Retail credit models are implemented using discrete survival analysis, enabling macroeconomic conditions to be included as time-varying covariates. In consequence, these models can be used to estimate changes in probability of default given downturn economic scenarios. Compared with traditional models, we offer improved methodologies for scenario generation and for the use of them to predict default rates. Monte Carlo simulation is used to generate a distribution of estimated default rates from which Value at Risk and Expected Shortfall are computed as a means of stress testing. Several macroeconomic variables are considered and in particular factor analysis is employed to model the structure between these variables. Two large UK data sets are used to test this approach, resulting in plausible dynamic models and stress test outcomes.