Upstart Holdings, Inc. (NASDAQ:UPST) Q2 2025 Earnings Call Transcript

Upstart Holdings, Inc. (NASDAQ:UPST) Q2 2025 Earnings Call Transcript August 5, 2025

Upstart Holdings, Inc. beats earnings expectations. Reported EPS is $0.36, expectations were $0.27.

Operator: Good afternoon, and welcome to the Upstart Second Quarter 2025 Earnings Call. [Operator Instructions] As a reminder, this conference call is being recorded. I would now like to turn the call over to Sonya Banerjee, Head of Investor Relations. Sonya, please go ahead.

Sonya Banerjee: Thank you. Welcome to the Upstart earnings call for the second quarter of 2025. With me on today’s call are Dave Girouard, our Co- Founder and CEO; Paul Gu, our Co-Founder and CTO; and Sanjay Datta, our CFO. During today’s call, we will make forward-looking statements, which include statements about our outlook and business strategy. These statements are based on our expectations and beliefs as of today, which are subject to a variety of risks, uncertainties and assumptions and should not be viewed as a guarantee of future performance. Actual results may differ materially as a result of various risk factors that have been described in our SEC filings. We assume no obligation to update any forward-looking statements as a result of new information or future events, except as required by law.

Our discussion will include non-GAAP financial measures, which are not a substitute for our GAAP results. Reconciliations of our historical GAAP to non-GAAP results can be found in our earnings materials, which are available on our IR website. With that, Dave, over to you.

David J. Girouard: Thanks, Sonya. Good afternoon, everyone. Thank you for joining us today. Before I begin, I want to welcome my co-Founder, Paul, to the call today. As most of you know, in May, we hosted our first Investor Day, what we call AI Day. For many investors and analysts who cover Upstart, it was their first exposure to Paul. Unsurprisingly, AI Day generated a lot of interest in how he and his teams are creating the world’s leading AI lending platform. After the event, many told us they’d like to see and hear more from Paul, so we asked him to join our quarterly earnings calls. You’ll hear from Paul in just a bit. On to the update. On our call a year ago, we provided the first signs that Upstart was returning to growth mode.

And today, you can see it in full bloom. The second quarter was exceptional for Upstart. In addition to achieving triple-digit revenue growth, we reached GAAP profitability a quarter sooner than expected. Additionally, our newer businesses, Home and Auto, actually accelerated off the amazing growth you all saw from them in the first quarter. Originations on the Upstart platform in Q2 were $2.8 billion, our highest volume in 3 years. Revenue in Q2 grew 102% year-on-year, helping us deliver positive GAAP net income for the first time since Q2 2022. Our Auto business grew 87% sequentially, while our Home business grew 67% sequentially. While this friendly sibling rivalry tends to go back and forth in terms of growth rate, I can happily say both businesses accelerated meaningfully from their Q1 growth.

For the first time ever, more than 10% of our originations came from our newer businesses, including our small dollar loans, which grew 40% sequentially. Our teams couldn’t be happier. After a long period of super focused execution, it all just seems to be working right now. Once again, our growth last quarter was not a result of dramatic macro improvements or Fed rate decreases. In fact, the Upstart macro index has been largely stable for several months now. Our growth was primarily on the back of model improvements, which helped to drive conversion rates from 19% in Q1 to 24% in Q2. These wins came first and foremost from Model 22, which we launched in early May. Paul will share more about our model advancements shortly. In addition to our ML team, our growth and operations teams continue to do amazing work to drive down the cost of acquisition and origination.

These are technology-driven economic wins that result in a superior product for the consumer and a sustainable advantage for Upstart. As I mentioned earlier, our emerging businesses are growing really quickly. Small dollar loans and Auto each crossed $100 million in quarterly originations in Q2, and we expect Home, the new kid on the block, to follow soon. Our newer products collectively drove almost 20% of new borrowers on the Upstart platform in Q2. For each of these emerging products, we’re now reaching the point where credit history is sufficient and volumes are substantial enough for third-party funding. In fact, we have a goal to transition most of the funding for these products off our balance sheet by the end of 2025, though deal timing is always hard to predict.

It’s worth noting that our Auto Retail product, that is our software installed at car dealerships, has really gained traction and momentum in the last couple of months. This product has always presented unique challenges relative to our others, and it’s clearly taken Upstart some time to get it right. Several months ago, we took the decision to narrow the focus of our software on an exceptional financing process, and this focus has paid off in spades. The dealership adoption right now is like nothing we’ve seen in the past, and the volume of loan requests and closed agreements from our dealer partners is on a steep climb. This is a recent phenomenon, and I expect we’ll share more about it as it plays out. In our Home business, we’re increasingly confident we’re on a path to building the best-in-class HELOC experience.

Home is a massive and fragmented category with few players versed in AI and its amazing potential to power superior home lending products. In Q2, we launched instant property verification, with the first applicant completing the entire verification process in under 1 minute. Our system automatically verified their identity and income, assess the property’s value and any existing liens and confirmed ownership and vesting information, all the key steps needed to close the loan. I believe this speed and efficiency in what is normally a slow handcrafted process is without precedent. We continue to strengthen the funding supply on Upstart’s platform. Our funding partnerships have been both durable and scalable, allowing us to grow rapidly while delivering the target returns our partners expect.

