At Loan Factory, we built an artificial intelligence application tool that has been live for two years. A borrower or loan officer uploads documents, and the system reads them and populates the application automatically. We are now extending that further so the tool can interview the borrower directly, the same way a loan officer would when taking an application. Between document reading and conversational interviewing, we can capture roughly 70 to 80 percent of an application from documents alone, with artificial intelligence gathering the rest through a normal conversation. Consumers are intimidated by long, complicated forms. When the process feels like a conversation instead of paperwork, the borrower experience improves significantly.
“That is the same shift we saw when electricity replaced manual labor and when the internet rewired how business gets done.”
We have also built tools that review the 1003 and supporting documents to flag missing items before submission, along with systems that underwrite loans and calculate debt-to-income ratios. These are not experimental add-ons. They are doing work that used to require a person, and doing it in a fraction of the time. That trajectory tracks with a broader look at how AI is reshaping the American mortgage broker, where brokers describe recapturing hours once lost to administrative work.
Build versus buy
Building proprietary technology has worked for us because we have run an in-house engineering team for ten years. That is not the reality for most brokers and lenders, and it should not stop anyone from adopting artificial intelligence. For the vast majority of the industry, the right path is to subscribe to tools built by companies that specialize in this work, rather than trying to build from scratch without the resources to support it.
What separates real adoption from checking a box
Almost everyone in this industry will tell you they are using artificial intelligence. Far fewer are using it at a level that actually changes their output. There is a real difference between opening a chatbot occasionally and building it into a genuine productivity system, whether that means automating email triage and scheduling or setting up agents that handle the repetitive tasks you already know so well that you should not be spending your own time on them.
