To carry out their various missions (collecting revenue, facilitating trade, and ensuring security), many customs administrations have established a risk management unit. In developing countries, however, because of the lack of dedicated human and material resources, intelligence and risk analysis remain insufficiently developed. In view of the lack of resources, this paper proposes a simple methodology aiming at detecting risky import operations. The mirror analysis first helps to identify and target products or sectors with the greatest risk. Based on the examination of customs declarations patterns (data mining), it is possible to identify and target higher risk economic operators (importers and customs brokers). When implemented in Madagascar, this method has helped to reveal probable fraud cases in the present context of customs reform. Estimates suggest that, in 2014, customs fraud reduced non-oil customs revenues (duties and import value-added tax) by at least 30 percent.