One large US bank is developing a prototype model for its annual Comprehensive Capital Analysis and Review (CCAR) as well as for the Current Expected Credit Loss (CECL) accounting standard, both of which require forecasting losses based on macroeconomic scenarios. The model will use machine learning to link economic variables with actual loss forecasts. The bank hopes this will generate new insights that are manually intensive using traditional modelling techniques. A US global systemically important bank (G-Sib) has been applying neural networks as a challenger model for its primary credit risk models in coming up with its CCAR forecasts. The machine learning model is capable of analysing non-linearities – changes in importance of variables – in data much faster than a traditional model. In retail credit, for example, during the first six months of the 39-month CCAR horizon, variables such as income and delinquency history are the primary determinants of default. Further out along the horizon, the importance of such variables decreases, and macroeconomic variables become the primary determinants. Using traditional methods, it would take data scientists months to instruct the model to capture non-linearities.