Upstart Holdings, Inc. (UPST) Earnings Call Transcript & Summary

Afternoon, everybody. Thanks for joining us this afternoon here at the 2026 Morgan Stanley TMT Conference. Very excited to have Sanjay Datta, President of Upstart. Thanks for being here.

Thank you.

Before we get started with Sanjay, quickly, I've got -- I'm going to read a couple of disclosures. We have ours, which is please see the Morgan Stanley research disclosure website at morganstanley.com/researchdisclosures. And then Upstart has asked me to read theirs. Today's discussion may contain forward-looking statements that relate to future results and events, which are based on Upstart's information available as of today and are subject to risks and uncertainties. Actual results may differ materially from these forward-looking statements. The discussion may also include non-GAAP financial measures, which are not a substitute for GAAP results. Please refer to the company's filings with the SEC and its IR website for additional information, including GAAP to non-GAAP reconciliations, along with other disclosures. Did I do it all right?

I feel like your disclosure is a lot more cogent than ours...

Well, I think we get away with referring people to -- anyway, thanks for being here. Really appreciate it.

Look, I want to start with just kind of like some of the leadership changes that have been announced. I don't know that they were under adverse circumstances or anything like that, but it does create questions for people. How should we think about strategic continuity versus change with the leadership transitions that have been announced, where should investors expect meaningful change, if any, in priorities or pace over the next 12 to 18 months?

Yes. I think this has got to be one of the leadership transitions with as much continuity as you could hope for. I mean, Paul and Dave have been covering the company since inception for the last 14 years, they've essentially been co-decision makers and aligned leaders throughout. And they started planning this transition -- I mean, I knew about it as of 5 years ago. So it's been -- they've been working towards it. And it's sort of like -- it's like Dave was running the company, and Paul was his co-leader, and now Paul will be running the company, but Dave is not going far. He's still the Chairman and the biggest shareholder. So he's still [ flocking ] us and giving us his opinions. And -- so I don't -- nothing will change with respect to our priorities with respect to our objectives and strategies.

Got it. So I want to talk about another announcement that you made and one that I know that we in the investment community grapple a lot with, and that is the periodic disclosure of KPIs and how that may impact volatility. I know certainly, I feel like there's probably a lot of opportunity for investors if there was a way to tamp down volatility of stock itself. And one of the things that it seems like it could be a step in that direction was your decision to start to publish monthly transaction volumes. That being said, is like anything, monthly transactions or any shorter period of time has the potential for more variance from period to period. How should we investors separate what is signal versus noise, particularly in month-to-month prints, whether that be seasonality, marketing cadence, partner onboarding? How should we try to digest those numbers as they come out?

It's a good question. We've sort of done two things with our guidance framework. On the one hand, we went more short-term transparency, right, from just quarterly guidance to like monthly updates on volume. On the other hand, with respect to guidance, we're now emphasizing the longer-term view, the 1-year view and beyond. And really, what we're doing there is on the short-end side, we're trying to provide real-time transparency to what is going on. There's so much speculation out there and so many third-party data sources trying to figure out what's going on. And they're adding to the noise because sometimes they're right and sometimes they're wrong and...

And sometimes they're right by a little bit and sometimes they're wrong by a lot...

Yes, it's created its own dialogue. And we are just like, look, just -- we'll just put the facts out there. And so it's kind of like maybe just kind of desensitize that a little bit. Now with respect to what within that is noise and what is signal because I do think like people tend to get maybe overly wrapped up in -- with this month versus last month. So I think the way we'll sort of signal what's important is when we come back and say, well, here's how you should think about how the year is change -- like if there's a trend in those things that are useful. We're going to come back and tell you that the year is kind of changing. And we'll give you a different outlook on the year to sort of reflect that. But short of that, I think a lot of it is just there's like different calendar days or different business days, depending on how the weekends fall in certain months versus others, there's seasonality, there's all these other things. And so yes, I think what we're going to really try to do, if you're interested in the signal of the business, is really telegraph that through our evolving view of how the year is going.

