Inside the $13T Mortgage Machine

22:44But that most of the industry didn't want to use made technology, wanted to use its own technologies and it wanted to promote competition and innovation in those technology providers. So what we tried to do was to create the conditions where those technology providers would see that there was a good opportunity to gain a significant again, market share. And so we basically pivoted towards trying to drive as much a digital adoption as possible and to facilitate our consumption of that digital data. Virtually everything that relates to a mortgage loan is just a piece of digital data. It's about consumer credit characteristics, it's about property characteristics.

23:30And today, almost all that information is digital data. And so we put fanning in a position to be able to consume all that through APIs and try to encourage originators, lenders, and servicers to be able to communicate with us in that way. And we would facilitate that, and we'd actually try to make it easy for them to do it, and we create certain rewards for them to do that, like day one certainty, which gave them relief from certain kinds of order known as representations and warranties in the industry that created potential liability for them. So we said, if you deliver the data to us in a reliable way using high -quality data, we'll consume that and we'll give you relief around that.

24:11The other thing is that from my point of view, everything about the consumer experience and everything through the lenders is like slower, more complicated, more expensive than it ought to be. And so my view was that we should try to drive this digital adoption and make it faster, more convenient, cheaper for everybody, but not compromise any of the risk metrics involved in this business. Like, we were not going to take on more risk in order to be able to do this. We actually thought that by using verified digital data, we could actually drive the risk down for the consumer, for the lender, for us, Fannie Mae, for the end investor in the mortgages.

24:55but we could actually make it also cheaper, faster, easier for everyone involved. And Tim, do you have a couple of examples of either data that's available that potentially the GSEs or others are not able to make use of or things that consumers are historically used to paying for that maybe aren't necessary anymore in evolution so the industry could go through? Sure. So look, still much of what passes for credit and a writing today is basically just like proxies for real data. So what do I mean by that? Today, it's possible to actually deliver people's digital bank account statements to lenders or let's say to Fannie Mae and we could actually peer into that data and we can see exactly how much money is coming into someone's account every month and how much money is leaving their account every month.

25:46And through in that way, whether there are regular W2 employee or there are a gig worker or they're an independent contractor or they own their own business, you can actually get a much clearer and more refined sense of what their true financial picture is, then by relying on things like credit reports or credit rating scores or those sorts of things. So our view is that we should be consuming the real data. Let's try to do real time, nuanced analysis of that and be able to underwrite people more effectively, but also more accurately, and hopefully actually be able to expand the class of borrowers who get to get access to credit because they might be non -traditional borrowers.

26:31So that's one example. Another example is that I just look at how much money consumers spend in this process, the mortgage process, without getting a whole lot of real value out of it. So for example, every mortgage loan that's underwritten or backed by the GSEs requires requires an estimate of value of the property. So what does that mean? Well, the most consumers end up having to go out and pay some appraiser, typically a guy whose average age is 60 years old, going out at the Tate measure and measuring how many square feet there are in a house and verifying the dimensions and the condition of the property.

27:08The reality is that they're much faster, easier, more accurate ways to collect that data. And once the GSEs have that data, they have enormous databases of property values much bigger than any other private party has in the United States. And so at Fannie Mae, we already knew what the property was worth. But we really just wanted somebody to go out and verify it was there and confirm that it hadn't burned down or or had been struck by lightning. But there are much less expensive ways of doing that paying somebody a couple thousand dollars to go to an appraisal. And we actually really didn't care very much about the statement of value from the appraiser, which we found to be not as accurate as our own estimate, and often was actually quite biased one way or the other, intended to be actually less reliable, the higher the loan devouse, the more that you were actually looking for the property value to cover the risk, that's when the appraisal was actually the least reliable.

28:04So you think about what all that costs the consumer, and that's just one example. This is an in effect and indirect tax on all transactions that policymakers, regulators, the GSEs, lenders, consumers, we should all have an interest in trying to reduce that number to as close to zero as possible. So Mike, I want to come back to building a loan origination system. And anytime you're trying to wedge in as a new company to an entrenched industry, there's generally two strategies. One is build a, these days, interesting agents or other workflow on top and then use that as a wedge into becoming the system of record.

