• Ranjit Patnaik Sekharamantri
• Narala Narasimha

As a result of financial frauds, financial losses are on the rise. The challenge of fraud detection is identifying losses and suspicious behavior. It is important to understand the underlying business objectives in order to apply data mining objectives. It is critical to apply data mining objectives to financial statements in order to point out fraudulent usage. All water and power companies worldwide that manage their billing systems using a financial system are faced with consumer dishonesty. Research has focused on identifying fraudulent electricity consumption for several years. This thesis uses data mining techniques to develop a new model for detecting non-technical losses (NTL) in water consumption utilities. As part of developing a fraud detection model for water consumption, this study applied a suitable data mining technique based on a financial billing system for water consumption in the city. An accepted method for testing and evaluating the model's efficiency and accuracy has been developed. As part of this research, an intelligent model has been developed that predicts and selects suspicious customers for inspection by the department of water theft combat (DWTC) teams at the municipality of India (MOI) in order to detect fraud. It is estimated that by using this model, 80% of the random manual detection hit rate will be replaced by an intelligent detection hit rate of 1-10%. In this approach, features are selected and extracted from historical water consumption data of customers, which is a form of data mining. In this study, SVC is used to identify abnormal customer load profile behaviors based on customer load profile information.

"Model For Identifying Fraudulent Water Consumption Activity Based On Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.g32-g35, June-2022, Available :http://www.jetir.org/papers/JETIR2206606.pdf

"Model For Identifying Fraudulent Water Consumption Activity Based On Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppg32-g35, June-2022, Available at : http://www.jetir.org/papers/JETIR2206606.pdf