This paper uses a large-scale natural experiment to study the equilibrium effects of restricting information provision in credit markets. In 2012, Chilean credit bureaus were forced to stop reporting defaults for 21% of the adult population. Using panel data on the universe of bank transactions in Chile combined with the deleted registry information, we implement machine learning techniques to measure changes in the predictions lenders can make about default rates following deletion. Using a difference-in-differences design, we show that individuals exposed to increases in predicted default reduce borrowing by 6.4% following deletion, while those exposed to decreases raise borrowing by 11.8%. In aggregate, deletion reduces borrowing by 3.5%. Taking the difference-in-difference estimates as inputs into a model of borrowing under adverse selection, we find that deletion reduces surplus under a variety of assumptions about lenders’ pricing strategies.