In this paper, a mixed integer linear programming (MILP) formulation is proposed that integrates financial risk measures into the design and planning of closed-loop supply chains, considering demand uncertainty of final products. The goal is to maximize the supply chain expected net present value (ENPV), while simultaneously minimizing the associated risk. The augmented ɛ-constraint method is used to generate an approximation to the Pareto-optimal curve for each risk measure. Four different risk measures, most popular measures within the literature, are implemented, compared and directions for their usage by decision makers are discussed. Managerial insights are outlined based in decision makers’ risk profile and goal of the risk minimization. A European supply chain case study is explored.