Right. And it seems like, to your point, is like it's going to put incremental -- create incremental opportunity for us on the investment side of things to try to take into account those things. But if I put it together with the way that you disclosed UMI, et cetera, it seems like there's a lot of message and signal, if you will, that we should be able to deduce as investors. So let's talk about the additional piece of this communication, and that's the multiyear framework. And give us a little bit of insight, if you can, into the thought process around providing a multiyear framework like you did, especially given the cyclicality inherent in the business, right? Like there's a lot -- and by cyclicality, I mean, the economy is changing, it's you're an economically -- cyclically sensitive business in some ways, et cetera.

Yes, that's an important point. I mean, one of the assumptions underpinning that longer term, I won't call it a guide, but it's the targets that we're operating against, if you will, is that it will -- it's sort of what we believe in a roughly macro neutral environment. Now the macro won't be neutral probably. Who knows if it will be a headwind or a tailwind, it may be both in that time frame. But really, what it's meant to say or meant to sort of connote is in that kind of a time frame, the underlying growth characteristics of the business will be a secular one. And the secular growth dynamic in our business is related to getting better base models and more automation over time. So as we do that underlying work, we get models that do a better job of avoiding default, which then reduces APRs for all the other applicants, who are not subsidizing as much default. And you get more automation, meaning you take friction out of the funnel, you take documentation out of the process, and that also acts to improve conversion rates. And so I think the road maps we have and the backlogs we have of all the R&D projects that are going to chip away at those things, incrementally more automation, incrementally better risk evaluation is long enough that we're signaling that we think secularly, we can -- we're very confident we can maintain that kind of a growth clip over the coming 3 years. And so it's really meant to telegraph the fact that -- I think -- because there maybe was a mental model out there that's like, okay, I see it's a pretty good core business. It's going to start cash counting in 2, 3 years. The margins will be -- I think what we're saying is, no, we've got a big growth curve ahead of us. Like the out-year outcome of our business should be a very big one. And the sustainability of the growth, I think, in the next 3 years is something we feel very confident in. So that's the main signal. Now of course, there will be macro puts and takes to that, probably. We can't predict what they will be. And so from that perspective, yes, we're not trying to put a stake in the ground and predict that. But I just -- we're trying to signal that in the underlying growth characteristics of the business, there's a lot of -- there's a lot more persistence that we see.

Got it. So let's talk about some of the components of that multiyear framework. And part of that is the scaling of secured risk -- secured lending. And I guess the thing that a lot of times we're asked is, hey, the unsecured personal loan market is still really large. What is the strategic necessity of scaling into secured lending? Is it risk diversification, capital durability? Are you looking to expand TAM? Or is there something else within that?

Yes, there's no necessity to do it in a sense. I think we could build a nice big business with healthy margins in our core. But I think this is like the difference between big and massive, right? The secured categories, I think the rationale for why we can and should be successful in them is the same as in the unsecured categories. There's always a combination of too much friction to access to credit and like a significant part of the market that's not getting access because the risk models aren't good enough. And the TAMs on that thing are like multiples bigger on the unsecured side. So I kind of view it as like, yes, no, we're very excited about the opportunity in the unsecured world and in our core business. But I think there's also this like there's a bit of a race that we're running alone right now, but we don't expect to be running alone forever. And it's like who's going to bring machine learning models into these new segments of credit and then ultimately, for maybe new geographies as well. And I think there's a huge first-mover advantage because you start generating the -- what's interesting and unique about applying AI to the credit space, unlike a lot of what's being done out there, where the training data is just hoovering up the Internet. The training data is not out there. You have to generate it. And so whoever is first, whoever starts generating that training data, not just through repayment history, but all the variables you collected against it. Where did that borrower study and where do they work? And what's their title and what's their role? And how do they interact digitally with my application? And when I ask them their FICO, how close were they to knowing it before I check -- like all those things provide a richness of data that then train the models. And because it's not out there on the Internet, you have to go and generate it. And so there is a bit of a first-mover advantage in taking AI into these spaces. And so we're just trying to plant the flag in these areas. We think there'll be massive opportunities. And it's not to sort of diminish the opportunity that we feel exists in the unsecured space. We're a bit greedy, frankly.

