Dynamic intensity-based point process models, in which a firm default is governed by a stochastic intensity process, are widely used to model portfolio credit risk. In the context of these models, this paper develops, analyzes and evaluates an importance sampling scheme for estimating the probability of large portfolio losses, portfolio risk measures including value at risk and expected shortfall, and the sensitivities of these quantities with respect to the portfolio constituent names. The scheme is shown to be asymptotically optimal. Numerical experiments demonstrate the advantages of the algorithm for several standard model specifications.