Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence (AI) to solve this problem. Promising approaches have been reported falling into two categories: expert systems incorporating hand-crafted expert knowledge or machine learning, also called pattern recognition or data mining, which learns fraudulent consumption patterns from examples without being explicitly programmed. This paper first provides an overview about how NTLs are defined and their impact on economies. Next, it covers the fundamental pillars of AI relevant to this domain. It then surveys these research efforts in a comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be solved. We believe that those challenges have not sufficiently been addressed in past contributions and that covering those is necessary in order to advance NTL detection.
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