Right. So let's talk about mix of secured versus personal unsecured loans, consistent with what you said is like the TAM for secured is much, much larger. And so you've obviously made the assessment or said that you expect that secured products should become a larger share over time. Remind us kind of what that time frame could be or should be in your planning and how you're thinking about it? And what would have to happen in the auto and home funnel performance for them to overtake personal loans sooner than expected?

With respect to time frame, the honest answer is I don't really know. And the reason is because I think we will grow faster in these new categories. But they're trying to catch a course that's moving pretty fast -- and it's a bit hard to tell exactly. I mean I think we've telegraphed a couple of years of strong growth in the core. And the new products will outgrow it, but that's a big and fast-growing business on its own right. So there's a lot of internal bets as to what we look like in 5 years. Has the core been overtaken? Is it auto? Is it home? I think everyone has a bit of a different take. So I don't think we have got...

Yes, everybody has a different take. I'm sure both externally, but it sounds like even internally.

Even internally, we all have our own favorites and our own -- I mean, we're obviously betting or hoping for all of the above, but everyone has different kind of view. And with respect to -- you said, what would have to happen...

In the funnel or a conversion, like help us think about like the differences between the different products, whether it be auto, home, or unsecured and what you see in terms of conversion rates, et cetera? And even reach.

Reach. Yes. I mean those are the two dimensions, right? One is -- one sort of variable is how quickly are we going to improve the models. And to what extent do we get some unexpected surprises, some unexpected progress. Some of the advancements we make benefit all the models and some of them are sector specific. And so there's different teams working against the different areas, and if they have some big advancement in one particular area, it will improve our conversion mechanically. And then the other axis really is about distribution. And they all have a slightly different distribution strategy. Unsecured lending is obviously heavily D2C. It's heavily digital. Our main distribution strategy in auto is taking software to the dealerships. In HELOC, we're still early days. We've stood it up as a D2C business. I think there's a lot of opportunity as a white-label strategy, working with other depository institutions and the like. So again, you can have big breakthroughs like -- just as an example, I think that we, for many -- a couple of years have been trying to figure out the right product market fit with the dealerships in terms of our software. Because once you get the software in there, you can surface the loan offers [ and ] the commercial flow. And I think we spend a lot of time trying to find like something that was very powerful and integrated, but it didn't take too much of a lift for retraining and like changing workflows. And I think that -- and relatively recently, I think we've hit it. And so I think that, I mean, some of the growth you saw in Q4 in the auto space, that's just like we found the right product market fit with dealers and it's starting to fly off the shelves...

And by product market fit, you mean like the way that the software itself has put together, how it integrates into their own sales flow, et cetera.

How much the dealers are demanding it. Like you can -- like sometimes selling things is hard, and it's like knocking your head against the wall. Other times, they're calling you, "Hey, I heard about this, like can you give me a demo." And I think we're starting to get a great flywheel in that regard. So that's just like one example. You can get these distribution breakthroughs that sometimes create a big sort of unexpected leap forward.

No, for sure. So let's talk about unit economics. As secured becomes a larger share, what's the right way to think about the blended take rate and contribution margins? And I ask this question because inevitably, investors, right or wrong, are also trying to grapple with it and see if the take rates are suggesting some -- one thing or another. But what should we expect headline take rate compression to look like even if contribution dollars improve?

