Magazine article American Banker

Ford Motor Credit Tests AI's Ability to Spot Overlooked Borrowers

Magazine article American Banker

Ford Motor Credit Tests AI's Ability to Spot Overlooked Borrowers

Article excerpt

Byline: Penny Crosman

Jim Moynes, vice president of risk management at Ford Motor Credit in Dearborn, Mich., first became interested in using machine learning to improve car loan underwriting several years ago.

"We were watching what others were working on," he said. "We like to be innovative and try to stay up with what's going on."

The company recently ran an experiment to see if machine learning could help its underwriters better understand the loan applications it receives.

It was a champion vs. challenger test: Moynes' team took several years of loan data, removed all personally identifiable information from it, and gave it to ZestFinance, a provider of machine-learning-based online lending software, and its own modeling team, which creates logistic regression models to predict potential borrowers' creditworthiness.

Each team ran the loan application data through its models and predicted the future performance of the loans. Moynes then compared the actual performance of those accounts over the past several years to the two teams' predictions.

The machine learning software won.

"What we discovered in this initial test is this more accurately places people on the scale from superprime down to subprime," Moynes said. "It does a better job than the tools we've been using today."

However, Ford Motor Credit will continue to test the ZestFinance software.

"It's going to take us a long time to move forward," Moynes said. "As we develop these models using machine learning, we'll continue to test them side by side with our existing model, and only after we go through that entire process over several years, checking the accuracies to make sure they hold up over time," will the company consider taking it live. That will take at least two years, he said.

"We're prudent lenders, we make sure we're thoughtful about anything we do, but the results that came out of the test were interesting to us, and we're going to study this as we go forward," Moynes said. "If we get to the end of the train and find out it's not performing any better, we won't make the change. But based on what we saw, it looks like a very exciting possibility for us."

What ZestFinance's software does

Douglas Merrill founded ZestFinance in 2009, the year after he left Google.

"I wanted to see if we could apply the kind of math we had used at Google to build web pages to the problem of credit for thin-file and no-file borrowers -- is there a way to have a financial inclusion play that allows lenders to make more loans but not increase their risk by doing so?" Merrill said. "I was thinking there's a huge market inequity in that people who are pretty good credit risks pay unfair credit rates. Banks are also hurting to grow their credit, and that's kind of a strange configuration of events."

According to the Consumer Financial Protection Bureau, 26 million American adults, or about one in 10, have no credit record, making them difficult to underwrite using traditional methods. This includes millions of millennials who are also starting to buy cars. Last year, new vehicles purchased by millennials represented 29% of all U.S. sales, and that number is expected to grow to 40% by 2020.

Traditional underwriting, Merrill said, does not work well for millennials because it only draws "a few handfuls" of data from credit bureaus and plugs it into logistic regression models. …

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