Electricity　theft　is　a　great　trouble　for　power　companies.　As　the　means　of　tampering　with　smart　meters　continue　to　increase,　the　electricity　theft　behaviours　become　more　diversified　and　covert,　which　are　difficult　to　be　identified　using　the　existing　electricity　theft　detection　method.　In　addition,　the　existing　methods　usually　cannot　estimate　the　economic　losses　caused　by　electricity　theft.　To　address　these　issues,　a　combined　unsupervised　learning　approach　for　electricity　theft　detection　and　loss　estimation　is　proposed　in　this　study.　First,　three　anomaly　measurement　indexes　including　the　mean　index,　fluctuation　index,　and　trend　index　are　proposed　to　capture　different　anomalies　respectively.　Then,　based　on　historical　electricity　consumption　data,　we　develop　two　unsupervised　learning　techniques　including　the　sample-to-subsamples　decomposition　algorithm　and　clustering　algorithm　to　obtain　the　typical　ranges　of　index　values,　and　the　load　samples　whose　index　values　are　not　in　the　typical　ranges　will　be　considered　fraudulent.　Furthermore,　three　anomaly　measurement　indexes　are　combined　to　judge　whether　the　load　sample　is　fraudulent,　and　the　user　whose　most　load　samples　are　judged　fraudulent　will　be　considered　as　an　electricity　thief.　Finally,　an　economic　loss　estimation　method　is　proposed,　which　quantifies　the　losses　of　electricity　theft.　Numerical　experiments　are　carried　out　based　on　the　Irish　smart　meter　dataset,　and　the　results　demonstrate　the　effectiveness　and　the　superior　performance　of　the　proposed　method　compared　with　a　series　of　electricity　theft　detection　methods.