It's a good question. We don't exactly know. But part of the reason we don't exactly know is because the take rate itself within a product has its own life cycle. If you think about the advent of -- or history of our unsecured business, for the longest time, our take rates, I think, were probably in the average of 5% or something like that, okay? And what happens is you can think about the market benchmark in terms of how loans are being priced to consumers as being somewhat credit score centric. And as our model get smarter and smarter and smarter relative to that benchmark, it's avoiding more and more and more. So if you think about that average price, there's half of the borrowers are riskier and half are less riskier and you get an average. As you avoid more and more of the riskier borrowers, your APRs start to come down. And as your APR start to come down, you create margin opportunity. And so by 2024 compared to 2018, we were charging 10% to 12% take rates. And we could, and we were still the best offer in the market because of that margin we had created. And that margin did not exist back in 2018. So if we had tried to charge those rates, we would have gotten adversely selected. So as your models get more mature, as they get better, you create economic opportunity. And that opportunity can go to the borrower in the form of lower rates or it can come to us in the form of higher takes or you could conceivably give it to the investor in the form of overperformance or some split of the three, right, which is typically what we do. And so that same thing is going to play out in the secured products. So I think we said in the latest earnings, I think the average upfront take rate for secured products out of the gate is going to be something like 4%, but there will be a higher component of servicing economics. When you think about the analog, our servicing rates in unsecured lending are 1% per year. So as the loan amortizes, you get like a ratable revenue stream in addition to the upfront take. In secured products, we think that will be closer to 200 basis points, okay? But there will be a differential between home and auto. And I think both of those segments will evolve. As we get better models, we should create room for more economics. And so it's a bit of a -- there's a lot of moving parts. In addition, our -- in our core business, our take rates went up to 10%, 12%, we signaled that they're starting to moderate down a little bit because at some point, it's not the best long-term business strategy to run an economic model where you're charging 10% take rates. Like there's some money you can sort of invest forward now in exchange for a much bigger business down the road. And so I think that one will moderate a little bit as we've telegraphed. I think these ones will improve over time and the relative mixes of the three, who knows.

Yes. So let's talk about competition in secured. The competitive set seems like it should change pretty materially in secured. Where do you see your primary advantage versus the incumbents? Is it better credit decisioning, a better conversion UX, better partner economics, time to close? Where do you think you have an advantage initially and which one of those facets or front, do you think will end up being most defensible?

Yes. I think it's different mixes of the same playbook. Almost every credit segment, you've got sort of a hyper-served prime segment and a very underserved, sometimes nonexistent riskier segment. And in the riskier segment, we aim to compete and sometimes create markets just by being good at underwriting. Now as you get more secured, you probably get more of the sort of prime sort of well-served customer. And then there, you're competing on two things. One is automation. Like how much friction can you take out of the process. And the second is your -- the efficiency of your capital. And so I think in each of these -- I think we're discovering that each of these areas, we've got opportunities for both. Like let's take HELOC as an example. It takes the industry 30 to 40 days to do a HELOC, we and maybe one or two other fintech players are down to 5. That's like a significant experience improvement. So for people who can access HELOCs already, we're not creating access for them, but it's like, well, you can wait 40 days at your bank branch or you can get this next Tuesday. And so that's a very powerful way of competing. But by the way, the HELOC space, they're typically coming from the home buyer, like the mortgage space, and they will take loss rates up to 2%. There's not a lot of 4%, 5% loss HELOCs out there. That's viewed as very risky in the home space. But guess what, like we're working with unsecured buyers who are buying 10% loss loans and 5% looks pretty good to them. And so -- but in order to sort of create a new sort of market space in a world where the traditional market isn't touching the risk, you have to have a lot of credibility in your underwriting. And so I think both of those are opportunities, whether it's auto or home or whatever is next.

Why don't I take a breath, see if there are any questions in the audience here. Before we go to funding, et cetera. So probably the biggest apprehension in the credit market right now, and you can see this even with you or others that they put up good results, be it the stocks or don't react in a way that maybe you would expect, seems to be tied to private credit and the state of capital availability. And there's been a lot of chatter of late on the health of the private credit space. There's an article on the front page of the Wall Street Journal talking about a big provider of private credit and kind of the things they're working through right now. That being said, recent vintages and their returns have looked really attractive versus benchmarks for you. How should we expect the spreads to express themselves and to evolve as you skew more to the prime and secured? And what are your capital partners talking about that they're looking for from Upstart?

