After a brief introduction to the Model Risk Assessment literature, this talk will present two recent results in this field. The first part of this talk focuses on risk aggregation problems under partial dependence uncertainty. The main point of our analysis is to show that the knowledge of a dependence measure such as Pearson correlation, Spearman's rho or the average correlation, has typically no effect on the worst-case scenario of the aggregated (Range)Value-at-Risk, with respect to the case of full dependence uncertainty. The second part of the talk deals with the robust assessment of a life insurance contract when there is ambiguity regarding the residual lifetime distribution function of the policyholder. Specifically, we show that if the ambiguity set is described using an L^2 distance constraint from a benchmark distribution function, then the net premium bounds can be reformulated as a convex linear program that enjoys many desirable properties.