Will AI and Machine Learning Affect Your Mortgage?
“Machine Learning” and “AI” (or “artificial intelligence”) are common topics in the tech world… and science fiction movies. But do either of them have a place (or any power) in the mortgage industry?
Maybe not today. But that day is coming fast.
Forbes reports that Experian is experimenting with big data and machine learning to streamline the mortgage process.
What IS Machine Learning?
Machine learning is essentially teaching computers to teach themselves – much the same way as humans can – by giving them access to huge amounts of data, rather than having to teach them to do everything ourselves.
Technology is growing at an exponential rate, especially in the areas of artificial intelligence and data-driven analytics. And Experian has enormous amounts of data – 3.6 petabytes worth. (That’s 3.6 MILLION gigabytes of data, from people all over the world!)
The velocity of data collection is also increasing by leaps and bounds, which means it’s theoretically accessible in real time.
“A decision based on information that’s a month old is not nearly as impactful as a decision based on data that’s a day, or hours, old,” Libenson says.
For lenders, this is huge. They will be able to make decisions on the most up-to-the-minute details of your life and credit… as long as someone like Experian can process quickly enough. And this is where machine learning comes into play.
Where machine learning excels as a technology for driving change is its potential for automating complex but often mundane and time-consuming calculations at incredible speed, using information from vast and quickly-changing datasets.
Another hope is that AI and machine learning can help shorten the mortgage process itself. Countless hours go into the gathering and signing of documents alone.
Experian is now using machine learning to look at the data elements that are most frequently needed during the application process, and learn how it can most quickly be located and passed to where it is needed.
Experts predict the technology may roll out in 2017 or 2018, and be in widespread use by 2020 or 2021.