Podcast: Retail POS lending is an opportunity for lenders, Pagaya president says

Whitney McDonald 13:45:11
Whitney, hello and welcome to The Buzz, a bank automation news podcast. My name is Whitney McDonald and I’m the editor of bank automation News. Today is June 25 2024 Joining me is Sanjiv Das, president of pagaya. He is here to discuss the power of data. Pagaya is banking partners and the evolution of POS retail lending. Thanks for being here on The Buzz.Sanjiv Das 13:45:35
Sure. Whitney, thank you for this opportunity. I joined pagaya About six months ago as president, and as you know, pagaya is a FinTech, credit solution provider. It has a two sided model. It gives loans to consumers that would typically not get a loan from their mainstream lender, pagaya approves these loans through an API interface with the mainstream lender, and then through a pre funded model, it sells those loans directly into an ABS structure. So pagaya is a two sided has a two sided model, consumers on one side, ABS, investors on the other side, and pagaya is in the middle. That basically facilitates loans to people that wouldn’t have normally received their loans through a mainstream lender. By way of background, I was CEO of caliber home loans before this, and had a great extent making sure that consumers got mortgages and consumers bought homes. Before that, I was at first data, which was a KKR owned company. I took, took that public along with a team of people at first data, which now called Fiserv. And before that, I was CEO for Citibank’s mortgage Division during the housing crisis.

Whitney McDonald 13:46:50
Great, well, lots of great experience as you kind of break into this role at pagaya. I know that you mentioned you’ve been in the role as president for about six months. I think you you started or took on that role in October, maybe talk us through what’s been going on the past six months? What have your top of mind? What have you been working on? Or what was your first orders of business? Well,

Sanjiv Das 13:47:12
one of the first things was that I realized banks really needed someone like pagaya to partner up with them, and so we have really sharpened our strategy with respect to complementing the bank offering. We announced our partnership with US Bank not so long ago, and have now extended that relationship from the US Bank Personal Loans business to the elevon point of sale business. We have now spoken to close to 15 banks, and have really, really strong institutional coverage with respect to the bank, so that that business is doing really well. Banks realize that in order to broaden the base of their offerings to consumers, particularly Americans who can’t get credit through normal mainstream institutions, they find pagar to be an excellent partner to complement with. So that’s been really my number one focus, and the second has been making sure that our value proposition is understood there by our abs investors. As you know, this has been a pretty volatile market with respect to interest rates, and so we’ve been making sure that we meet the needs of our abs investors. So making sure that the two sides of our two sided value proposition is strong has been my focus in the last six months. Great.

Whitney McDonald 13:48:33
Well, thank you so much for talking through that focus. And one thing that we can kind of dive into here is some of those conversations that you’re having with financial institutions and kind of broadening what you’re offering to them. Of course, we can’t have conversations nowadays talking to tech providers without mentioning AI and the AI infrastructure that you offer, maybe we can take a step back before we get into the bank conversation and talk a little bit about the innovation. How do you ensure that that your team keeps up with an evolving technology like AI, so that you can be offering tech that’s understandable, usable, that that clients can tap

Sanjiv Das 13:49:12
into? But right now, I would say that the fact that we make decisions that are based on real data that we collect from our financial institutions in a way that there is no human bias, but there is rules that have been codified are extremely important ways in which we have made decisions. Secondly, we’ve made sure that we continue to evolve how consumers will behave through different periods of stresses, as I’m sure you can tell, with inflation being high and rates being somewhat high, we have made sure that we modified our models to make sure that consumers across different asset classes, whether they are personal loans or auto loans or point of sale loans, that the behavior is something that we are monitoring across these different. Asset classes. So if, for example, we find that there is some stress going on in the auto side of our business, we will immediately translate that to the personal loan side, knowing that there is a certain hierarchy or a sequence by which consumer asset classes go delinquent. So we’ve been using a lot of our intelligence, using data, as I said, as opposed to human biases, to really understand how markets are behaving and how consumers are likely to behave. So to us, the use of data right now has been predominant in making sure that we really leverage our models, and understanding cross sectional data has been really critical. Instead of making sure that we focus more on avoiding consumer delinquency for a given set of consumer loans, that’s really been where our focus has been. Yeah,

Whitney McDonald 13:51:05
I mean, a lot of conversations right now around the the data is king, right? So leaning on those leaning on that data in order to influence those AI models. And a lot of financial institutions have a lot of data, but how do you tap into that and organize it? So yeah, that’s great. Maybe we can talk through now, what some of those conversations with your financial institution clients, or those that you’re you’re in talks with? What are they asking for? What are they looking for right now. What are some of those conversations entail? Maybe talk through some of those trends.