With respect to banks and credit unions, we expect to reach a new all-time high for monthly available funding in Q3, surpassing our prior peak from early 2022. The funding markets continue to improve as the year progresses, particularly since the Liberation Day fears in early April subsided. In June, we priced and closed our second ABS deal of 2025, delivering significantly improved execution compared to our first, which closed in April. It’s worth noting that the more recent transaction had nearly twice the number of investors as the first, including some new names. We feel increasingly confident that these committed funding partnerships can scale with our business as needed and will play an important role as we begin to commercialize our newer products.

Before I turn the call over to Paul, I’ll share a few final thoughts. Looking over the last couple of years, we’ve done a lot of work to run our business more efficiently and streamline our cost structure, but we had conviction that investing in much larger Home and Auto opportunities made sense. These categories are ripe for AI disruption and they’ve expanded Upstart’s TAM by more than 10x. Our considerable investments in Home and Auto are really paying off with fast growth, strong credit performance, rapidly improving separation and commercial readiness with 9 lending partnership deals recently signed across one or more of our secured products already. To be clear, our goal is market share leadership in each one of these product categories in the future.

As our CMO, Chantal mentioned at AI Day, we’re building the always-on everything store for credit, aiming to persistently underwrite 100% of Americans with the best credit products in the world just to click away, and we’re off to a great start. Thanks. And now I’d like to turn it over to Paul, my Co-Founder and Upstart’s Chief Technical Officer. Paul?

Paul Gu: Thanks, Dave. Our aim at Upstart is to win by having objectively the best rates and process for borrowers and technology, specifically AI is how we do that. To that end, I want to highlight several areas of recent progress. First, we’ve continued investing in our core AI advantage. Model 22 made use of neural networks at every level of the model architecture, whereas prior models only made use of neural networks in the base layer. That may sound like a subtlety, but it increased our separation accuracy advantage over our benchmark textbook credit model by 17 percentage points to 171.2%. Equivalently, it decreased the inaccuracy remaining to be solved to 87.5%. This is a metric where the starting point is the benchmark textbook credit model I described back at AI Day and 0% would be a model that gets every credit decision perfectly right.

As you can see, there is a long way to go, but fortunately, we have a commensurately long road map of model improvement ideas to get there. As of the end of Q2, core underwriting had 91 million borrower repayment events to train on, up from 86 million at the end of the prior quarter. To support the larger and more complex models, we invested in further parallelization and cache-ing solutions that cut up to 17 seconds of latency off borrower pricing and saved on model costs. Those time and resource savings can now be reinvested in yet more powerful models. Second, servicing is the newest frontier for us and realizing loss reductions via best-in-class servicing has been a major focus. Over the past year, including the most recent quarter, we launched numerous improvements and optimizations to how customers can pay, how much they pay and when they pay.

As a result, year-over-year population-adjusted delinquency rates are down 20% and raw delinquency rates are down 32%. Machine learning is already informing many of these optimizations and will soon allow us to determine the causal impact of servicing actions we take. This will include assignment of specific agents, hardship programs or settlement offers to specific borrowers. We also plan to apply machine learning to the problem of individualized recovery prediction for the first time ever, replacing a fixed assumption about an economically significant portion of loans’ cash flows with machine learning. Servicing wins directly improve loan loss rates on loans, which in turn improves the pricing and approvability of new loans. Third, we made strong progress in Q2 generalizing our AI technology across product verticals.

I want to start by noting that even with accelerating growth in new products, our share of fully automated loans actually kept up this quarter. That will be challenging to keep pace with, but we’re encouraged by wins we had across new products. As Dave mentioned, HELOC had its first instant property verification, which involves solving for over a dozen facts or documents that previously required waiting for a manual verification. In Auto refi, we launched full automation of the remote online notarization process. Both of these wins remove major procedural barriers to model-driven automation, which we’ve seen relentlessly drive the percentage of loans fully automated up in core personal loans over the past few years. Our growth in auto has been supported by and coincides with strong advances in generalization of our core underwriting technology.

Auto is the first area where instead of directly training an auto model, we start by training a foundational credit model on data from multiple credit categories and then apply fine-tuning to arrive at an auto-specific model. We are now working to add embeddings to the auto retail model, along with generalizing what we call “APR as a feature” and our macro framework from personal loans. This type of model generalization is powerful because it means all of our loan products can learn from repayment patterns observed across our platform, not just within their individual category. Lastly, I want to touch on generative AI and its applications to our business. I’ll start with the table stakes. Like any good tech company, we’ve realized solid productivity wins from application of large language models to our internal operations.

60% of our developers are weekly active users of LLM-powered developer tools and teams all across the company have built over 700 custom GPTs to automate various internal workflows. More interesting are the applications to the end borrower. We’ve already launched early versions of borrower impacting generative AI tools around model explainability and customer service. We will continue to build on these with an eye towards eventual agentic management of our consumers’ credit needs. As Dave has discussed, one of our key priorities in 2025 is to 10x our leadership in AI. We continue to have a robust pipeline of modeling wins, and I’m incredibly proud of the team and what we’ve been able to accomplish so far. With that, I’ll turn it over to Sanjay.

Sanjay?

