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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...