Ford Motor Credit and ZestFinance recently announced the results of a study that measured the effectiveness of machine learning to better predict risk in auto financing and potentially expand auto financing for millennials and other Americans with limited credit histories.

As a result of the study’s success, Ford Credit said it is developing plans to implement machine learning credit approval models to further enhance its consistent and prudent lending practices across the credit spectrum.

“At Ford and Ford Credit, our primary goal is to serve our customers,” Ford Credit chairman and chief executive officer Joy Falotico said. “For this study, we worked with ZestFinance to harness the capability of machine learning to analyze more data and to analyze our data differently.

“The study showed improved predictive power, which holds promise for more approvals, enhanced customer experiences and even stronger business performance, including lower credit losses,” Falotico continued.

The captive explained that Ford Credit’s proprietary models have performed well for decades. The machine learning study compared results from a Ford Credit scoring model with a machine learning model developed by ZestFinance, using its underwriting platform to do deeper analysis of applicant data.

Ford Credit and ZestFinance found that machine learning-based underwriting could reduce future credit losses significantly and potentially improve approval rates for qualified consumers, while maintaining its consistent underwriting standards.

According to the Consumer Financial Protection Bureau, 26 million American adults, or about one in 10, have no credit record, making them difficult and often impossible to underwrite using traditional methods. This includes millions of millennials who are also part of the fastest-growing segment of new car buyers.

Although these consumers may have steady jobs, officials insisted their creditworthiness is heavily based on credit history. This makes it more difficult for companies to provide financing, and they could miss an opportunity for revenue growth.

Last year, the captive pointed out that new vehicles purchased by millennials represented 29 percent of all U.S. sales, and that number is expected to grow to 40 percent by 2020.

“Machine learning-based underwriting will be a game-changer for lenders, opening entirely new revenue streams. Millennials offer the perfect example. They are typically a good credit risk and are expected to command $1.4 trillion in spending by 2020, but many lack the financial history needed to pass a traditional credit check,” ZestFinance founder and CEO Douglas Merrill said.

“Applying better math and more data to traditional underwriting illuminates the true credit risk and helps forward-looking companies like Ford Credit continue to grow their businesses while predictably managing their risk,” Merrill added.

The companies also highlighted that machine learning tools can analyze data more deeply and in more detail. They also are capable of “learning” over time, for example, by proposing changes to variables as patterns evolve or emerge, or by recognizing and incorporating macroeconomic changes into their assessments.

ZestFinance is now offering the Zest Automated Machine Learning (ZAML) Platform, which it developed specifically for credit underwriting. ZAML uses complex algorithms to analyze thousands of data points to provide a richer, more accurate understanding of all potential borrowers, delivered in an easy-to-use Web interface.

The ZAML Platform consists of three components: data collection and assimilation, machine learning modeling tools and transparency tools that enable companies to explain credit decisions.

“The work with ZestFinance exemplifies the innovation efforts at Ford Credit to support Ford Motor Company and its customers,” the captive said.

“Financial technology is key to many of these efforts, as fintech can contribute to an even more seamless and better personalized vehicle financing experience for consumers,” the company went on to say.