Importance sampling is a commonly used technique to improve Monte Carlo methods, especially in working with rare events. It is designed to increase the probability of sampling from rare events and is therefore well-suited for estimating default related items in various products given the rarity of default events. It is also simple to implement and versatile in that in can be easily extended to estimate different items. But the main challenge is selecting an importance sampling scheme that not only increases the probability of rare events but also effectively reduces the variance of the estimate. Under the multivariate framework when multiple entities are involved, variance reduction becomes even more challenging as there is no closed form solution for such optimization problem. In this study, we propose an effective importance sampling algorithm that both increases the probability of rare events and reduce variance of estimates. We consider the problem of variance reduction under the framework of Large Deviation Theory, and establish an efficient importance sampling estimator that can be applied to evaluating default events. Then we extend this importance sampling scheme to another popular type of default event and incorporate it into a conditional importance sampling scheme. Our numerical results confirm that the proposed algorithms for direct importance sampling and conditional importance sampling are more efficient in terms of variance reduction. Our algorithms are overall more robust under different specified initial conditions.