Sanjay Datta: Thanks, Paul, and thanks to all of our participants for sharing some of your time with us today. I’ll now spend some time giving context on our numbers. With respect to its impact on financial performance, the credit environment we operate in was largely a non-story in Q2. The emergence from last quarter’s tax seasonality played out roughly as expected. The broader macro has been idling in regards to its impact on credit trends, registering as neither a significant headwind nor tailwind over the past 6 months. As Dave alluded to, the strong sequential momentum we achieved in Q2 is largely due to the strength of our model launches during the quarter. In addition, take rates and contribution margins increased in the core personal loan business, although in our aggregate numbers, these dynamics were partially offset by the continued rapid scaling of the newer Home and Auto products, which still have immature unit economics.

The combination of these effects allowed us to beat our guidance across both top and bottom lines in Q2 and break through to GAAP profitability a quarter earlier than anticipated. We have been able to comfortably fund the ongoing growth in the core personal loan business through our existing lending relationships and capital structures. The main source of pressure on the balance sheet as it currently stands is from the continued scaling of the new products and an increasing priority for us this year will be to finalize and implement our third-party capital plan for these new products. With this as context, here are some of the financial highlights from Q2 of 2025. Total revenue for Q2 came in at approximately $257 million, up 102% year-on-year.

This overall number included revenue from fees of approximately $241 million, which was up 84% year-on-year and 15% better than guidance. Within this, transactional revenue more than doubled year-on-year, largely reflecting the influence of the aforementioned Model 22. Separately, servicing fee revenue grew by nearly 20% year-on-year as the outstanding book of serviced loans continued to expand. Net interest income represented roughly $17 million of overall revenue, ahead of guidance by $2 million, reflecting the growing volume of new products being incubated on our balance sheet and in particular, the Auto book of loans where our return on investment has meaningfully strengthened. The volume of loan transactions across our platform was approximately 373,000, up 159% from the prior year and 55% sequentially and representing just over 250,000 new borrowers.

Average loan size of approximately $7,570 was 15% lower than the prior quarter as model advancements drove higher approval rates in smaller loan amounts. Our contribution margin, a non-GAAP metric, which we define as revenue from fees minus variable costs for borrower acquisition, verification and servicing as a percentage of revenue from fees came in at 58% in Q2, up 3 percentage points from the prior quarter and exceeding guidance. This improvement reflects a strengthening take rate in our core borrower segment in addition to the acquisition and operational unit cost efficiencies driven in part by Model 22. GAAP operating expenses were roughly $252 million in Q2, up 16% sequentially from Q1. Expenses that are considered variable relating to borrower acquisition, verification and servicing were up 21% sequentially relative to the 55% increase in volume of loan transactions, supporting the higher contribution margins previously referenced.

Fixed expenses were up 13% quarter-over-quarter, largely reflecting a onetime catch-up in the compensation-related accruals, which on the current business trajectory, we expect will normalize in the back half of the year. Q2 GAAP net income was approximately positive $6 million, well ahead of expectation and reflecting outperformance on fee revenue against our tightly managed fixed cost base. Returning to GAAP profitability has been an important objective of ours over the past year, and I am proud that our team has reached this milestone ahead of schedule and while subsisting in the persistently high default environment that still surrounds us today. Now that we are over the line, we will look forward to continuing the positive momentum of our bottom line and to improving our profitability profile as we scale.

Adjusted EBITDA was $53 million, also scaling nicely in accordance with our operating leverage. Adjusted earnings per share was $0.36 based on a diluted weighted average share count of 118 million. We ended Q2 with approximately $1.02 billion of loans held directly on our balance sheet, up from $815 million in Q1. This sequential increase is mainly due to the continuing growth of our new products, which have all simultaneously entered the transitional period between R&D and commercialization, a period in which we must ramp deliberately in order to demonstrate credit performance and our ability to deliver meaningful volume before obtaining third-party funding commitments. In this regard, we are in a bridging period with these new products, which is precipitating what we expect to be a temporary expansion of the balance sheet usage that we intend to reverse as these products exit incubation.

As Dave mentioned, we have already begun the process of securing external capital to support these initiatives, and we believe these efforts will allow us to transition away from direct balance sheet funding of these in the near term. As we plan for the back half of the year, our macro assumptions remain consistent with our prior view, which is to say a steady environment with the UMI continuing in the 1.4 to 1.5 range, steady interest rate levels and a labor market that remains resilient in the face of unpredictable policy shifts. Inflation will remain a near-term risk. In this environment, we expect to continue to launch model enhancements that will improve conversion rates, our take rates and contribution margins will remain robust, and we will continue to scale and fund the newer products.

In this scenario, for Q3 of 2025, we would expect total revenues of approximately $280 million, consisting of revenue from fees of approximately $275 million and total net interest income of approximately positive $5 million. Contribution margin of approximately 58%, GAAP net income of approximately positive $9 million, adjusted net income of approximately $44 million, adjusted EBITDA of approximately $56 million with a basic weighted average share count of approximately 97 million shares and a diluted weighted average share count of approximately 105 million shares. For the full year of 2025, we now expect total revenues of approximately $1.055 billion, consisting of revenue from fees of approximately $990 million and net interest income of approximately positive $65 million, an adjusted EBITDA margin of approximately 20%, and we expect GAAP net income of approximately positive $35 million.

These numbers are, of course, the outcome of a lot of hard work and great execution by the various teams across Upstart. So I’ll take this opportunity to both thank and congratulate all of those teams. And with that, operator, over to you to kick off the Q&A.

