How Valon Rebuilt a $13 Trillion Industry From Scratch

Linda Du (0:00)

Mortgage is top three in terms of undisrupted industries that exist. This is an industry that hasn't even caught up to 2000s technology yet. That's sort of the size of the opportunity in front of us.

Andrew Wang (0:13)

The way that we are going about it as an industry is creating the infrastructure so that it's a continual learning process. Building a system that allows you to continuously run the evals, make sure you're actually continuously improving the model in a direction that is aligned, not just with the way that servicing should be done generally, but customer specific.

Linda Du (0:30)

It's $13 trillion of consumer debt that basically runs on a single incumbent that built their legacy system before the Internet was invented.

Andrew Wang (0:39)

It turns out revenue cycle management is just servicing for hospitals.

Angela Strange (0:43)

Everything is servicing.

Linda Du (0:44)

It's the underlying critical infrastructure that supports basically anything that has money movements, some sort of regulation, and then an operational component.

Angela Strange (0:52)

We started the company before generative AI was really working. What is possible today that would have been in your series A pitch deck?

SPEAKER_4 (1:01)

What is possible today is mortgage servicing is a $13 trillion market that still runs in large part on technology designed before the Internet. Valon decided to rebuild it from scratch.

In this episode, a16z general partner Angela Strange sits down with Valon's Linda Du and Andrew Wang to unpack what it took to replace decades of legacy infrastructure in one of the most regulated parts of financial services. They discussed why Valon became a mortgage servicer before becoming a software company, how the team turned decades of regulation into code, and why operating the technology themselves was critical to proving it could work at scale. And now, AI is changing what can be built on top. Linda and Andrew explain how agents can handle workflows that were previously too complex to automate, why the underlying system of records still matters, and what they've learned deploying new technology into enterprises with thousands of employees.

Angela Strange (2:01)

So, Andrew, I think a great place to start would be from your time at Soros and how you decided that mortgage servicing was going to be a really interesting problem to work on.

Andrew Wang (2:09)

So, a fun story is that when I got into the world of finance, I actually had zero background in finance. I never took a course in economics. I barely understood what the time value of money was. And so, my general interest in finance was actually just in math. And what people don't really know about mortgages, which is just like a very random esoteric fact, is that it's actually a place where there's a lot of stochastic calculus. Pre-payment models have stochastic calculus. And so, when I started the job, I got very interested in making sure I mastered stochastic calculus. I had taken a course in college. I was going to do all that. And it turns out, it's all a finger in the air. Just guess your way through math. So, that wasn't really...

SPEAKER_4 (2:46)

Yeah, you just need to guess some number.

Andrew Wang (2:47)

You're like, yeah, I think it's going to prepay at XP. Anyways, so that was my initial reason to get into mortgages. What happened thereafter was you end up realizing, working with many of the mortgage servicers, that it's a big systems engineering problem because there's a ton of data, a lot of money moving around, a lot of entries. They have to constantly be reconciled. And so, I spent a lot of time at Soros when I was making these investments, really making sure I understood exactly what was going on building my own reconciliation systems to make sure the money tied.

Turns out, it never tied. And so, I spent a lot of time trying to fix that problem internally. And that got me really interested in figuring out, hey, how can I actually build better infrastructure? How can I invest maybe in a company, do it with a company, and actually ultimately build better systems so that none of this would be a problem? So, it really started originally off of systems in my own pain. Maybe I would say it started off with math. Math became systems, systems became pain, and here we are today.

Angela Strange (3:44)

Excellent. And so, discovering a market has a lot of technical problems to solve is one piece of it. Maybe Linda, from a market perspective, describe the state of the world as a mortgage servicing industry before Valon came in.

Linda Du (3:55)

Yeah. The way that I think about it is mortgage is probably top three in terms of undisrupted industries that exist. And so, it's $13 trillion of consumer debt that basically runs on, for the most part, a single incumbent that built their legacy system before the Internet was invented. And so, we're not talking about, for most industries, you are fighting with modern incumbents, new startups for the last 20 percent. We're talking about the entire, it's like, this is an industry that hasn't even caught up to 2000s technology yet. And so, that's sort of the size of the opportunity in front of us. And from a deployment standpoint, these are like the biggest, baddest enterprise deployments to exist. You have to train 1000s of employees. You have to basically rip and replace the core operating system. You're actually deploying AI into the real world in a very heavily, heavily regulated industry. And what we always tell the team is that the skill set that people will be hiring for in the next decade is the ability to apply AI.