The application of credit risk models in Comprehensive Capital Analysis and Review and European Banking Authority mandated regulatory macroeconomic stress testing is of significant concern for banks. The credit models that are used to project stressed losses and impairments under multi-horizon macroeconomic scenarios are also instrumental in projecting interest income and accrual as well as the balances that go into projected risk-weighted assets. In this paper we demonstrate how different credit risk models can be efficiently implemented for scenario analysis and stress testing execution with concrete application examples. Many of the credit risk models banks use in practice can be efficiently implemented through a very simple conditional Markov iteration. Examples include multifactor models derived from the Merton structural approach a dynamic transition matrix models that depend on economic factors and are traditionally estimated on cohorts of loans.We also analyze the efficient implementation of more complex dynamic transition matrix models with the added feature of delinquency (rating) history tracking. Such models are frequently used for both retail and corporate portfolios and can introduce significant past state dependence. Traditionally, such models are therefore deployed in scenario analysis and stress testing using simulation of state transitions. However, in some important cases, such as quarterly models and monthly models with delinquency state indicator functions, the models can be solved more efficiently with an expanded conditional Markov iteration.