Q&A Session

Operator: [Operator Instructions] Our first question comes from Peter Christiansen with Citi.

Peter Corwin Christiansen: Really great results. Dave, Sanjay, I just wondering if you could chat about ABS — the health of the ABS market. Are you seeing any appetite for equity tranche investments at all? And then I’m curious if you had any thoughts on more competitors coming into the more near prime space, also the prime from a loan platform perspective. Are you seeing any competitive pressure there?

Sanjay Datta: Pete, Sanjay here. Great to hear your voice. The ABS markets, let’s see, we — I think have returned to being a somewhat regular issuer now on the cadence that we would like. I think the bond market is very constructive. The residual or equity market that you asked about I would call it an opportunistic market right now. There are buyers. I don’t think they — it is an efficient market, and I think buyers sort of pick and choose deals. So I wouldn’t call that market “back in the way that I think the bond market is”. But overall, it’s a constructive market, and we’re certainly happy to be a regular issuer again.

David J. Girouard: Pete, this is Dave. I’d just say on the competitors, to the extent the funding markets have improved and capital markets have improved somewhat through the year, I think that does tend to bring more competitors into the space. So unsurprisingly, it’s a fairly competitive game these days. But again, we’re very focused on having best offers, both at super prime level and at our core business as well and also very confident in our ability to grow our market share and keep our strength in those markets as well.

Operator: [Operator Instructions] We’ll take our next question from Ramsey El-Assal with Barclays.

Ramsey Clark El-Assal: I wanted to ask about the increase in the loans on the balance sheet. And Sanjay, you mentioned that those you would start transitioning to external funding in the near term. I just want to kind of zero in on what that means exactly. Should we expect next quarter that amount to begin rolling down, decreasing? Or is it going to take a little more time to line up the external funding category? And I guess what will be the pace of decline there that we should expect over the next few quarters?

Sanjay Datta: Yes, Ramsey. Yes, as I mentioned in the prepared remarks, a lot of the volume on the balance sheet today are from our new products. So our core business, I think, is well funded. Categories like Home and Auto are growing quickly. So it’s a bit of a good problem to have, sort of the original use case for the balance sheet, which is R&D and incubation. In terms of time frame to get the flows moving to third-party capital, I think we’re looking at a time line that’s sort of roughly between now and the end of the year. And a lot of that is just about new originations and getting that flow to capital sources. I think as we make those deals, obviously, we’ll opportunistically use our balance sheet to seed those relationships. So I think as we sort of transition the new flows, you should see our balance sheet start to release as well. But yes, I would give sort of a couple of quarter time line on that dynamic.

Ramsey Clark El-Assal: Okay. One quick follow-up. The super prime percentage of loans as a percentage of total just declined a little bit quarter-over-quarter. I’m just curious what is the driver there? Is it mix? Is it underwriting decisions? How do you — how is that trending right now?

Sanjay Datta: I don’t think it’s anything in particular because I think we have enormous room to grow in our core as well as in super prime and both are growing very quickly. So it wouldn’t surprise me that it kind of goes back and forth. We have enormous market share opportunity across those product segments. So to me, that’s not surprising. We’re also really just beginning to build in the depth of funding in the super prime segment. So we do have very competitive funding in super prime, but we need to build the depth there, and that will allow us to scale while keeping prices in a very competitive place. So that’s kind of the process going on there. I wouldn’t read anything more into it than that.

Operator: Our next question comes from Simon Clinch with Rothschild & Company Redburn.

Simon Alistair Vaughan Clinch: Maybe I could just start with the pretty impressive step-up in contribution margin versus your guidance. And Sanjay, could you perhaps break that down? I mean I presume some of that is due to sort of lesser mix of prime within the mix of loans that you’ve originated. But also the comment around take rates being higher. Could you just talk to that and elaborate a little bit more, please?

Sanjay Datta: Yes. Sure, Simon. Yes, I guess the overall contribution margin improved. That is in the face of yet sort of growing new products, which have immature unit economics. So you may infer that the contribution margin of our core business grew by even more. And within that, there’s both a mix benefit from having slightly more core borrower segment loans versus super prime. Those loans obviously have higher margin than very super prime loans. And then within that core borrower segment, there in of itself, our contribution margins and our take rates improved. And some of that is a result of the model launch we had, an improved model improves conversion rates that decreases acquisition costs like-for-like. And so there’s sort of benefits to the unit cost side.

And then even our take rates within that core borrower segment in the personal loan business saw some improvement, I think largely a result of the ongoing cost or sort of the take rate optimization that we’re always doing, trying to understand elasticities in the different bands and optimizing against them. So that gave us some opportunity to improve our take rates and our contribution margins overall.

Simon Alistair Vaughan Clinch: Okay. That’s great. And just a follow-up, just on the outlook you’ve provided and your comments around the macro that we’re seeing. Perhaps you could just give us a sense of what your — what assumptions you’re making around that are sort of fed into your guidance? Because I see that the third quarter is a little bit above consensus, but the fourth quarter looks like it’s kind of unchanged relative to consensus. And given the substantial beat this quarter, it feels like you’re holding something in reserves. So I just want to get a sense of that.