When you say the spreads, you mean like the pricing of the loans...

Yes, the pricing on the loans and the [ fees ] they're willing to pay.

I see. Yes. I mean I think from those guys' perspective, so you got two dynamics. On the one hand, each of these credit classes has some traditional buyer population. And then you get folks who I think are maybe, you could say, coming along with us for the ride, right? And so for that latter category, I think they kind of view all of this as some version of ROE, right? So a more secured credit class compared to unsecured will have more competitive APRs, lower APRs but they'll also have higher financing advance rates because they have lower losses. And it's sort of like levers up to the same mid-teens ROE or something like that. So I think, like whether the underlying asset is risky or very secure, that will be sort of compensated for in the leverage ratios, and it will ultimately -- you should sort of have a comparable level of risk and return across those products because of how you use the financing. I think that folks who -- they're sort of like folks who know Upstart very well, but maybe are getting their feet wet with new credit categories. And I think that what they want from us is consistency in performance and improvement. And then there's people we're engaging with who know credit classes very well, but are new to Upstart. And there, it's a lot of diligence, kicking the tires, making sure that we have the right incentives, the right sort of guardrails.

So for those capital partners, both existing and potential or newer, have you seen any reticence in their kind of behavior recently, et cetera, that would indicate like some apprehension that they're demonstrating around their own performance or access to credit and capital?

Yes. None at all, really. I mean the big annual tentpole Structured Finance Conference happens every year, it was actually last week. And I got to say that the tone was constructive and confident. But it's because the underlying credit is benign. The moment that turns, then there'll be discussions. But I think despite all the headlines out there and the talk of equity exposure, cross contamination, like everyone is like, I think these vehicles are very specific to consumer credit. And I think the underlying stuff, as you said, is performing very well, and everyone's happy. So everyone's like looking to deploy. And so that, yes, the tone is good right now, but the tone is always a reflection of credit performance and credit performance is in a good shape.

Yes. So let's talk stay on the funding topic just for a couple of more minutes. Let's talk about secured products and as those scale. Would you expect your ABS forward flow structures to become more central versus whole loan buyers? What kind of constraints, whether it be structure, ratings, data history, do you have to get past for that to matter?

No. The ABS sort of component of our strategy is very secondary in a sense, everything that we do is -- like the predominant objectives are to get the yield in the hands of either lenders, like depository lenders or institutional buyers. And then from there, if institutional buyers themselves want to securitize loans, we'll create -- we create the vehicle for them to do that. But there's very little sort of the prioritization of having us run a securitization shelf off our own balance sheet. It's really for -- it's like a feature of liquidity for the people who are buying the loans. That said, we do intend to stand up liquidity like ABS shelves for these new products because it's good for the buying community, it creates all kinds of good hygiene in terms of liquidity and price discovery. And I don't know that there's any massive hurdles other than usually in those vehicles you need to prove yourself, right? You need some history. You need to work with the rating agencies. It will probably be some version of what we've already gone through in unsecured, but hopefully a little bit short circuited because now these rating agencies know us. The first time we said, no, this is not going to perform how it looks like it will perform. They thought we were smoking something. And then like 8 years later, they're like, okay, they're now sort of taking our grades at face value. And so hopefully, that will sort of -- that will play itself out in auto and home lending as well.

So let's touch on balance sheet as I think you guys have gone through the process of educating people pretty well on, hey, as you're experimenting and entering into new markets, there may be some changes in balance sheet, even the amount that you carry initially may grow, et cetera. I think people are well conditioned and informed on that. That being said, you've been reducing on balance sheet exposure and scaling third-party funding recently. What's the road map to get home lending and that component of maybe that -- what you will carry on balance sheet initially to look -- be auto-like and get to the third-party involvement there so that you can kind of normalize and stay within your real long-term objectives?