28:45The other is, all right, just take the pain at the beginning, build the entire system and then sell a software that's way better. You chose the much harder ladder strategy. Talk about why. Yeah, I think that there's a, FinTech has had like a debate over the last decade probably around whether you do people call it core replacement or core wrappers. And I do think that 10 years ago, everyone was in the core wrappers camp. My favorite is the company that I don't think was an injury -symphorfolio company. It got acquired called mantle. And it's very called mantle because the mantle is the thing around the core, the earth.

29:19And to me, that's like the pinnacle of the fintech strategy of that version of the strategy. And I think for us, I had the fortune again of working with Tim at Blend. I think for us, a blend in many ways was like the wrapper on the LOS. And the question was, hey, can you actually drive a lot of the foundational change in the back office that you want to change without going in and replacing the whole LOS? And I think the answer basically was no. Like, I think that if we had tried to build something as a wedge around the whale that is encompassed, I think we would have probably ended up in a similar place where we could have added some value, but it would have been very hard to drive the fundamental change with lenders that you need to drive.

29:53The other thing that I've noticed, for example, when we're implementing the LOS, is like, as we go into a customer and we go into play Vesta and we rip out whatever the existing LOS might be, a huge percentage of the project is actually not deploying the technology. And so it's not building the system, it's not setting up the system for them and configuring it. It's actually the operational transformation of changing the way that these people do work and the way that they think about the process. And you have to achieve that operational transformation, kind of almost regardless. And so in some ways, it's worked out really nicely for us because being that biggest project, being the fundamental platform and changing that is like the only way really the force a lender to understand that they're also going to have to do the operational transformation.

30:32And honestly, a huge percentage of the value, of course, comes not just from the fact that you're using a new piece of software, but that the software enables that operational transformation. And that's what gives our customers really good results. And so I think ultimately it was just the blend experience after having it made kind of obvious, I think, to Devon and I that you also kind of get the curse of success where you have a big customer base. They're all very happy. Now you have to go take on as a competitor, your biggest partner. And I think that also is like a very awkward place for business to be.

31:00And so for us, it was like you have nothing to lose to go after the big price day one. I think one of the other points you made, like for instance, if you notice it's a consumer as you apply for mortgage, it's a very linear type process. And one of the innovations of Vesta is you've completely rethought the workflows. But in order to do that, you also needed to change and adjust the data structures. Yeah, and I was on a demo today with the prospect where they were asking, basically, hey, this process is so dynamic, right? everyone talks about task -based workflow and mortgage origination. And one of the problems that happens is you get halfway through the loan, and then something happens.

31:33For example, when the borrower gets married, I was like, OK, I don't think that many boroughs get married in the middle of the mortgage. But it does happen sometimes. But other examples are like, all right, that appraisal comes back low. And that means your LTV is above 80. And now you want to switch from a conventional to an FHA loan. That kind of stuff happens all the time. And so for us, to your point, it was like a maniacal focus on getting the right data model and then making sure the entire process was going to be data -driven. So that you don't have this weird path -dependent work workflow that you're trying to sketch out that is super complicated and has to satisfy thousands of pages of rules.

32:02And it's much more, if you model the data correctly, you can figure out all of the steps that have to be taken, looking only at the data, ignoring all of the steps that you've already done in a way. I don't know if that's really an innovation or that's just like an approach that we've taken that's been very successful, but like a very maniacal focus on data model and everything being purely data driven, I would say is a big part of why the architecture works at all. Yeah, and I think one of the most surprising facts to me is with many of the existing systems, only one person can be in the loan at a time.

32:30That is a surprising fact to pretty much everyone we hire at best at yes. A lot of these things were built on not like SQL databases in many cases, but actually even older like the flat file databases. And so you like have to automatically save the entire loan file in a save button. And so actually some people have hacked their legacy LMSs where you can have two people and file at the same time. If you actually do that, their saves will like overwrite each other to the tune of like hundreds of fields and there's no merging mechanism. So yeah, there are like database level constraints that certainly prevent good dynamic parallel processing and existing systems.

33:04Hence the rebuild the whole thing. Okay, Andrew, you've also chosen not to wrap existing servicers. So you started by not only building your own software, but also being the user of the software or a subservicer in Interstate Paralympus, which is one enabled you to scale very quickly. And then two, when we talk about how the business of surfacing is usually a 5 % margin business, you've taken it up to a 50 % margin business. So what did it take to build that and where are you going to go from here? Yeah. We're going to take to build that a lot of blood, sweat and tears. So we started the company six years ago and we went through the process of like, okay, when you start the company and you first have to get licenses, it turns out when you get the licenses, sometimes they say you actually have to have a certain number of years servicing before you can get the next set of licenses So you like went through that entire sort of okay, let's do one step at time Let's get this first set of customers.

