  Power grids are critical infrastructure assets that face non-technical losses
(NTL) such as electricity theft or faulty meters. NTL may range up to 40% of
the total electricity distributed in emerging countries. Industrial NTL
detection systems are still largely based on expert knowledge when deciding
whether to carry out costly on-site inspections of customers. Electricity
providers are reluctant to move to large-scale deployments of automated systems
that learn NTL profiles from data due to the latter's propensity to suggest a
large number of unnecessary inspections. In this paper, we propose a novel
system that combines automated statistical decision making with expert
knowledge. First, we propose a machine learning framework that classifies
customers into NTL or non-NTL using a variety of features derived from the
customers' consumption data. The methodology used is specifically tailored to
the level of noise in the data. Second, in order to allow human experts to feed
their knowledge in the decision loop, we propose a method for visualizing
prediction results at various granularity levels in a spatial hologram. Our
approach allows domain experts to put the classification results into the
context of the data and to incorporate their knowledge for making the final
decisions of which customers to inspect. This work has resulted in appreciable
results on a real-world data set of 3.6M customers. Our system is being
deployed in a commercial NTL detection software.
