NDLI: Fraud detection in high voltage electricity consumers using data mining

Content Provider	IEEE Xplore Digital Library
Author	Cabral, J.E. Pinto, J.O.P. Martins, E.M. Pinto, A.M.A.
Copyright Year	2008
Description	Author affiliation: Electr. Eng. Dept., Fed. Univ. of Mato Grosso do Sul, Campo Grande (Cabral, J.E.; Pinto, J.O.P.; Martins, E.M.; Pinto, A.M.A.)
Abstract	This work presents a methodology and a computational system for fraud detection for high voltage electrical energy consumers using data mining. This methodology is based on a non-supervised artificial neural network called SOM (Self-Organizing Maps), which allows the identification of the consumption profile historically registered for a consumer, and its comparison with present behavior, and shows possible frauds. From the automatic consumer behavior pre-analysis, electrical energy companies will better direct its inspections, and will reach higher rates of correctness. The fraud detection system validation showed that the methodology is robust on the cases of lower consumption resulted by fraud, and on the cases of atypicality intrinsic to the consumer.
Starting Page	1
Ending Page	5
File Size	916926
Page Count	5
File Format	PDF
ISBN	9781424419036
DOI	10.1109/TDC.2008.4517232
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Voltage Energy consumption Data mining Inspection Databases Self organizing feature maps Consumer behavior Computer crime Artificial neural networks Robustness Self-Organizing Maps Fraud Detection Artificial Intelligence KDD Data Mining
Content Type	Text
Learning Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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