The use of detailed statistical data in customs reforms: The case of Madagascar

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 article 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 then 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 context of current customs reforms.