Ford To Use Machine Learning To Assess Credit Risk

ƒFord Motor Credit Co. the financing arm of the vehicle manufacturer, announced Friday (Aug. 25) that it will implement machine learning credit approval models to determine if it will lend a consumer money as it goes after a segment of the market that doesn’t have a solid credit history.

In a press release, the company said the move was a result of a study with ZestFinance 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.  “At Ford and Ford Credit, our primary goal is to serve our customers,” said Ford Credit Chairman and CEO Joy Falotico in a press release. “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.”  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.  By adding machine learning to its risk assessment Ford will be able to tap the 26 million American adults or one in around 10 who have no credit record, making them hard to underwrite under traditional standards.  Ford said that includes millions of millennials who are also part of the fastest-growing segment of new car buyers. Although these consumers may have steady jobs, their creditworthiness is heavily based on credit history.

“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,” said ZestFinance founder and CEO Douglas Merrill in the same press release. “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.”



The pressure on banks to modernize their payments capabilities to support initiatives such as ISO 20022 and instant/real time payments has been exacerbated by the emergence of COVID-19 and the compelling need to quickly scale operations due to the rapid growth of contactless payments, and subsequent increase in digitization. Given this new normal, the need for agility and optimization across the payments processing value chain is imperative.