  This paper addresses the ``curse of dimensionality'' in the loss valuation of
credit risk models. A dimension reduction methodology based on the Bayesian
filter and smoother is proposed. This methodology is designed to achieve a fast
and accurate loss valuation algorithm in credit risk modelling, but it can also
be extended to valuation models of other risk types. The proposed methodology
is generic, robust and can easily be implemented. Moreover, the accuracy of the
proposed methodology in the estimation of expected loss and value-at-risk is
illustrated by numerical experiments. The results suggest that, compared to the
currently most used PCA approach, the proposed methodology provides more
accurate estimation of expected loss and value-at-risk of a loss distribution.