Sanjay Datta: Well, let’s see, one part of your question was about macro assumption. We’re typically conservative with respect to the macro. We sort of roughly expect the status quo. So the main way that we measure that, of course, is in UMI, the macro index we have, which is hovering in the 1.5 range, and we sort of plan to a consistent UMI for the rest of the year. So remaining in a relatively high default rate. We plan for no real cuts in interest rates in the market. Obviously, there’s a lot of speculation around what that might look like for the rest of the year, but we certainly don’t bank on anything in that regard. And we’re sort of continuing to rely on a relatively resilient labor market, notwithstanding the noise of the last week or so.

I think the labor market continues to be in relatively good shape in terms of how many open jobs there are out there versus how many people are seeking jobs. So that’s sort of the totality of the macro assumptions that go into our planning, and I think it’s a relatively conservative sort of kind of a status quo, if you will. You also asked a little bit about the shape of the guidance. I would just say that we have relatively direct line of sight into what things are going to drive our Q3 numbers. Those projects are very sort of near term and rounding the corner. And so we feel very confident in being able to guide against them. I think there’s a lot of things we’re excited about with respect to Q4 and how that’s going to go, but I don’t think we quite have the line of sight required to guide specifically against them.

And so I think that a lot of the near-term uplift you see is just excitement over some of the sort of the projects and the dynamics that we can see much more in front of us.

Operator: And our next question comes from Dan Dolev with Mizuho.

Dan Dolev: Amazing results, as always, very proud of you. My biggest question is the UMI, what could make it go up or down as we move throughout the year? This is the key question.

Sanjay Datta: Great to hear from you. Let’s see. UMI, I mean, it is ultimately a reflection of the impact of the macro on credit trends. I think the things we think most about in terms of what could move it, what could make it go down? I think the main thing is improvement in savings rates, improvement in consumption patterns relative to income. I think we’ve been consistent in saying that the American consumer in aggregate is probably overspending relative to the income levels that we’re earning, and that’s been true for a while now. And if that balance improves, we would expect that credit trends would improve as well. In the opposite direction, you might imagine things like a reacceleration in inflation or significant unemployment.

Operator: And we’ll take our next question from Kyle Peterson with Needham.

Kyle David Peterson: I want to start off on like the average loan size and the take rate. It seems like that’s been drifting down, at least in the core personal loan product. Should we continue to see that drift down? And I guess like is that a strategic shift? Or is that like a broader response to the macro where that’s where you guys are seeing like the most favorable risk reward here, I guess, just trying to level set on kind of whether — what’s strategic versus like what’s the market conditions response here?

David J. Girouard: Kyle, this is Dave. I think you can, I guess, categorize it as strategic, meaning it’s intentional because it really is reflecting the very rapid growth of the small dollar product, which for us is really, again, pushing the boundaries of the credit model, getting much more people onto the platform. It accounts for a lot of our new users. They can be upsold to other products like Auto refinance, et cetera. So it is definitely the fact that, that product, which has much, much smaller loan sizes is growing very rapidly. And again, our goal is to have like every American persistently underwritten on the platform. So having more and more ways to get them in is, from our perspective, good and having more products to cross-sell to them.

So that’s all part of the larger game plan. And I don’t know if you’ll see it continue to go back down. Right now, products at the other end, like mortgage and home loans are growing, but — so at some point, these will outweigh each other. There’s no real change other than the product mix itself is getting more diverse.

Kyle David Peterson: Okay. Okay. That’s really helpful. And then I guess on some of these new products, obviously, it seems like a really good opportunities are in like HELOC and Auto. How would, I guess, like you guys compare the competition there versus the core personal loan product? Is it equally as much of a knife fight? Or like is the sledding any easier or tougher? I guess, just kind of how should we think about the economics of these products, especially as they shift to external funding and the ability to scale quickly? Any color there would be really helpful.

David J. Girouard: Yes. I think I mean these products are different and our ability to create a very differentiated product happens in a different way. I would say in the unsecured products, the underwriting itself is a big part of the advantage and the magic that we bring to the market, and that is what’s built the company. And the newer products, particularly like Home, Auto refi, they’re actually quite low loss rates, prime-ish products. But the real opportunity for both Home and Auto is to create a very differentiated experience and process that costs a lot less to originate and also just creates a far better consumer experience. So the relative ability to price differentiate isn’t as great as it is in unsecured, but the ability to create a very much differentiated experience for the consumer and also a lower cost origination is much larger for those products.

So overall, that’s what we kind of keep pushing on and sharing. We mentioned automating a lot of the process of getting a home equity line of credit. That’s a product that normally would take more than a month on average to get from your local bank or credit union, and we can do it just in a few days or even faster. So that, to me, is important. AI can bring not only pricing things properly, but also just eliminating the friction and reducing the risk in highly automated, very efficient ways.

Operator: The next question comes from Mihir Bhatia with Bank of America.

Mihir Bhatia: Maybe just starting with the newer products, particularly the Home and Auto. You mentioned you’re working on additional funding partners or funding partners to get some of that off the balance sheet. Can you provide a little bit more color on that? Are those going to be more bank partners? Are you thinking securitization? How are you thinking about that?

David J. Girouard: Mihir, this is Dave. I mean it will be a combination of banks and credit unions. For both Home and Auto, they have a lot of history and familiarity with those products. Particularly HELOCs are something that are extremely popular in the bank and credit union world. So I would say on a relative basis, they would probably have a larger play there relative to the institutional capital, private credit capital. Though as we go, we will always find the right most competitive combination of capital to have the best product in the market. And I think that’s actually what’s unique about our position as we have both depository capital as well as private credit and other sources of institutional funding in effect, competing with each other to make the best product for the consumer. And I think these things relative to the unsecured product, this will swing — I think the other products will swing a bit more toward depository capital.