33:57Let's do in a self -set up states Etc etc etc And then there's a lot of like step -wise function through right because once you like know how to service a loan You can probably service 10 loans a hundred loans But you'll still like mess up on a whole bunch of different things and then you stop for while make sure you fix your core system because like Mike, we really, really believe that you have to fix the core to do a good job. And then once you've done that, you can say, okay, let me 10x again. And so, six years into that journey, we're a top 10 player. We have 1 % of the US market share, and we're probably the most profitable service or developer exist.

34:29We are putting all of that behind us, and we're turning 100 % of our attention out and focus to providing software throughout their services in this space. And that's how we actually get enough market share to have our product affect other people's lives, right? And what's really incredible is that the amount of receptivity we've had because of the work that we've done on the offering side many of the United States is Largest and most prominent servicers have signed up with Valin and that means we had pipeline of hundreds of moons in AR and what more We now see this really really large opportunity to not just go after the mortgage servicing business But really build an OS for servicing in general or regulated financial type businesses And so it's a really incredible moment from going from a place where we were trying to get a servicer stood up and we just literally wanted to make sure we survived and it worked.

35:14To a place where we're now in this place where these whales will like a bear way to put it or now in a position where they're saying, hey, we've seen what you've done. It's amazing. It's honestly transformative and now we're really excited to use your software as the way we power businesses going forward. So clearly we all think Morgan's is exciting. We are in the middle of one of the largest product technology waves of all time, which is artificial intelligence, which is going to apply everywhere. Maybe starting with you, Mike, where are the most realistic immediate applications of AI and mortgage?

35:46What could work today in production? Yeah, I think the obvious use case that people have been trying to do for ages and origination that we've deployed in production is reading documents. It's kind of like a very, unfortunately, basic use case. But the reality is, if you think about the mortgage origination process, it's basically, you've got like a pile of data that describes the bar in a pile of rules that you get from the GSEs for the most part. And a lot of that data lives in documents today. And so a huge percentage of what you're actually paying human beings to do is read the document, extract the data from the document, key that into a system, compare that document to other documents.

36:21It's all really like document management. And so for decades, the industry has been trying to figure out how do I take the data out of the document and just put it in my LOS or put it in a schema somewhere that I can manage it. And people have spent tens of millions, hundreds of millions of dollars It's like training small models, like training convolutional neural networks. Even before there were convolutional neural nets, like training other stuff to try and like read documents. And for the most part, the only thing you could really read super successfully was like W2s. And of course, a lot of the advancements in language models have meant that reading things like purchase contracts or title insurance policies or closing protection letters have become problems that are frankly actually really simple to now hook up because you just plug them into a large language model.

37:00Of course, there's a bunch of like orchestration you have to do. You have to do a certain amount of reasoning and consensus across multiple LLMs and prompting and whatnot. But for the most part, it's a much lighter weight effort to basically say, hey, I have an API that can read and I have a bunch of documents that need to be read and passing that document there and getting the data out, I would say is a place where we've seen a lot of returns for customers already. And it's a pretty easy straightforward case. There's lots of other more interesting and exciting things you can do as you start to unlock like computer use and can help copier your configuration and there's all sorts of things we're thinking about.

37:30But the obvious low -hanging fruit is the problem that has been there for years and years and years And that's just some laps and several Cisco gave us an easier to solve it We use a lot of the same sort of use cases as Mike We use all of them to do some rosation. We have voice agents. We parse documents all the types of stuff I'm gonna say some things that will probably drive some amount of industry permit What we find super interesting where we're going to really get to right is Personalize AI agents and so if you think about what we do as valid and we basically have big workflow system and task system and all these different primitives and mortgage servicing.

38:04And we basically allow our users to stitch together and put together their processes on a workflow system. And that's great. That's awesome. They can sort of orchestrate. They can control all their business processes and have a lot of disability and auditability all that episode. What's really cool though is that we also have the tasks that humans do when you need a human in the loop in that workflow, which happens to be a really amazing place to get a lot of training data whenever the workflow gets done. Think about it as there's a bunch of things that have to happen, so I'll have context. There's a task that's the next step, which is human in the loop.