The -- I mean it's just doing what we're doing, that's sort of my day job, right? We've got to do the deals. We've got the first couple of HELOC deals in place, and we've got other people looking at it. And yes, it's just a question of cementing those partnerships. Some of them are heavy lifts because they're meant to be multiyear commits. And so you got to do a lot of diligence upfront. But once you lock it in, it's very resilient. So I think we're just maybe a quarter behind where we are [ in a lot of ] the other emerging products.

Okay. Okay. Got it. Okay. So pretty good. All right. So let's talk about back to kind of this medium-term target and the profitability is we've got this roughly 25% EBITDA margin target. On that path, how do you think about how much do you expect to come from operating leverage versus internal AI productivity versus improving contribution margins as products mature? And just help us think through like what the drivers of -- or the points of leverage on your profitability should be.

The majority will -- should and will come from operating leverage. Yes. I mean, I think we view ourselves as a business that can grow the top line pretty quickly with a relatively controlled expense base, a fixed expense base. Now I think AI productivity will be a component of that, not the headline. But yes, like I think that we would expect to, on the balance to be able to do more growth with the same number of engineering bodies as they become more productive, so that will enhance operating leverage. I think it will be less so about contribution margin. I think we want those to settle into some nice stable ranges and there should be like, as I said, sort of some incremental improvement over time. But really, I think the headline is the bottom line of this business should largely be a function of operating leverage as we scale.

Got it. So let's talk about your technology moat and operational AI. Through our conversation this afternoon, you've talked about some of the challenges of taking even the emerging AI technologies and scale and applying them to this industry because of data, accessing data, developing the data repositories, et cetera. And something that you guys have been doing for a long time now. And you've mentioned that model updates incorporate broader outcome data beyond loans just originate on your platform. How are you validating that label and data quality and to avoid selection bias? And what's the impact on approvals for previously lost borrowers? How do you improve not only your data, but your ability to underwrite people maybe that you weren't able to originate for previously?

Yes. So I guess your first question is how do we sort of avoid bias in this expansion of our -- I mean in a sense, it's just -- it's more data available to our models. In a sense, the bias was bigger beforehand. So before, obviously, if you have a bunch of applicants and you give out a bunch of loans and you deny a bunch of people, you can only see the outcomes -- so you have this inherent bias, which is like you may be not approving some people and you'll never learn from that. And what you're referring to specifically is we figured out how to see the outcomes for the people that we decline who get loans elsewhere. And so we can learn from that. So in a sense, all we've done is expand our sort of visibility set in a way that reduces the bias compared to when we just saw the selection bias of the ones we have approved. So I think it's just like net positive, and it's had a positive -- so the benefit of that, of course, is then you'll learn from those people in ways that you're like, "Oh, I sort of declined a bunch of these people. But look, they got loan somewhere else, and they did pretty well." So then that's additional model learning that your model otherwise would not have had and then that obviously contributes to higher approval rates going forward.

And with that incremental visibility, like just remind us where is that coming from? And what's the opportunity or potential to further expand that capture data capture?

It was a body of work that really involves matching the data we had on applicants that were declined to outcomes of theirs that we found in the credit files.

In the credit files, right.

So it was being able to link those in a large data set. I'm sure there's further opportunity to be better at this, right? Like ultimately, you want the model to see as many outcomes as possible, so you learn as much as possible. So we've got other ideas for how to run sort of R&D programs aimed at learning from declines, but that's a good example of one big outlook.

Yes, really fascinating. I mean, certainly, I think you can see that there's a lot of nervousness in certain elements of what may happen in the market, but it's certainly encouraging and reassuring to hear that things still seem quite solid, both on the consumer front and performance as well as even on the financing side of things. So I really appreciate all your insights here, Sanjay. Thanks for joining us.

Thanks for having us. Yes.

That was awesome.

It was a pleasure.