Credit Scoring in the United States

Credit scoring is the paradigmatic example of algorithmic governance (Fourcade and Healy 2017; Pasquale 2015). Corporations take information about thousands of individuals, data mine it for patterns that predict people not repaying their loans, and then make decisions about future lending—who gets money, how much interest they pay—based on variables that predicted default in the past. This is not the only way to make lending decisions, but in the U.S. it has become the dominant one (Mays 2001). This article explores how that came to be the case and the ramifications it has had in order to provide a window onto the credit-centric U.S. economy and an illustration of how predictive algorithms take hold.