38:37They figure out the answer. They make a decision that the decision gets reported. And now you basically start to create these training sets. And what's really great about that is that then you can automatically start training AI agents based on that specific task, which basically means if people put their entire business process is on our workflow engine. They then are able to build automatic AI agents for that specific task. And so you have this like amazing thing where you're able to then get people to not only put their business positive into automated winters, but they can have their best people work on specific workflows.

39:10And you can learn from that. You can sample information that you need. You can reinforce them and learn. And you can effectively clone your best people. And so what that basically means in our minds is that you can create a world where by building on Valin, and you're building for an AI world by default, right? And you're not only being able to automate a lot of your work, but you're able to clone your best people. And that to us is like an extremely exciting thing. And also honestly at this point, again, to Mike's point about some really, really great researchers in San Francisco doing a lot of great work for us.

39:39That's a world that is actually possible today. And we see that as a near future reality. Yeah, I think one of the most exciting things going on here is the software you can now sell in, like the attempts before might have been 2X better. This is now 10x better in that all of the things you wanted it to do, A .K .A. Document Ingestion, now actually work. It can incorporate and scale some of your best employees. And then I think Mike, to your earlier point of the why now, financial services used to be slow at buying. And now there is a tremendous urgency from boards, from executive teams that are very much embraced.

40:12The gents and quotes that AI is not your competitor, it's your competitor that adopts AI before you. And so bringing that into your, This is a very, very good way to prepare for the future. Tim, I want to ask about AI in regulation. And there is a, like, every industry, an increasing amount of regulation in this industry. Even just the Fannie Mae selling guide, I think is 1200 plus pages. How do you think about how I could potentially both help regulation but also make it more efficient? Interestingly, one of the first things that we did in terms of using language models at Fannie Mae when I was still there and I left in 2018.

40:50So it's been a while since I've been gone, but we actually took the entire servicing guide and applied machine learning to it and created an inquiry system that allowed lenders and servicers to submit questions using regular language and get answers without sifting their way through the 1200 pages of material, which today doesn't seem like an amazing thing to do, but seven, nine years ago was really something quite an important step forward. And so I think it's an example of the kinds of things that Mike and Andrew are talking about, and the technology has advanced so much, so quickly, that I really do think that virtually everything relating to the regulatory aspects of mortgage will end up being subsumed by AI.

41:42This is a way to essentially take all the rules and processes and procedures required by the GSEs and make them into software code and not only can lenders deploy it that way, but at the same time, the GSEs and the regulators could actually enforce and in monitor compliance with that using those same tools. So much of what happens in the mortgage business is really just a function of human error. There's some fraud, but it's relatively small these days, but there's a lot of human errors that occur. And if you can take the human error out of all this and replace it with code and be able to monitor it with another set of code, I think it makes the life of the lender much easier and in the end, for my point of view, the goal here is to drive down the cost of the consumer.

42:34So if you can replace all those people who are doing their best to try as good a job as possible, but nonetheless committing errors and replace that with these technologies, it's better for the lender. They'll be able to improve their cost base, but it's also better for the consumer because they'll end up paying less for it. And in the end, it doesn't increase risk. It actually reduces risk. It should make regulators happy. It should be consistent with what policymakers want to see. Mike, you talk to probably the heads of mortgage across most of the major institutions at the country. They're all wondering, how should I prepare for the future with AI?

43:12And what is the advice that you have? I would say they're a handful of things that really matter here. One is that most of these institutions are probably not going to get there on their own. Of course, that's also a little bit self -serving of a statement. But I do often use as a mental model. We've seen like meta throwing around like a hundred million bucks or whatever for researchers. And so the question I often ask mortgage lenders in general is, but someone who can make $100 million from meta really be in the mortgage industry at all, let alone working at your lender. And so I certainly think there's like a diffusion that will have to happen over time, but I certainly think that people that use partners and that find the right technology partners to work with, whether those be existing mortgage vendors, other partners that are more general purpose are going to be the ones who are able to get ahead first.

