We present a new dataset on the dynamics of non-performing loans (NPLs) during 92 banking crises since 1990. The data show similarities across crises in NPL buildup but much heterogeneity in the pace of NPL resolution. We document how high and unresolved NPLs deepen post-crisis recessions and use a machine learning approach to establish pre-crisis predictors of NPL problems. These predictors—a set of weak macroeconomic, institutional, corporate, and banking sector conditions—help shed light on post-COVID-19 NPL vulnerabilities.

Lenders can exploit households’ payment data to infer their creditworthiness. When households value privacy, they then face a tradeoff between protecting such privacy and attaining better credit conditions. We study how introducing an informationally more intrusive digital payment vehicle affects households’ cash use, credit access, and welfare. A tech monopolist controls the intrusiveness of the new payment method and manipulates information asymmetries among households and oligopolistic banks to extract data contracts that are more lucrative than lending on its own. The laissez-faire equilibrium entails a digital payment vehicle that is more intrusive than socially optimal, providing a rationale for regulation.