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Publication Date

10-2021

Abstract

Big Data methodologies are applied to understand subprime borrowers in the U.S. automobile space. The focus on the automobile market is essential as this subsegment is responsible for directly and indirectly employing over one million people and creating payrolls in excess of $100 billion annually in the U.S. It is found in this article that if a subprime borrower is a homeowner, the probability of repaying their auto loan increases by almost 4%. However, if the borrower is renting, the likelihood of repaying their auto loan increases by nearly 1.4%. Applying Big Data in making subprime auto loans can add 1000’s of jobs and improve security of millions of dollars in payroll.

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