43:57I often and advise people that what you want to make sure you're doing is you're figuring out how to benefit from all of these really smart people, mostly in San Francisco, who are working on making intelligence via API better and better as opposed to competing with them. And so there are still lenders out there who have told me they want to train their own models or they want to build their own agents. And for the most part, I think that is probably not the great idea. And then it's really about how do you make sure that you are positioning your technology in a way that you have partners who are going to get better as the models continue to get better and that you're a business will basically allow you to be able to take advantage of those models.

44:30So how are you changing your processes, changing your staffing, changing the profile of people that you're hiring, so that when the technology is much better one, two or three years from now, you're really like oriented towards that. And I think a lot of people see this AI thing and people always ask where will the value accrue in the stack and they're like, oh, it accrues to the models or the app companies. So I have to go build my own apps. I have to go train my own models. When I always want to remind people, yes, value will accrue to those two places. But most of the value in this AI revolution is going to accrue to the people who use the AI.

44:57And so how can you make sure that as a mortgage lender, you are really well positioned to be one of the people who use the AI and that you're working with the partners who will bring you the actual innovations and AI as opposed to the people who are kind of like using AI to go fish for customers as a buzzword, which there are a lot of them. And so that definitely I think is the hardest part is partner selection. And you're saying question to you. Yeah, I think there's a lot of truth to like, We pursue certain strategies because we think that is true and obviously it's a little bit self -serving because it is the strategy that we pursue.

45:28There's two things that I would think about. One is, look at them today, when you think about what does AI need, it needs a lot of context, it's really, really great with more context, it needs a lot of training data, it needs access to tools. And so, like Mike said, don't try to compete in the middle of the equation, right? Compute on really having the right platform to utilize, or you have to decide you want to be a foundational model company, That sounds like a bad idea though. So the question for me is more so, how do you centralize all the contacts you have? How do you have clean trade data?

45:57How do you put as much of the foundational parts of your business in a way that AI can leverage and you can utilize it with the stuff that a lot of people are building? That will make you a big beneficiary in this outcome. Because ultimately, to me, who are the biggest beneficiaries of AI? It's like the customers and it's basically the operating systems that they run off of. It makes it like a really, really great outcome. So I'd say that's one big thing to think about. And then the second thing about is you should really think about mixture between the right partners and the right platform that you're working with right because we work in an extraordinarily regulated space.

46:29And so you need the right guardrails, you know, people thinking about agenteic behavior. Agenteic behavior is kind of troubling right when it comes to a lot of the things that we need to do. There's a specific set of processes and specific set of steps that you want to be able to follow so that you can explain it. One of my favorite stories is when I talk to the folks at FICO back in the day about why do they still use logistic regression to effectively come up with FICO scores. And the answer was, it's just explainability. I need explainability because I need to be able to say this parameter did why in a very linearly explainable way because anything but that, right?

47:01And this is 10 years ago when gradient boosting and whatever else was all the rage, that's not explainable. And then that means when I get in front of the regulators and I get and ask all these different questions, I won't have a really great answer. And so I think working with the right platforms would aid structure their solutions and their infrastructure in a way where you can kind of crawl, walk, run, sprint, right? So you can do all of it in the right sort of steps and processes. We'll be the right way and also the fastest way you'll be able to leverage AI for your business. Looking ahead fast forward in 10 years.

47:32What is the best instantiation of the mortgage system look like? Well, 10 years is a very long time, especially in today's technology world. I think that in 10 years consumers, assuming that the capital market structure doesn't change, which I think is like a debatable assumption in a decade, I think that you'll have consumers really with near instant fully automated one touch experiences. And you'll still have compliance and disclosure and a whole bunch of things that you have to just kind of work through and kind of like opening a deposit account, you still have to sign like a million disclosures to get a deposit account, even though it's like a one session experience.

48:02But I certainly think that this fulfillment will be automated. It'll all be source data and the execution will be much better on the back end. Let's end on vision for a utopian future. Fast forward, X number of years. It's a more modern, more efficient, more transparent, pick your favorite adjective. What does that look like? And what are one or two things that are needed to get there? Two angles. I think that two interesting things that I think about are capital markets, and then I think about what the experience is going to be for a consumer. And for the consumer, I think it's more dystopian.

