Risk aggregation is virtually everywhere in insurance applications. Indeed, in the vast majority of situations insurers are interested in the properties of the sums of the risks they are exposed to, rather than in the stand-alone risks per se. Unfortunately, the problem of formulating the probability distributions of the aforementioned sums is rather involved, and as a rule does not have an explicit solution. As a result, numerous methods to approximate the distributions of the sums have been proposed, with the moment matching approximations (MMAs) being arguably the most popular. The arsenal of the existing MMAs is quite impressive and contains such very simple methods as the normal and shifted-gamma approximations that, respectively, match the first two and three moments, only, as well as such much more intricate methods as the one based on the mixed Erlang distributions. Note however that in practice the sums of insurance risks can have numerous and just a few summands; in the latter case the normal

approximation is very questionable. Also, in practice the distributions of the stand-alone risks can be light-tailed or heavy-tailed; in the latter case moments of higher orders (e.g., second, etc.) may not exist, and so the approximation based on mixed Erlang distributions is of limited usefulness. In this talk I will reveal a refined MMA method for approximating the distributions of the sums of insurance risks. The method approximates the distributions of interest to any desired precision, works equally well for light and heavy-tailed distributions, and is reasonably fast irrespective of the number of the involved summands. (This is a joint work with Justin Miles and Alexey Kuznetsov, York University.)