Abstract
Abstract: There are many significant problems facing by water supplying companies and agencies because of fraudulent water consumption. Which is resulting a higher loss of income to water supplying agencies. Finding efficient measurements for detecting fraudulent activities has beenan active research area in recent years. To detect this fraudulent behaviour faced by water companies’ intelligent datamining techniques can be used to reduce the loss. This research explores the use of two classification techniques SVM and KNN to detect suspicious fraud water customers. The SVM based approach uses customer load profile attributes to expose abnormal behaviour that is known to be correlated with non-technical loss activities. The data has been collected from the historical data of the company billing system. The accuracy of the generated model obtained 74% which is better than the current manual prediction procedures.The system will help the company to predict suspicious water customers.
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