Lenders traditionally make decisions based on a loan applicant's credit score, a three-digit number obtained from credit bureaus such as Experian and Equifax. Credit scores are calculated from data such as payment history, credit history length and credit line amounts. Upstart uses machine learning algorithms, a subset of AI, to make underwriting decisions. The platform's algorithms analyze 10,000 data points to evaluate the financial situation of consumers.
Today, lenders are applying powerful new tools based on artificial intelligence and machine learning to make the process of underwriting and approving mortgages faster and more accurate. As the process becomes increasingly digitized, (bank account activity, credit information, tax forms, pay stubs, and other required information) more and more lenders arrange for borrowers to pre-fill online forms with data directly from each source. Borrowers do not have access to information about their application and the status of their loan, so they must rely on loan officers to communicate documentation requirements and status updates. With modern technology automating manual tasks, these costs fall significantly, potentially saving money for both the borrower and the lender.
In has long lived in labs as a tantalizing possibility, but the growth in computing power, the increasing sophistication of algorithms and AI models and the billions of gigabytes of data spewing daily from connected devices has unleashed a Cambrian explosion in self-directing technologies. "Could you start offering people with Nest better mortgage rates before you start getting into fair lending issues about how you're biasing the sample set?" However, MIT Sloan professor Erik Brynjolfsson, McAfee's co-author on the new book Machine, Platform, Crowd: Harnessing Our Digital Future, acknowledged that it makes it harder for humans and machines to work together if the machine can't explain how it arrived at its reasoning. Scott Blandford, chief digital officer of TIAA, said companies have to worry about AI's black box problem because if "you're making decisions that impact peoples' lives you'd better make sure that everything is 100 percent."
All of this information would be impossible to manage or process without machines capable of learning and making decisions about data on a large scale. So far, humans have only been able to create machines that can grasp information, make decisions and act as the machines are told. Consider the fact that the IRS has been letting consumers file taxes electronically for nearly 30 years. Because tax filing takes much less time and effort than buying a house – for most of us, anyway – I find it unlikely that most borrowers will trust their home purchase to a website any time in the near future.
The pressure is particularly strong with FinTechs like US online lender Rocket Mortgage and UK digital mortgage broker Trussle creating a completely digital experience for prospective home buyers. This was the exact focus of Synechron's blockchain accelerator for mortgage financing and processing where we re-architected business processes and developed an accelerator application to help banks leap-frog the innovation stages needed to embrace this type of technology. Additionally, a public blockchain for real estate title, deeds, planning permissions, mortgage registry and other public records associated with the real estate assets could provide a second powerful application to further enhance these processes. In fact, in Dubai, Synechron recently won a leading position in a hackathon for its Land Registry team's blockchain submission which built an application to automatically generate title deeds on the blockchain.
Like all financial services, they are being rapidly changed by waves of technological innovation sweeping through industry – none more so than artificial intelligence and 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. "There's a movement towards open source technology which is less costly to operate and scales very effectively, so essentially you have a lot more horsepower at your disposal and can operate on much larger datasets. Larger datasets obviously give a more accurate picture of whatever they represent, leaving less margin for error.
All of this information would be impossible to manage or process without machines capable of learning and making decisions about data on a large scale. Consider the fact that the IRS has been letting consumers file taxes electronically for nearly 30 years. Because tax filing takes much less time and effort than buying a house – for most of us, anyway – I find it unlikely that most borrowers will trust their home purchase to a website any time in the near future. He has spent the last 10 years in the financial services industry, holding various positions at industry-leading technology companies, including Ellie Mae and Salesforce.
The FHA 203k loan program provides home buyers the opportunity to buy and fix up a property, without exhausting their personal savings. 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.
Customers interested in a loan complete a simple application at LendingClub.com LC leverage online data and technology to quickly assess risk, determine a credit rating and assign appropriate interest rates. Customers interested in a loan complete a simple application at LendingClub.com LC leverage online data and technology to quickly assess risk, determine a credit rating and assign appropriate interest rates. Home ownership: Home ownership: Does home ownership have any relationship with LC grades, FICO scores and Charge Off rates? The fico score does not reflect this while the LC score seems to partially capture that risk: Revolvoving balance and employement length: Revolving Balance, along with Employment length are actually the features with the least obvious link to default rates: ## revol_bal_bucket charged net_EL avg_fico avg_grade## (fctr) (dbl) (dbl) (dbl) (chr)## 1 0-1,333 12.93 8.19 734 B4 ## 2 1,333-3,170.2 Fully paying borrower tend to have slightly more accounts but too many accounts may be bad too.
And eventually it is theoretically possible to feed the borrower directly from the digital adviser into the lender's application system and then receive an approval in principle without the need to deal with a human at all. Hegarty adds: 'We haven't yet got to a stage where the Habito system can directly interact with lenders' application technology but it is something we have had conversations with lenders about and they're keen to develop their digital banking offerings, so we will see how that progresses. Malhi: The whole mortgage advice industry hadn't changed in 20 to 30 years and this was just widely accepted as a problem One borrower who used Trussle earlier this year had this to say: 'I had the option of either waiting three weeks for an appointment with a high street bank mortgage adviser at an inconvenient time of day, or giving Trussle a shot. He hasn't gone down the route of online advice, preferring to interact with customers and process the mortgage application himself.