at Autonomous. We're very happy to have Gal Krubiner today from Pagaya. He is the CEO and co-founder of Pagaya. Prior to co-founding Pagaya, Mr. Krubiner focused on structuring and distributing sophisticated credit and asset-backed securities with UBS, as well as holding other positions that were specialized in investment, and he's been an entrepreneur for quite some time. As you know, fintech has been growing steadily for a decade now, and we've written quite a bit about lending, particularly, especially unsecured lending in the near and subprime space. This includes companies like Affirm, LendingClub, and Upstart. Pagaya is a unique fintech that operates at the intersection of two customers. On the one hand, they have lenders, companies like Klarna, and on the other hand, they have investors whose capital actually funds the loan. So we're very excited to have Gal over here to talk about Pagaya and how they differentiate themselves. So, Gal, let's start broad. What was the genesis of Pagaya? What inspired you and your co-founders to start the company in 2016? Rahul, thank you very much for having me today. I think highly of Autonomous in the different engagement we have, and happy to share today both my personal story as the founder of Pagaya and a little bit of what we're trying to achieve. To your very first question, I think it all comes from what is our mission, what is the vision. When you think about it, at the end of the day, we are trying to solve for one of the most important things in the U.S., per se, on the financial ecosystem, which is to allow for many Americans who are looking to get loans to actually help them do that and for millions of consumers to be able to achieve that. Now, let me start with two interesting facts. We have provided already $22 billion of loans to the U.S. consumers, and we have did so for over 2 million customers, which is a very, I would say, high number compared to how much people know about us, and the reason for that is, as you mentioned, we are a B2B2C business, so we are a white label solution that is working with the best lenders in the United States. Some of them you're very well familiar with, like Klarna, like SoFi, and even US Bank and Ally, which are a little bit more traditional players, and we help them to facilitate the ability to provide these extra loans to the people, and when you think about what we have built here and why we are so passionate about that, it's really the ability to help the consumers get the actual loans and the credit that they need. The way we did it is just in a very different way, which is more a B2B rather than a B2C. Yeah, no, it's an interesting model. So as we dive into that, and I think this is where a lot of our listeners, our investors are very curious to learn about this. So it helps lenders underwrite loans, your core product, which they would have otherwise passed on or may have already passed on. And it also helps capture the flow of customers, which sort of fall outside the traditional credit parameters of a lender. Am I saying this right? I mean, and I would love to kind of have you explain your core business model and how you generate revenue. Definitely. So the reality is that while we have a very sophisticated financial ecosystem in the United States, every lender has its own preferences, has its own what you will call the credit box. Now, it doesn't mean this credit box is better or worse. It just means that for the right balance sheet that they have and regulatory oversight, they prefer over the time to focus on a specific segment in the market. But if you think about it from a high level, it creates a lot of inefficiencies because you spend so much money in marketing. You have branches sometimes. People are coming to you. And because you are a little bit more focused on a subsegment, there are many others that, as you said, will going to be passed on. So you will not give them an actual offer. Many people think about it as someone who didn't get an offer because a model got declined. But the reality is that they are not even going through the credit checks because of very hardcore cutoffs that the organization is setting for strategy perspective and where they want to focus rather than if that specific borrower or a specific customer is a good or bad customer. And what we said is when we are going to connect to these lenders, we are going to see the full flow as it comes and will extend their ability to actually approve customers, even if it's not in the very core strategic view of that organization. The best way to describe it is to take an example. So let's take Jane, which is a person that goes to one of the branches of US Bank or somewhere else. She might go in and for some reason could be that the FICO cutoff of US Bank could be a 700 or 720 for the personal loan for different reasons for them internally. In that situation, we are actually getting all the information of Jane and the loan that she requested rather than just tell her, "Hey, sorry, we cannot review your loan." We are running that through a very strong AI machine learning that we have that is built from all the aggregated data that we got from all the lenders. Then in many cases, we can go back to Jane and say, "Hey, Jane, you got an approval for a loan." Now, that approval is going through US Bank. So Jane doesn't even know that Pagaya exists. But what we created here is a very efficient world where Jane is already in the branch. She wants the loan now. She doesn't want to go home, take it online, looking for something else. She's going to have a bad day if she got a no, so we are helping her streamline that. I call it the secret