48:32The capital market side, I think it's more just like an interesting to think about, right? This is a very interesting, very niche, but important topic, but one of the great inventions of basically the US mortgage market is this 30 -year fifth straight mortgage that every big financial, sovereign wealth fund institution, whatever else, buys from American homeowners and basically earns extremely low spread and returns on relative to the risk that they're taking on terms of prepayments and whatever else, right? Because of this amazing sort of like liquid ecosystem that they've all built. And so as things get more efficient, right?

49:09Fundamentally, what ends up happening is that people should immediately refinance and so basically rates keep dropping and so this instrument becomes less and less like valuable, right? Today or the way it works is there's a little bit of a spread above treasuries. You basically buy it because some people won't refinance when rates go down. So that's sort of like why there's a excess return and there's like a call option built into it. And so all of this financial mathematics works because there's truly inefficiencies built into the system. When the inefficiencies disappear, that instrument looks very, very different.

49:35There's a reckoning at some point in time, the future that will happen because of that. That's more like an interesting financial mathematics exercise that financial deakes will geek out on. The second thing is this, again, more consumer oriented view, which is kind of dystopian. I just personally have started to believe that this will happen. If you think LOMs are just going to do to eat up more and more tokens and have bigger context windows, whatever else, I think what's going to end up happening is that like a mixture between near link or like people having, if you've ever watched a movie circle, there's like these things that fall around, like record you everything.

50:07But basically the idea is, look, at them day to day, even today, I have so much information about consumers that I can embody and really encapsulate all their information a little bit still, but in the form of tokens that can be fed to any originator, right? Like I have all the contacts about their loan documents and their contacts about their work history, all that type of stuff and also their pay history. And if you get more and more of that and that gets constantly fed into a machine, I mean, you can basically supply any super app or any sort of like customer, all of this embedded context about that individual, which means that they can take that, immediately get a new loan, you know exactly what their mindset is, exactly what's going on, and everything becomes instantias.

50:42It's more dystopian than it is like utopian, but I just think it's gonna end up going there because if you look at the sequential steps that take place, that's what's gonna end up happening. It's kinda cool to think about, kind of scary to think about at the same time, but you know, it is what it is. It's like push button, get mortgage, but you don't even need to push the button. Yes, exactly. Ten, what about you? Well, I don't want to promote a dystopian view of the world. It may come to pass as Andrew suggests, but I think there'll be a lot of good that comes out of it as well, and there'll clearly be a lot of change.

51:14One thing that I think is necessary in order to kind of drive all this forward is because the government and the GSEs play such a big role in this system, policy makers, regulators, the management of the GSEs need to be involved in this set of changes. And those conditions don't really exist today to promote this kind of innovation. Fannie Mae and Freddie Mac have been in conservorship, government conservorship for the last 17 years. When I joined Fannie Mae, I was told that the board of directors had been assured by the highest levels of the United States government that the future of Fannie and Freddie would be recharted in 12 at most 18 months.

51:57That conversation was in December 2008. It's now 2025 and there's still in conservatorship. And in addition, in that time, regulations have been passed that prohibit them from engaging in any new products or any new activities without an elaborate regulatory approval process. Which includes public comment And from all the entrenched vested interests of the current mortgage industry, like this is all set up to slow innovation. This is all set up to keep the existing power players in their current positions. And if what you really wanna do is to drive all this innovation, with the ultimate goal of better protecting the taxpayer, making the system safer and better, driving down costs for consumers, that sort of conditions is not helpful to that.

52:46So what I would say is the conservator and regulator of the GSEs, the Federal Housing Finance Agency, ought to be seriously thinking about not only how it took, make it easier for innovation to happen, but actually mandating that this innovation happened, even if it means that certain current entrenched interest get disintermediated or their role is reduced. Because there's no path to innovation where everybody who currently makes money money, makes the same amount of money, and nobody else makes any money. Like, innovation comes with disruption. And if you want the innovation, you have to be willing and actually promote the disruption.

53:23And our current regulatory structure, especially conservatorship and the current regulatory regime, discourages all of that. So I think we're finally at a point where given all of the financial pressures on the mortgage industry and all of the technological innovations, and really the brilliance of entrepreneurs like Andrew and Mike, that actually tried this change, but we need to make sure that the policy environment, including the regulatory environment, adapts to it as well. That is just the absolute perfect note to end on and extremely well said. Thanks, Sam. We could have done a whole excerpt just on that.

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