Machine learning techniques are taking hold in US banks’ stress-testing models, bit by bit and byte by byte. Proponents trumpet their ability to calculate revenue and loan-loss forecasts faster than existing methods. But users are running up against a familiar barrier: the difficulty of explaining the complex practices to model validators and regulators.

One large US bank is developing a prototype model for its annual Comprehensive Capital Analysis and Review (CCAR) as well as for the Current