Wharton Research Data Services (WRDS) has awarded its annual Best Paper Award to the four authors of “Predictably Unequal? The Effects of Machine Learning on Credit Markets.”
The authors–Andreas Fuster of the Swiss National Bank, Paul Goldsmith-Pinkham of the Yale School of Management, Tarun Ramadorai of Imperial College Business School and CEPR, and Ansgar Walther of Imperial College Business School–explored how machine learning and artificial intelligence (AI) could either enhance or disrupt mortgage credit access opportunities among specific racial and ethnic groups. The paper examined data on more than nine million home mortgages between 2009 and 2013, and the authors concluded that African-American and Hispanic borrowers would be most likely to be disproportionately impacted by the use of machine learning in credit decisions.
“WRDS is excited to honor Professors Fuster, Goldsmith-Pinkham, Ramadorai and Walther with this Best Paper Award,” said Robert Zarazowski, Managing Director of WRDS, which is a part of the Wharton School of the University of Pennsylvania. “As machine learning advances, it is critically important to understand its real-world impact and this paper does a tremendous job of highlighting potential unequal effects across borrowers. I congratulate the researchers for their important work.”
Trevose, Pa.-based LoanLogics recently published “’Big’ AI Driven by Today's Machine Learning,” a White Paper focusing on how AI and machine learning can help mortgage lenders in reducing redundant and repetitive tasks, while ensuring data quality and upgrading the borrower experience.