Mortgage startup Copperlane targets origination inefficiencies with AI loan officer

As lenders weigh how to adopt artificial intelligence under evolving fair lending and compliance expectations, Copperlane is pitching a full-fledged AI “employee” rather than another point solution.

The startup, founded by 21-year-olds Athan Zhang and Brianna Lin, recently More importantly, she gives loan officers context around the borrower. Penny is one of the things that interacts with the borrower throughout the intake stage, so we can surface the most important pieces of information to the loan officers. That gives them more bandwidth to understand more customers at the same time, while Penny works to do things such as figuring out why parts of an application don’t line up, Wolak: How did you develop this idea? Was it a class project that evolved or something you both had a personal interest in?

Zhang: It was not a class project. For background, both Brianna and I are from the Washington, D.C.-Maryland-Virginia area, and both of our parents have worked extensively in the mortgage space. So growing up, we were always exposed to these problems.

When we went to college, this wasn’t necessarily what we set out to do. I was a computer science major; Brianna studied computer science and Compared to many people in the mortgage industry, we understood more about the frontiers of AI, which is important for governance and alignment. On the flip side, compared to a lot of our peers, we just knew more about mortgages than the average person because of our proximity and what we grew up with.

Wolak: Can you explain how you saw inefficiencies in the mortgage space without having direct industry experience yourselves?

Zhang: The actual story starts with a conversation with my mom. I generally know what she does — she works in the Wolak: The mortgage industry traditionally lags on technology, but there are companies with internal AI or AI assistants. For instance, There’s a lot of cold feet around AI because we don’t really know what safety and governance will look like. That’s part of why Brianna and I got into this. We come from AI research backgrounds and environments where you’re exposed to alignment work. Alignment as a process really started gaining traction only recently, so we know it’s still early, which some industry leaders might not fully appreciate.

When we build Penny, we’re positioning ourselves with the expectation that Wolak: To achieve what you’re describing — thinking and acting like a real person — how long did it take to implement everything? And with compliance, some people might argue that mortgage veterans building AI know how to best address fair lending concerns. How are you building those considerations into Penny?

Zhang: It’s not a set duration. This is always an ongoing process. In terms of legislation, it’s not as if we’re unaware of fair lending,