Mihir Bhatia: Got it. That’s helpful. And then just turning maybe I think on the conversion rate improvement, you mentioned the biggest driver was the new product, the new model, which launched in early May, if I heard that correctly. So does that mean you only got the benefit of 2 months. So 3Q conversion rate should be even higher from there? And just if I could also just throw in there, if you could just talk a little bit about the Walmart partnership, any call-outs there?

David J. Girouard: We aren’t really forecasting anything about conversion rates for current quarter. There’s always puts and takes on conversion rates. When it goes up, we often end up spending more and pulling it back down intentionally. In other words, turning conversion rate into extra growth. So there’s just not a straightforward kind of up and to the right on conversion rates. If you sort of see the chart that we provide in the investor deck, I think it’s a great illustration of how conversion rate trades off with volume. And so I’d say that. On Walmart, we continue with that partnership. It’s been a great success for us thus far, but we don’t have anything new we want to share about it today.

Operator: And our next question comes from Reggie Smith with JPMorgan.

Reginald Lawrence Smith: Congrats on the quarter. Really strong quarter. I had a follow-up on the conversion rate. I’m not sure if you guys have shared this in the past or if you’re comfortable sharing it. But I’d love to hear about, I guess, the 2 elements of conversion rate, the approval rate and then kind of the acceptance rate is how I’ve been thinking about it. I guess, one, am I thinking about that right? And then two, can you talk about how those ratios have maybe changed versus the prior year? And then as we think about the new model, maybe anecdotally, talk a little bit about the types of people or the profile of the people that may have been rejected before that they’re being approved today. Obviously, I don’t want to give your secret sauce, but just any color you can give there? And I have one follow-up.

David J. Girouard: Reggie, your sort of decomposition of conversion rates is correct. It’s sort of a product of our approval rates and the subsequent exception rates of the loan. We’ve never really decomposed it in how we analyze externally, and I don’t think we have any off-the-cuff narratives around the relative subtrends there. It may be something that if it’s interesting, we can sort of look at exposing over time or in the future, but I don’t think that’s anything that we have any great soundbites for you as of right now.

Paul Gu: This is Paul speaking. Just on the second part of the question about specific types of borrower characteristics that we may be waiting more or getting more of. I think the short answer is that like we’ve said a number of times at AI Day and other instances, the real power of our model comes from its ability to find many, many small subtle relationships in the data, and that’s happening at multiple levels of the model architecture as we described with Model 22. And the unfortunate result of that is that it’s not like there is one — well, unfortunate for answering the question is that there’s not really one simple answer of like we have suddenly got more high credit score borrowers or more low-income borrowers or anything like that.

It really is just picking a couple of borrowers from many different sort of parts of the credit fabric, if you will, and then finding borrowers who are more likely to repay than their sort of conventional credit characteristics would suggest.

Reginald Lawrence Smith: That makes sense. Okay. And then if I could, Slide 23 in the deck, you guys give this every quarter. We see, obviously, the numbers are increasing. It looks like the assessed value is greater than the co-invested value up until this point. Maybe help us understand like how should we interpret this slide? And what should we take from it? And then as far as outperformance or underperformance relative to expectations for this piece of your portfolio, where does it show up? And how do we see that flow through? Because it looks like maybe things are better than you even thought when you put these loans on the books. I’m just curious where that shows up.

Sanjay Datta: Reggie, well, as far as what takeaway from this slide, I mean, it does pull together, I think all the various ways in which we are co- investing in risk capital deals. So it hopefully gives you a holistic perspective of what that investment level is at any point. I think in terms of thinking about how to model it, we’re sort of in the ramping phase. To the extent that these deals start paying back a couple of years into the deal, you should expect this to sort of ramp probably for a few more quarters, and then it will start to level off as the amounts we’re investing in new quarters are roughly offset by the amounts coming back in from prior deals. And so there’s sort of a ramp-up and then a sort of a platforming of this amount.

And then, of course, this tells you how we’re doing on those investments. I think early on, in the early sort of instances of these deals, our goal is to make sure we were preserving capital. And so you want to make sure that the way that we’re valuing what these positions are is at least on par with what we invested. I think more recently, we have an intention to start earning returns on these investments. And so you’d expect or hope that the assessed value of these positions starts to grow in relation to the invested capital. But I think the idea of this slide is to give you a picture of how this investment is trending and how the returns are looking. In terms of how it shows up in the P&L, it’s probably a much more complicated answer to your question because the reality is it hits on various line items depending on the structure of the deal, and there’s a lot of different structures at play here.

But I think in a — at a very high level, you can expect that the amounts that we’re assessing at current value, if they were to hold, they will make their way back into the P&L largely in the form of fair value improvements or net interest income really. And so while there’s a couple of different paths back into our financials, I think that’s probably like if you were just to really crudely simplify it, that’s how it will show up.

Operator: And we’ll take our next question from James Faucette with Morgan Stanley.

