AI and the future of the mortgage industry

by Ryan Smith09 Jan 2017

The technology space is constantly evolving — but it’s one space where many feel the mortgage industry is behind the power curve. But now powerful tools like artificial intelligence and machine learning — once the stuff of science fiction — are becoming reality. And that’s one area where the industry needs to be at the cutting edge.

“In 2017, machine learning means more efficiency in the mortgage loan cycle,” said Ken Bartz, co-founder of Sales Boomerang and Lead Squeeze and CEO of Monster Lead Group. “Machines could now start to effectively predict retention patterns and alert sales or retention departments to those most likely to be at risk. Machine learning will allow sales people to better manage and sell leads, processors and underwriters to do more with less and do it three times as fast, secondary marketing departments to stay ahead of market conditions. It will effectively make lending cheaper and easier for the consumer.”

It’s the simplicity and efficiency AI can bring that makes it such a powerful and important tool, said Alex Kutsishin, chief marketing officer for Sales Boomerang.


“I like to talk about organizations like Uber, that use AI and predictive algorithms,” Kutsishin said. “If you’re standing in a busy place where there are a lot of Uber drivers, how does it know which one to choose for you? Well, it uses this intelligence. It uses traffic flows. It uses what it thinks will be the fastest, easiest route for the driver to get to you. If I wanted to, I could order an Uber and without engaging in any conversation or reaching in my wallet. I could arrive at my destination and the fare would already be paid and everyone would be happy.”

That’s an image that appeals to Bartz as well.

“How do you think the consumer will react when a company makes refinancing as easy as catching an Uber?” he said. “Machine learning and the resulting AI will make a high-stress and non-consumer-friendly process, like a mortgage, frictionless.”

And Sales Boomerang and Lead Squeeze are already well on their way to making that ambition a reality. Sales Boomerang plugs into existing CRMs and acts as a tracking system to automatically alert loan officers to any number of opportunities, like an improvement in a prospective clients’ credit score or an increase in home values in specific areas, which is important in today’s rising-rates environment. Lead Squeeze uses voice recognition technology to help LOs seize opportunities they might otherwise have missed.

“Whenever I talk to people about our technology, they’re blown away,” Kutsishin said. “They say things like, ‘The mortgage industry is always the last to take advantage of a new technology or trend. You guys are doing something that seems like it’s up to date with what’s happening across the world.’ The mortgage industry is not being forgotten this time.”

Supplementing humans

It’s the kind of technology that mortgage companies are going to have to embrace in the near future to stay competitive, Kutsishin said. And while some people are concerned about AI replacing human workers — indeed, it’s already happening at a Japanese insurance company — for Kutsishin, AI and machine learning aren’t about replacing people, they’re about supplementing them.

“Some people are afraid of machine learning. But especially in the mortgage space — now and, I think, for quite some time forward — you’re still going to need people to work with other people to get things done,” he said. “So when people talk about machine learning, they want to build a computer to do something a human couldn’t do. That’s not how we look at it. We’re building a machine that’s going to do what a human could do, and would do. This, to us, is what an LO or a mortgage business owner would do if he had the time and the capability to do it at the pace or efficiency, but simply can’t because he’s got too many things to the think about and only so many hours in a day to accomplish his tasks. He’s got to think about life, he’s got to think about home, he’s got think about business. So he can’t go and track somebody’s credit. Maybe one person’s, sure — but he can’t do that for an entire database of 5,000, 10,000, 20,000 or 200,000. This is something people can do, and want to do. We’re just training a machine to do it the way a human would do it, but more efficiently. That’s our take on machine learning.”

AI and machine learning are also about helping human workers to think around corners, suggesting things that loan officers might not have thought of or pointing out opportunities they might have missed. It’s giving time and power back to the people and allowing them to focus on what’s important right at this moment.  

Machine learning is already being implemented all around us. Google Home can suggest music for users to listen to depending on the time of day or recommend a new recipe based on a recent interest in healthy foods. And that’s without users directly asking the AI for suggestions, according to Kutsishin; Google Home is trying to help you discover something about yourself that you might have missed or never come across on your own.

“That’s what machine learning is about — suggesting. What you do with that is your own prerogative,” Kutsishin said. “Lead Squeeze uses some machine learning and speech recognition to, almost in real time, analyze a loan officer’s phone conversation. Let’s say the loan officer gets off the phone and marks the conversation as ‘no deal.’ The system uses speech recognition and machine learning to say, ‘Hold on, this person said this _______, and you never responded to it.’ It sends it up to the manager and suggests to the manager, ‘listen to this call.’”

The system even transcribes the call and highlights the part of the conversation where an LO might have missed an opportunity. And according to Kutsishin, the LeadSqueeze system is constantly learning from every conversation it listens to and how the managers grade the call, much like how Pandora will find new music that users like based on what they say they enjoy listening to. The more calls LeadSqueeze analyzes, the better the results will be because it will begin to flag the calls that are similar to the ones that the manager has marked off as real opportunities.

“It gets people out of their own way,” Kutsishin said. “This person maybe didn’t do anything wrong, but simply has been conditioned to sell a certain product and just didn’t think about other products that the customer could clearly benefit from.”

Ease of use

It’s not just a general anxiety about intelligent machines that keeps companies from adopting AI, though — it’s also the fear that the technology will be too complex to learn. That’s why Kutsishin said it’s important that new tech is as frictionless as possible for the user.

“It really is management that makes most of these (technology buying) decisions,” he said. “But a lot of times managers think, ‘Oh, I can only imagine the headache of the team learning this thing.’ But with both of these products — Sales Boomerang and Lead Squeeze — there’s nothing new to learn. That’s the beauty. Good technology is like an electric toothbrush. If you’d only ever used a standard toothbrush and then purchased an electric toothbrush, how hard would it be to learn to use it? Probably not that hard; it’s very well thought out. It vibrates, put it on your teeth, rock and roll.

“When a new technology creates something new for people to learn, yes, there’s a big hurdle,” he said. “But when technology seamlessly integrates with your everyday actions, then it becomes one of those things where you go, ‘What did I do without this?’”

For more informations, visit www.salesboomerang.com 

 

COMMENTS

  • by John - Caliber | 1/10/2017 7:08:37 AM

    I was just reading about machine learning in robotics and found it so fascinating and to find this kind of article in this publication is wonderful. It's time our industry took full advantage of the vast technology landscape because so much of what we do everyday is inefficient and now we lose deals because of it.

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