Sanjiv Das 13:51:38
There are these discussions have been really extraordinarily exciting. Whitney, it’s really interesting because the financial institutions, or the banks on one side, are really watching what’s going on with rates and really constrained in some ways, with where regulation is demanding higher regulatory capital for them on loans that banks feel are lower credit score for them, and so they find us to be excellent partners who will come in and complement their lending strategy. So there have been really intense discussions going on with banks about how pagaya can help them a lot more. And this is not hyperbole. This is what I’ve experienced in my last six months in meeting with several banks, Bank CEOs, many of them, my colleagues from my prior banking experience, they are all really interested in the pagaya solution across their personal loans businesses, their auto businesses and their point of sale businesses. They all want a second loan provider like pagaya. So at the highest levels, those discussions have become extremely intense because of both rate pressures as well as regulatory pressures. The second thing is, banks really love the fact that pagaya takes these loans off their balance sheet, sells it to the ABS investor market, but gives the customer back to the bank for them to be able to service these loans. So banks find that model to be really complementary to what they do, where they keep the customer and the customer relationship, but not the asset on which they need higher regulatory capital. Those discussions have been going extremely well. And the third thing I would say that banks and us have been extremely careful and diligent about making sure that our models follow all the right rules and regulations around fair lending. It’s not just about the loans we approve, it’s also about the loans that we don’t approve. So we want to make sure that when we don’t approve a loan, they have the right explanatory part about why the loan didn’t get approved. And we continue to make ourselves and our banks robust, because we have to meet the high standards that our banks and our that our banks have to our bank partners have to meet with. And so I feel really good about the industrial strength of pagaya to be able to deliver that, yeah,

Whitney McDonald 13:54:12
having that confidence in the decision making. I mean, explainability is key, even just from a compliance perspective. You have to have that explainability in place now, with those conversations in mind and kind of where those are leading and what ideas are coming to the table. How do those conversations spark innovation ideas, or drive innovation ideas within pagaya,

Sanjiv Das 13:54:36
yeah. So a lot of the innovation that we have right now is in the use of data, as I mentioned before, and I don’t want to make it sound any more exotic than it is, because data in itself is so powerful that understanding, for example, the data that is behind a bank’s existing customer base, as opposed to new customers or. In addition to new customers, is something that’s extremely valuable to us, and that’s been a new source of innovation in terms of our new product development and our new product design. So so far, pagaya has been a second look provider to new loans that a bank would originate. Now, pagaya is becoming a mainstream advisor to existing loans that a bank has, and that’s the innovation, because those existing loans, the bank already has performance data on them. So in addition to bureau data, we also look at Bank existing data, and to us that has been a great source of being able to open up the credit box to more loans for existing bank customers. So imagine if you were, let’s say, a Sofi, and you had a depository customer, and that depository customer had a FICO of 680 and SoFi had to say no to their own depository customer, that would be embarrassing, and that customer now gets a pagaya loan through SoFi and and, you know, so now the customer has a much higher degree of satisfaction with their primary lender and their primary depository bank. And so keeps that relationship with sofa and makes it stronger.

Whitney McDonald 13:56:35
Thank you so much for that example, it’s it kind of helps understand a little bit more what you’re actually accomplishing here with with padaya, and how things are are changing and evolving, and how the technology and the data is being used. Maybe we could talk take that a little bit further. How else are some clients tapping into pagaya now? Or what are some of those other use cases now that that clients are having success with

Sanjiv Das 13:57:03
Yeah, so I mentioned to you how pagaya works with banks. On the personal loan side, we not only work with traditional money center banks, but also the FinTech banks. I gave you the example of SoFi Lending Club. They’re examples of FinTech banks. The major money center banks being US Bank. Pagaya has also had deep relationships with auto lenders, so ally, for example. And the big thing that we are realizing in our relationships with with our lending partners, is that is that it’s not just about being able to provide credit, but it’s also being able to approve more loans that comes through their dealers, for example, or through their branches. So there’s a great deal of intermediary satisfaction when they don’t have to say no, and they can say yes to more customers. Now, the most exciting thing, though, has been in the last few months, and I gave you the example of elevon, is the rapidly evolving asset class, as we call it, or area of lending, which is point of sale. Klarna has for a long time been a big client of ours, but the Klarna small ticket loans that I’m sure you’re familiar with is obviously something that’s been a great, great example for us in the point of sale business. But we are realizing that there is a new form of as well, new for us, but it’s been there for a while now of asset class that’s emerging, which is basically retail like point of sale. So these are loans that are given for, let’s say, medical purposes, or loans that are given for education purposes, or loans that are given for home improvement. So let’s say you want to do a home improvement loan, so instead of applying for a separate home improvement loan, you essentially apply for a loan at the point of sale, let’s say, at a Home Depot, and that loan is given by US Bank. But actually that loan is at the back end, truly being given, approved by pagaya for home improvement purposes. But that loan. For that loan, the customer didn’t have to come to a bank branch for that custom. That customer got the loan, potentially at a Home Depot store, you know what I mean. So those point of sale loans that are larger in in size, 15, $20,000 sometimes longer in terms of duration, 18 months, 36, months, 60 months, as opposed to the small ticket items at a Klarna point of sale, where you had to add an at a digital checkout, you would have a Klarna option available to you when you’re checking out. This, these, these new category of loans, is the really new exciting asset class that is that, in my opinion, will be translational to lending in our institutions in the next few years? Yeah,

