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|Title||A DATA MINING BASED FRAUD DETECTION MODEL FOR WATER CONSUMPTION BILLING SYSTEM IN MOG|
Financial losses due to financial frauds are mounting, recognizing the problem of losses and the area of suspicious behavior is the challenge of fraud detection. Applying data mining techniques on financial statements can help in pointing out the fraudulent usage. It is important to understand the underlying business objectives to apply data mining objectives. Water consumer dishonesty is a problem faced by all water and power utilities that managed by a financial billing system worldwide. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This thesis presents a new model towards Non-Technical Loss (NTL) detection in water consumption utility using data mining techniques. This work applies a suitable data mining technique in this field based on the financial billing system for water consumption in Gaza city. Selected technique used in developing a fraud detection model. The efficiency and accuracy of the model were tested and evaluated by one scientific method and reached one accepted technique. The intelligent model developed in this research study predicts and select suspicious customers to be inspected on-site by the department of water theft combat (DWTC) teams at the municipality of Gaza (MOG) for detection of fraud activities. The model increases the detection hit rate of 1-10 % random manual detection to 80% intelligent detection. This approach provides a method of data mining, which involves feature selection and extraction from historical customer's water consumption data. The Support Vector Classification technique (SVC) applied in this research study uses customer's load profile information in order to expose abnormal customer’s load profile behavior.
|Publisher||الجامعة الإسلامية - غزة|
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