How Valon Rebuilt a $13 Trillion Industry From Scratch

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

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 e-bills, 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.

It's thirteen trillion dollars of consumer debt that basically runs on a single incumbent that built their legacy system before the internet was invented.

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

Everything is servicing.

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

You started the company before generative AI was really working. What is possible today that wouldn't have been in your Series A pitch deck?

What is possible today is-

Mortgage servicing is a thirteen trillion dollar 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 record still matters, and what they've learned deploying new technology into enterprises with thousands of employees.

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

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. Prepayment models have stochastic calculus. And so when I started the job, I got very interested in making sure I mastered stochastic calculus. I'd taken a course in college, I was gonna do all that. And it turns out it's all finger in the air, just guess your way through math. So that wasn't really-

Just need to put the multiple actually. [laughs]

Yes, yes. You just need to guess some number. You're like, "Yeah, I think it's gonna prepay at X speed." Anyways, so that was my initial reason to get into