  We consider a generic class of chance-constrained optimization problems with
heavy-tailed (i.e., power-law type) risk factors. In this setting, we use the
scenario approach to obtain a constant approximation to the optimal solution
with a computational complexity that is uniform in the risk tolerance
parameter. We additionally illustrate the efficiency of our algorithm in the
context of solvency in insurance networks.