angel that is allowing her to get the good day and the good outcome that she was looking for, and then obviously, she appreciates US Bank much more, and she wants to keep and stay a customer of US Bank for many years to come, and the connection between US Bank and the customer is becoming better and stronger within time rather than she is going to leave for a fintech competitor, so all of that to say that it's a win, win, win. To your last point, the one who is financing these loans are institutional investors that have a lot of, I call it the pension balance sheet of the world and the insurance balance sheet of the world, but they don't have the same connectivity to consumer credit as the bank's balance sheets are having. We are coming and connecting these dots in between the pension money and the insurance capital to the actual people that deserve to get that loan so much. Yeah, I mean, that's the good part about the business. I want to dive into this. Before we get there, I want to go back to the point that you raised about this sort of white label solution that you have. When I was quite young, somebody explained to me the two ways to make money in credit. Either you underwrite better and/or you acquire customers cheaper. So you've obviously got on the second part, you've got this white label solution where I'm assuming that your CAC is obviously delegated onto the lender entirely now. How do you think about this from the perspective of price defensibility and contract modes in the long term if your customer relationship is actually in the hands of the lender, not Pagaya? So I want to think about it even more holistically. What we are doing is we are helping all the banks and the partners that are in our network to improve their CACs. Because if you think about it from a pure math perspective, the marketing dollar that they spend doesn't increase, but the revenues that they generate do increase. So by definition, we are creating a better CAC for all of our partners. Now, from a mode perspective and from where we are coming to play, the fact that over time we are learning better and better the behavior of these customers, the marketing channels that the people are having, the different behavior in the different channels is allowing us to feed it to the AI. And that's where algos and algorithms make a big difference. And we are increasing over time our approval rates to make sure more people are going to get our offers. Now, that learning, like ChatGPT, is very much specific IP related to Pagaya because the millions of customers that we learn what they like, what they don't like, which brands, which offers, how they perform, how they perform in different times, it's a very strong IP that builds the moat. The best example for that is Capital One. Capital One cracked the code on credit cards subprime or near prime, to say, in a way that very few companies in the U.S. managed to do it. And this is the same type of moat that we are collecting here in Pagaya through the 31 plus partners that we have now and in the future many more. That makes sense. Now, obviously, you're working with a series of different partners. You've got Klarna, it's a BNPL, SoFi, unsecured, Ally, which is predominantly in the auto space, you're working with them. Do you see any particular lending channels that are taking predominance now or into the future? So maybe let's spend one more second on how we make money, and then we'll go into what do we think are the biggest contributors from a market perspective to the Pagaya future. So the way we make money is we are actually getting a fee for every loan we place. So think about it, that the lenders are actually happy with the offering and with the fact that they managed to provide financing to their customers. And therefore, they are paying us somewhere between 3%-4% on every loan that we generate. It can be as high as 6%. And as we move over time, we are adopting more and more partners to open more channels and to push for the higher fees, per se, because they appreciate the value that we bring to their customers. To your point, Pagaya is working in different markets while all of them in lending, which varies from the personal loan, the auto loans, and the buy now, pay later. Now, the personal loan is the place where we started from. I would say that 60% of our production is there. But we have experienced a very interesting in auto thereafter, so that's the second biggest. But we have experienced a very interesting phenomenon from what is being called the fintech buy now, pay later, what is being called in the bank's point of sale, that the growth that is happening, which is combined a lot with the e-commerce boom, is actually one of the fastest credit markets that is growing very rapidly. Now, the interesting dynamic that we see there, and I know from a strategic perspective, you're always interested in who are the players and what's happening. I would say that buy now, pay later or point of sale, however you want to call it, is maybe the area that we see the most amount of investments that are coming from the banks. So not just that we had and have the phenomenon of Affirm and Klarna. And Klarna, obviously, is a very strong and close relationship of ours that we are growing while we speak to very interesting numbers and continue to grow that. We see many banks that are tapping on and investing a lot of technology and actually calling us to help them try to get that to the next level. So, if you will ask me, based on all of that, there is a very strong probability that as we move forward, buy now, pay later is going to be a much bigger pie of the Pagaya production, of