Ten years ago in June something new and different entered the American marketplace. The iPhone was a hit from day one, the first “smartphone” and a hint of things to come. It was not just a communication tool, said Apple co-founder Steve Jobs, “but a way of life.”
In a similar sense we’re just at the start of the “fintech” – financial technology – revolution, the growing use of data by the real estate and lending industries.
“We can position our clients in front of their customers at the most optimal time,” said Alex Kutsishin, Co-Founder and Chief ROI Booster with Sales Boomerang. “We can analyze hundreds of millions of records, compare them to current market conditions and then give our client information that allows them to better service their existing borrowers, or even future borrowers. For instance, we tell our mortgage clients the moment an existing customer has 75 percent LTV in their home or when a prospect needs to get a call because they NOW are a good fit a loan product. We do all of this with almost no effort from our clients.”
Moving electrons back and forth in new ways will radically change both industries, and like all revolutions there will be both winners and losers. Ten years from now some of today’s largest and most important players are likely to be gone, replaced by – well, that is the big question, isn’t it?
The Data Economy
Traditionally the goal of every business has been to sell more and see profits rise as a result. With artificial intelligence (AI) – an expression which broadly includes such things as robots, machine learning, software, deep learning, 3-D printing, and automation — the path to profitability in the new data economy now involves revolutionary tools with the capacity to produce more, extract new efficiencies, reduce costs, and improve margins. It’s the difference between digging a canal with tractors and using spoons.
“Much of what we do with machine learning happens beneath the surface,” said Amazon CEO Jeff Bezos, in his 2016 shareholder letter. “Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations.”
For the past few decades “automation” has generally meant changes on the factory floor, the use of software and robots to endlessly repeat given tasks. The result has been a workplace revolution, one where production increased while seven million U.S. manufacturing jobs were lost. Between 1970 and 2010 manufacturing blue collar employment shrank from 25 percent of the workforce to just 10 percent.
The initial efforts to transform manufacturing involved such basic tasks as using a robot arm to move parts and weld. In a similar sense, we’re only at the starting point of the AI revolution. There’s more to come and what comes will transform real estate and finance with the same zealous efficiency that re-made the factory floor.
AI Applied to Fintech
Consider the problem of loan applications. From the borrower’s perspective applying for a mortgage is a huge hassle with tons of paperwork and lots of nosy questions. The AI alternative is to use an online form, provide some basic information and bingo; you’ll have a solid sense of your borrowing ability in minutes.
What’s really going on is that lenders are increasingly interconnected with data sources. When a borrower applies for a speedy approval, hordes of electrons are instantly dispatched to the far corners of the financial world. Information — data points — from credit reports, bank accounts, public property records and other sources are instantly gathered, analyzed, and then plugged into various mortgage options.
Democratizing The Marketplace
When it comes to competition a lot of the marketplace is simply off-limits to newcomers because few people have the dollars to open a new supermarket chain or steel mill. However with AI the nature of competition is different. The capital barriers to entry are low, in large part because companies of all sizes have access to the same essential tools.
“Technology is democratizing everything in favor of startups, from compute and storage via AWS to global brand distribution via social media channels,” said Bryan Copley, CEO at CityBldr, a company that provides estimates of what a builder or developer would pay for a property based on its highest and best use. “Small companies have advantages in today’s economy that they didn’t used to have. With a differentiated product (and without legacy products to drag along) startups can fill market gaps less nimble incumbents haven’t filled.”
Ken Bartz, Founder and CVO of Monster Lead Group, a company that mines public record property and loan data to identify leads for lenders, explains that “the cost of technology has such a low barrier of entry that even a very small company can utilize the same tech as the largest companies.”
A strange thing has begun to happen. It becomes clear that fintech is not just a shiny new thing, it’s actually producing results.
Copley says his firm “met with five neighbors and told them their homes were best sold together. Leading market valuations priced their homes at a total of $1.9M, but our AI said a developer would pay $3.2M to use the aggregated site to build apartments on. We were wrong — the developer paid $3.5M. That solution was a direct result of the investment we’ve made in machine learning.”
Consumers, of course, are also AI drivers. If new technologies mean less paperwork and fewer hassles, if they mean lower costs — that’s great, bring it on.
“As a customer I would love to get a call from my mortgage banker letting me know that without a shadow of a doubt I have enough equity in my home to get rid of my mortgage insurance (MI) or that based on my current FICO score I can get a better loan,” said Kutsishin of Sales Boomerang. “Or even more importantly, I would forever be grateful to the LO that calls me and tells me I am now qualified for the loan that I was denied for a few months earlier.”