James Eugene Faucette: I wanted to just talk about really quickly, you mentioned your CAC and some benefits you’re getting there. But you’ve also always been really CAC efficient during even lean periods and whether it be your organic or your own CRM mind leads. As you expand though, how should we be thinking about how much is purely organic traffic to upstart.com, how much is originated via direct mail and how much is sourced via third-party marketplaces? And how should we think about the evolution of those types of channels?

David J. Girouard: James, this is Dave. I mean, I think the long-term trend has been more repeat borrowers, both in the core products we started with the unsecured product and increasingly cross-sold into other products. So those can be, as you might describe it, mined from our database or just people that have a relationship with us, and that’s largely close to 0 CAC and an increasing fraction of our loans. The offset will always be how fast are we growing and acquiring new borrowers. So it’s not necessarily bad, of course, if new borrowers are suddenly coming on board much more quickly and most of those are paid for one way or another. So I just think we’re moving toward a place where reliance on aggregators or other forms of sort of people that represent our brand is sort of slowly declining over time.

And we have much more sort of direct relationship with the consumer and more things to offer them. I would think the thing that is really of note in the last few quarters is we are beginning to really get much better at how we — how to properly cross-sell and through lots of testing and things, somebody that might have gotten a personal loan or a small loan, and we are able to like refinance their auto loan at a lower price. That’s something that works much better as a cross-sell than it does at a first-time acquisition. So I think as you see the Auto and Home categories grow, you’re seeing a lot of cross-selling to them. And that’s just the road that we’re on, which is, I think, more repeat longer-term relationships with consumers. But also, by the way, it also sort of — I think we’re proving the long-term value of a customer that we serve, and that allows us to invest a bit more upfront with the confidence that it’s going to generate more margin downstream to cross-sell to other products or second loans, third loans, et cetera.

So I think that whole model, of course, is something we’ve worked on for a long time, and it’s how we’ve been able to grow and have kind of acquisition cost per loan actually for years has kind of generally gone down.

James Eugene Faucette: Got it. And then I wanted to ask, you guys often give updates on what portion of the loans are being handled completely automatically. And I don’t know, I may have missed that this time, but just it was interesting that you called out within some of the new products like HELOC and Auto that you’re looking at taking advantage of your improved automation, basically that becoming part of your brand. And I’ve got to imagine that’s really helpful in this cross-sell. Where are you at in terms of the automation levels now for — and where do you think you can get to ultimately?

Paul Gu: Yes. Thanks. Great question. So we obviously haven’t broken out the exact fully automated percentages for the new products at this point. But I think it’s safe to say that they’re a fair bit less mature than they are in the core personal loan product. And that’s not surprising. I think they’re both newer products, but also they start out with more challenges. I think ultimately, we believe that those challenges can all be overcome. And I think we shared in the preprepared remarks that we made quite a lot of progress in both Auto and the Home categories in this quarter. And in particular, we had our first instances of fully automating several new parts of the home loan process for our HELOC product. And there’s still quite a bit more work to be done before those numbers will reach the personal loans level, but I think they are on a very good trajectory towards that. And I think in the long run, that would be our expectation.

Operator: And our next question comes from Rob Wildhack with Autonomous Research.

Robert Henry Wildhack: On the fair value adjustment in the quarter, I noticed that was higher or more than a negative than it’s been in the past. Can you speak to the drivers there? And then I think along the same line, the aggregate NII guide is down for the rest of the year despite the increase in balance sheet loans. So how you’re thinking about fair value marks for the rest of the year?

Sanjay Datta: Rob, yes, I mean fair value has some volatility to it. I would say the rep contour is UMI, our macro index sort of fell by a lot in the second half of 2024. So we took some of those gains as fair value marks as it persisted into the year. So in Q1 and partially in Q2, you saw some of the benefits of that declining UI. Now UMI has drifted up a little bit in the first quarter or 2 of this year. So that’s starting to reflect itself in Q3. In Q4, I think you’re starting to see some of the benefits of the risk capital deals, some of the earlier vintages of risk capital deals that are starting to repay. So there’s sort of some benefit happening there. And you sort of have a bit of a dead period in Q3 where the UMI has drifted up.

So there’s a bit of pressure there. The risk capital deals are not quite yet starting to materialize as benefit. And then you have this sort of phenomena that depending on the seasoning of your loan book, if I could describe the fair value of one loan in isolation, it would be really high at the beginning of the loan because there’s really — there’s a lot of interest and no charge-offs. And then at some point around month 12, you hit the peak charge-offs. So the fair value of that loan at that time tends to drop. And then at the end of the loan, the charge-offs, the curve has sort of run off and you get pure interest again and it’s high again. So you have this sort of natural phasing of the value of a loan where it sort of goes from high to low to high.

And when you project that over an entire portfolio, you can sometimes get these effects where the value, even though it sort of averages out to 0 over time, has a bit of seasoning volatility, if you will. And some of that is playing in Q3 as well. So I know that’s like — that’s a large recipe of a lot of ingredients. But the reality is fair value is quite a complex sort of topic. But I think those are the main things that are impacting the trends over the quarters.

Robert Henry Wildhack: Okay. And then bigger picture, competitively, it seems like all the personal loan origination — or excuse me, originators are growing very quickly lately, even someone like SoFi is loud and clear that they’re drifting into near prime with their loan platform, which is kind of your space. You grew quite a bit in core loans, too. I’m wondering how you guys think about adverse selection in a competitive environment like this one.