the credit in general, and definitely a market that is booming right now. Thanks for that. That's very helpful. Let's shift to the other side, the other set of customers now, your investors. So congrats on the AAA rating on your ABS product. Can you share your perspective on the funding market now, the funding environment? So, I think if people are interested in one of the most interesting places that we have as a Pagaya is that we are very big on the capital markets. We are the biggest personal loan ABS issuer in the United States. And on the other side, because we are connected to 31 partners, we are usually seeing cross-country trends first on the credit side. So these two very big markets, which is the consumer credit market and the capital markets, is things we have very strong insights into them. And I can share with you a little bit of things that I think are a little bit less understood these days because the Fed and a lot of incoming data is confusing so many people of where we stand and what we do. So I can share only data, but very important data, I think, for people to appreciate. So let's start with the funding side that you just mentioned. So we just priced yesterday and about to close when it's obviously public our latest ABS transaction, which had the cheapest cost of capital, I think, since 2022, and with the lowest amount of actually equity or dollars that we need to retain because all of that is becoming so much more efficient. And what we saw is tight spreads. And what we saw is strong demand even on what we call the junior pieces. So I will say, if you will ask me, that the funding markets are actually functioning the best since the Fed started to increase the rates. And the reason for that are twofold. One is what we spoke about from the fact that the Fed is starting to lower rates and people and investors looking to lock the high rates as they go further, and I would say that the first half of the year 2024, the story was the investment grade spreads coming down. Now we're actually talking about demand that are coming on the junior pieces too. So very healthy environment or much healthier than it used to be in the last two to three years. But the other piece of why spreads are in that place is that the production of the personal loans and the performance of the personal loan and actually the auto loan too of the latest vintages are coming in line with expectations or even better, which is something we didn't see since 2022. Just to give you a small nugget that from a Pagaya perspective, and I'm speaking about the Pagaya production, we define the how on the auto loan, the earliest indicator of performance is how many loans are late at least 60 months after 60 days, sorry, after five months that we have originated them. Today, you know, what was the performance of the February 2024 production. When you look on these numbers, which we call them the CGL, and we publish them from every now and then, we are actually seeing that it is at the lowest level since 2021. It's been constantly improving since 2023. Now, then you will ask yourself, Rahul, but, God, how all of these noise is happening that people think that maybe the economy is weakening, et cetera. What usually is happening is that sometimes banks and others have portfolios. So while you can see the liquidity is going up, it's still from a past production that doesn't really reflect the new production and the actual stability of the consumer as we are speaking about the origination as we see today. And if you will ask me, as financial conditions are becoming better, to your question about the funding markets, the ability to refinance for many borrowers is becoming higher. And therefore, while I don't have a future vision, if we're not going to see a very high unemployment, which now is unlikely, the next few months and a year actually should be in the same trajectory, which is a strong performance of the consumer. I showed you how the link goes between the funding markets that are becoming healthier both from the Fed perspective and where we are going on macro, but at the same time, because the performance of the latest vintages since 2023 is becoming better and better to the point where people are seeing that this is a different world. I gave you specifically the linkage to the auto loan versus a lot of noise that exists out there. I hope that, Rahul, was good for your communication. It certainly helps. I mean, the two bets we're making here is the current, or let's say the six or two quarters plus minus worth of a cohort is a healthier cohort to underwrite versus the ones probably written some time ago, and the job market doesn't deteriorate so if that holds true, then we should have a reasonable time to underwrite. I would say even more than that, Rahul. Just let's comment on that. Even on unemployment, unemployment is a potential downside for consumer credit when you speak about very high spikes. If you see unemployment gradually moving from four to four and a half to five, you have time to price it in. You can increase interest rates. You can select different pools. I think the personal loan industry all definitely after 2021 have already known how to trim down these things. The one thing that we don't have the ability to react to is if unemployment moves from four to ten. Then it doesn't matter how smart you are. But as you said, it doesn't feel like these are actually worries that are emerging as we speak. Yeah, no, that makes sense. I wanted to ask you a couple of things about tech, but since we're on the topic, I'll dive a little bit into this. So one of the questions we get from our investors about Pagaya