Whitney McDonald 13:59:55
the point of sale loans outside of a traditional institution is just one of those innovative avenues where you can get access to capital in a non traditional place, even like within a Home Depot, right? Yes, exactly No. That’s that’s different things that that are in place and you’re working on it. I’m sure seeing adoption tick up there. When you think about the either short term or long term efforts that you’re working on, what’s next for pagaya, what are you working on now? Or what’s next for certain AI, or how you’re developing or tapping into data. What’s next? What are you working

Sanjiv Das 14:00:37
on? Well, we’ve realized that we now have because we have 30 partners, and we have so much data, and we have such good understanding across asset classes, that our ability to scale up and to be able to deliver our solution to let’s just take banks for a second as a as a segment of lenders. It’s just such a massive opportunity that one could say we don’t even know what the market cap of this opportunity is going to look like what the TAM of this, of this opportunity is going to look like. Banks are going to continue to shrink their credit box bug guys continue to go to going to expand its partnership with banks for exactly the same reason. The other thing that’s really important is that as data and machine learning and AI techniques are improving, our techniques are also improving. And I’m sure you’ve been reading and hearing about different kinds of AI methodologies or machine learning technologies which have much greater explanatory power in terms of consumer loan acceptance or rejection. So we are spending a lot of time understanding the power of the underwriting process. And our hope is that as we continue to get better and better at what we do in personal loans and auto and then from auto to point of sale loans, that we will expand that same capability to all forms of consumer lending, including credit card someday, home equity loans, student loans. I don’t want to get ahead of myself, but it certainly is heading in that direction where we are truly becoming an expert in complementing financial institutions across all forms of consumer lending.

Whitney McDonald 14:02:32
Now one more question, and we can kind of get into the technology here. Let’s say you do have a financial institution interested in partnering. What does it take on the technology side in order to tap into the institution? What do they need to have in place?

Sanjiv Das 14:02:48
That’s a great question. So when we talk to a financial institution, we go through a pretty intense process of really ensuring, once we get past the value proposition of what pragaya does, really ensuring that our models are models that they are completely comfortable with, because the because the the consumer is assuming that the lender is the true lender, we have to act, and we are acting on behalf of the lender. We have to make sure that the model standards that we have are up to the standards that the lending institution would have. Second, we want to make sure that the integration of our models into the bank underwriting system, the origination system, is seamless, and so we go through a pretty intense onboarding process. Sometimes it takes Whitney eight to 12 months to really onboard the pagaya technology solution and and make sure that our APIs that are connected to the bank origination systems are absolutely seamlessly integrated, so that the pass through of a loan from a bank to us or from any lending institution to us, is seamless to the consumer. And then we make sure that the loan is approved in seconds, milliseconds, so that it is it basically runs through our our systems and gets approved or not. And then we want to make sure that the chain doesn’t stop there, that, as you know, the delivery cycle goes all the way from from once the loan is approved, to how the loan sits in the bank’s balance sheet for at least, you know, a couple of days. And then comes across to our abs funded structure in a seamless way. The master Trust, the ABS trust, are all sort of involved in this process. And then the loan goes back to the cons, to the to the lending institution servicing side, so it makes sure that the servicing is seamless. So also, it’s a non trivial technology integration process. But the beauty of this whole process. Is, once you’ve done it, then you are able to do two things. Number one, you you are in in the banks or the lending institutions technology infrastructure, so you’re part of their offering. And number two, once you’ve offered it to one side of a techno offer of a financial institution. Let’s say you’ve offered it on the personal loan side to extend it to the point of sale side is actually quite simple, so intense in the beginning, but pretty straightforward once you’ve done the hard

Whitney McDonald 14:05:48
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