David J. Girouard: We certainly think about it. I would say, first of all, it’s one of the reasons we really focus on making sure our cost of capital is competitive across the spectrum or anywhere we want to participate or else you are at risk of adverse selection. So at the conceptual level, we just have to make sure that the fuel, the dollars funding the loans are as good or close to as good as anywhere, and then you have much less concern about that. Also, I mean, the nature of our models that have gotten sophisticated enough to handle issues of like how does the price of the loan affect the performance of the loan. If you recall back last fall, we introduced what we call APR as a feature, which was quite an innovation on our side, which helps us make sure the price of the loan is considered when you’re measuring the risk in the loan.

And that was a giant leap forward back in model 18. So we kind of feel from a technical perspective and also from a business perspective, we are both aware of and responding to potential for adverse selection. So a very competitive market is not new to us and something we feel pretty good about.

Robert Henry Wildhack: Sorry, if I could just sneak one more in on that, Dave. How do you compete with deposit funding from somebody like SoFi or LendingClub in terms of cost of capital?

David J. Girouard: Well, about, I would say, 25-ish percent of our loans are funded by deposits. So it’s just that they’re not our deposits, they’re from credit unions or bank partners. So that’s an important way. And that’s, of course, for the primest of our loans. So that’s deposits compared to deposit. We’re just doing it in a distributed manner as opposed to it being Upstart Bank or something. The other thing I think is important, which is happening quickly is I think non-depository capital is getting more competitive at the primary end of the spectrum just because there are sources of funding that have all sorts of different blends of risk and reward appetite. And through the worlds of private credit, insurance, et cetera, there is non-depository capital that may not be exactly as inexpensive as depository capital, but getting closer and closer. So I think that difference, which historically was quite large, is actually beginning to blend much more these days.

Operator: And our next question comes from Kyle Joseph with Stephens.

Kyle Joseph: A lot of them have been addressed, but just thinking about the product mix, obviously, you guys talked about the benefits of AI to the personal loans. And then obviously, you’re seeing good growth in Home and Auto, but factoring in the law of large numbers, just kind of how you see the mix going forward and any implications in terms of margins and/or customer acquisition costs?

David J. Girouard: Well, let me just speak to the mix to the extent we can, and maybe Sanjay can comment on what it might mean for margins. Look, we, of course, feel like there is enormous growth potential in all our product areas, including in our core personal loan product where we have very, very well-established margins and automation, et cetera. And then these newer categories are generally much larger categories, but we’re much earlier into penetrating them. So we think we have a very attractive, like very large addressable market. We’re very early into it. And almost inevitably over time, I think the secured products, meaning Home and Auto are going to grow as a fraction. And could they become much larger than unsecured over time, it’s quite possible.

I don’t know that we know that. I think unsecured as a category even though it’s smaller, is growing, it’s quite popular with consumers. So I guess that’s a long-winded way of saying, look, we’re in like single-digit market share in unsecured, which we have enormous advantages and strengths and just at the cusp of really beginning to move in Home and Auto. So the potential for very, very rapid growth over a long period of time in all those categories is pretty significant.

Sanjay Datta: Yes. I would just add with respect to margins. I mean, the margin profile of these new businesses is still materializing to some extent. But all these markets have the same rough shape as the core market we exist in today, which is the unsecured market. And that’s that — there are some segment of very well-served borrowers where prices are competitive and margins are thin and you largely compete on process and distribution. And then you always have a part of the market which does not have access or is underserved, and there’s a lot of opportunity to create volume and margin. And I think that’s no different in the auto space or even in the HELOC space, frankly. I think these loans that are secured will have larger loan sizes, maybe smaller percentage take rates and just a similar dollar revenue and/or profit per loan that we have in our unsecured business.

But we don’t know the exact precision of that yet. We’re still — that’s still materializing as these categories are growing for us.

Operator: And we’ll take our next question from John Hecht with Jefferies.

John Douglas Hecht: Like Kyle, most of my questions have been asked. I guess I’m curious as to kind of your sense of what interest rate reductions would do. I assume the Auto refi business would pick up, and the HELOC business, I imagine as well. But in the core products, do you pass on rate changes to the customers? Do your — some of your partnership agreements with the private credit, do those contemplate changes in interest rates? Just to kind of get a sense if we go into your rate cycle, what to think.

Sanjay Datta: John, yes, a reduction in rates would telegraph itself into our core business virtually one for one, not immediately with some lag. But if rates in the economy go down, that means financing rates go down, and that means your — the sort of ROA of your unlevered loan can go down commensurately. And so it would result in lower rates to our borrowers and conversion improvement. And yes, the sort of the types of structures we have in place with our committed partners contemplate this and would sort of act in this fashion. So it would be unambiguously good if rates were being reduced, at least in a direct sense because it would mean that rates for borrowers over time would sort of reduce commensurately.

Operator: It appears as there are no further questions at this time. I’d like to turn the conference back to Dave Girouard for any additional or closing remarks.

David J. Girouard: All right. Thanks, everybody, for joining. Q2 was a great quarter for Upstart, no doubt. For those of us in the inside who saw kind of the radical makeover we’ve been going through in the last couple of years, it wasn’t a surprise, but it was very rewarding. So thanks again. We’re very excited for what we’ll do the rest of this year, and we’ll see you all in November. Thanks.

Operator: And ladies and gentlemen, this concludes today’s call. Thank you for your participation. You may now disconnect.