is, how does a company perform? How does this company perform through a full credit cycle? So understanding that rate cuts are on their way, and hopefully there is no recession, but if there is, it's a soft landing and so on and so forth. But the main question is, how does Pagaya perform given that you have exposure to lenders that are obviously lending in the near prime, thin, subprime sort of market space? If they start retrenching, and the ABS market, obviously, like you've just laid out, seems to be pricing in the risk a lot better. It's a lot more fluid. What are the risks that you see if things do turn towards a soft landing? So I think the way we operate and how we would like to think about it is that we are always the balancing act. So these markets are actually reacting to each other. So when you will see, for example, a higher cost of capital on the ABSs for whatever reasons or worries, usually you can price it into the loans and to the borrowers and the consumers and vice versa. And what we like the most about our business model and the place where we are standing is that while we are providing, so to speak, credit to subprime and near prime, I do want to unpack that a little bit for two main data points. One is the duration is very short. You're not talking about giving mortgages of 10 years for these things. Your ability to course correct after three, six months is very, very doable, and it's impacting most of your book very quickly. From a post point of sale that is only four to six months, personal loan that is usually 1.1 to 1.5 years duration, and even the auto that is on the high side is only two to two and a half years. After three to six months that you see things are changing, you can very much react. Therefore, the system, Pagaya and the lenders are building to a way to course correct as things emerge. The other piece is that the borrower and the consumer we are facing with is much stronger than people think. We are talking about a consumer that is earning $115,000. It's a consumer that has at least 17 years of credit history. It's many of them people that have mortgages, people that have many open credit lines, so the vast majority of the people we speak with or the people we lend to, sorry, are actually people that have higher, stronger ability to pay even if a cycle coming through, now you need to be prudent. Your model needs to adapt all the time. As you can tell, our conversion is very, very tight as we are keeping all of these things very intact, but I think from an industry perspective, and especially after what we've been through, the highs of 21s with the simulations and after that, the processes become more tight and everything now is buttoned up to a point where I will say there is a strong resilience capabilities in different scenarios. Thanks. That makes sense. And of course, you are pre-funding, which is another unique part of the business model. These loans are pre-funded. You're raising the capital before you actually lend it out. Let's shift to the revenue side a little bit. I know we discussed the two ends of your revenue. There's the fees from the lenders, and then there's the fees from the investors. Now, what I'd like to focus on is there's been a meaningful shift in the last nine quarters where you were 99% fees from funding from your funding partners in 2022, and you're down to 31% in quarter two, which you reported in 2024. So in the last nine quarters, the coin's completely flipped here. Do you think this is where the ratio normalizes? Can you shed some light on the mix and the contribution margin of each and what you think that's going to look like at scale or maturity, however you define it? So I think I will describe it in three main comments. One is maturity of a company and maturity of a product. When you are penetrating with a new product to a market, you usually price it low, and as time goes by, companies and people see the value, and you can increase prices over time for the value that you bring. So I would say that the fact that you didn't see, nine or ten years ago, much fees on that side was not because we didn't intend to do that, just because that's the right strategy of how you build these things to the right place. And then to your second question, it's like, how do you think about this mix, margin, et cetera? From a margin perspective, we think about it the same, so it doesn't matter if it's from here or there. But it goes a lot to your question before. We have a business model that is resilient to earn fees either here or there. When there is over demand here, we have the ability to take here. I mean the investors. We have the ability to charge the investors. When you have the over demand on the partners, we have the ability to charge the partners. I will say though, and that's my third comment, that we are thinking about that today in a much more stable recurring way. What I mean by that, I mean that when you are becoming a public company and you are wanting to have strong, long-lasting relationships with 30, 40, 50 partners, you need to have a very strong ability to predict what's going to be your outcome both for yourself, for the partner, and so on and so forth. That actually what brought us to beat guidance or beat-and-raise, however you want to call it, in the last eight or nine quarters. Now, that means that we owe that stability to our partners as part of the service we provide. It's so natural that these agreements are becoming fixed fee with the partner that everyone knows about. It's the same evolution that Best Buy and Citi has been going through for many years, United and Chase and so on and so forth. The ability to