The detection of non-technical losses (NTL) is a very important economic issue for power utilities. Diverse machine learning strategies have been proposed to support electric power companies tackling this problem. Methods performance is often measured using standard cost-insensitive metrics, such as the accuracy, true positive ratio, AUC, or F1. In contrast, we propose to design a NTL detection solution that maximizes the effective economic return. To that end, both the income recovered and the inspection cost are considered. Furthermore, the proposed framework can be used to design the infrastructure of the division in charge of performing customers inspections. Then, assisting not only short term decisions, e.g., which customer should be inspected first, but also the elaboration of long term strategies, e.g., planning of NTL company budget. The problem is formulated in a Bayesian risk framework. Experimental validation is presented using a large dataset of real users from the Uruguayan utility. The results obtained show that the proposed method can boost companies profit and provide a highly efficient and realistic countermeasure to NTL. Moreover, the proposed pipeline is general and can be easily adapted to other practical problems.