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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks Volume 18
Editor
Cihan H. Dagli
Cihan H. Dagli
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ISBN-10:
0791802823
ISBN:
9780791802823
No. of Pages:
700
Publisher:
ASME Press
Publication date:
2008

Fraud is a problem that affects many business areas. Energy companies, for instance, have to deal with frauds perpetrated by their consumers. An electricity company located in Brazil showed that its income has been heavily affected in millions of dollars per month because of frauds. Trying to minimize such a problem for electricity companies, this paper proposes an approach based on the KDD process and data mining in order to identify consumers who are most likely to defraud an electricity delivery system. We define a measure that is the accumulated monthly differences of power consumption figures between current and previous years. This measure is calculated for each electricity consumer. Consumers who have an accumulated score above a specified threshold are considered for inspection. After prototyping the approach and adjusting a threshold value, an evaluation showed that our approach provides correct answers for 80% of commercial consumers grouped by business type.

Abstract
1. Introduction
2. Electricity Fraud
3. Knowledge Discovery in Database
4. KDD in Commercial Consumers
5. Preliminary Results
6. Conclusions
7. Acknowledgements
8. References
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