create a very strong, sustainable, predictable model is going through what we call the partner fees. We did give a range, which is the 3.5-4.5, and that's what we think is the long-term number. We just raised it from 3 to 4 because we see a strong demand. We have partners with up to six points, and we are targeting and trending towards these numbers as we build the company, as we build scale. Got it. That's very helpful. I want to shift to your tech. We'll go to one question on the regulatory side, and then I want to leave a couple of minutes just for audience questions as they started pouring in, so on the tech side, you obviously differentiate yourself by having an AI-based underwriting model. You're assuming you've got your sets of weights and tokens, and you're doing all of the detail on the data. How do you measure comparative accuracy and outcomes for your customers, so when I say customers, I'm talking about the lenders, and what do you think the adoption curve is going to look like from your perspective? I know, Gal, we've talked about this in the past in selling this as sort of the way of the future to banks, the middle office in the banks. So maybe let me start with your last question as first because I think that's the most visionary and important piece. We are at the forefront of bank interaction to the question of which technologies and solutions should be observed by banks to provide better outcomes for their customers. And I want to say that when I came to the United States in 2018 and started to deal with banks in 2021, I would say that the last year has been a major shift in how banks are thinking about technology and technology implementation. I contribute a lot of it to the ChatGPT, but I might be wrong. I think we are at the point where fintech has become not anymore a disruptor or a different category, and much more the way banks are thinking. We gave the example of the buy now, pay later, which I think books will be written on the amount of money that is being spent on these things to become the new way for the American consumer to consume credit. And I see that in many more places where the maturity of the fintech industry has reached to a point where even banks are feeling comfortable engaging with fintechs. We invested a lot of time and effort in sorting out and nailing down all the adverse actions and the discussion that the CFPB and the OCC and everyone should expect from us. And we got it to a level which is in line with the AAA banks such as US Bank and others. The fact that the fintech industry has moved to that place and embracing that and not looking anymore on banks as a competition, but as an enabler, we are the generation of enabling the banks rather than disrupting the banks. I think we'll accelerate the technology and the implementation of tech in the financial industry by a tenfold in the next 10 years. Last sentence to that, in many of the McKinsey's, BCG's, and all of that reports, call it three, four years ago, it was always banks are slow to adapt, and that's why disruption is happening, and yada, yada, yada. In our view, the reason why the disruption is still only on its early days is because banks didn't take the full baton. If we as a fintech industry will focus about doing whatever they think is right and help them to get to that place, the impact, and don't forget, we are about impact at the end as an industry. The impact that we can have on millions of Americans is 10 times bigger. So I encourage more companies to go not just through the B2C and to find ways to do that, but to go through the core heart of the financial system in the U.S. that is going through banks. I do believe that the era of that importance is before us and not after us. I really appreciate that. I'll ask one question, and I don't have to call it a day from the audience. I'm sure you get this, Gal, all the time. How do you kind of protect against adverse selection where you're not just getting customers from the banks or from your lenders in general that are just low-quality customers that are getting shuffled to Pagaya? What's your answer to that? Great question. And the answer is embedded in the way we operate with the banks and the lenders. This is not an adversary relationship. It's a partnership. It's almost a joint venture. What does that mean is that when they say no to someone, I get the reason. Why? And then our ability to assess if we are in agreement based on our models and the other basis of the actual reason of decline is very helpful to the understanding of an adverse selection. The adverse is usually being generated when one has better information. He decides if to approve or not based on information that you do not know. And then you assume it's a good borrower, but you actually happen to not know that it's a bad borrower while he knows that. And that asymmetric information over time is what is being described as adverse. But the reality is there is no adverse. There is asymmetrical information. When you solve this, so you can ping or mark the different risk-reward in the different part by what your lender or partner thinks, and then it's becoming much easier to avoid that, and to remember, their success is our success. We approve more customers, it's more customers to them, so there is a very strong collaboration in doing so, right? And as you can tell in our performance, we don't have any adverse in the past. Thank you so much. I think we're right on time. Really appreciate all of your thoughts and insights, and I hope to see you soon in the future. Thank you so much, Rahul. It was really a pleasure to be here. Thank you. All right, everyone. Thanks very much.