Fraudulent behavior in drinking water consumption is a significant problem facing water supplying companies and agencies. This behavior results in a massive loss of income and forms the highest percentage of non-technical loss. Finding efficient measurements for detecting fraudulent activities has been an active research area in recent years.
Intelligent data mining techniques can help water supplying companies to detect these fraudulent activities to reduce such losses. 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 behavior 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 hit a rate of over 74% which is better than the current manual prediction procedures taken by the companies and agencies. To deploy the model, a decision tool has been built using the generated model. The system will help the company to predict suspicious water customers to be inspected on site.
A data mining-based model for the detection of fraudulent behavior in water consumption is an important application of data analytics and machine learning techniques. Detecting fraudulent behavior in water consumption is crucial for ensuring fair billing and preventing water theft, which can have significant financial and environmental implications.
Building a data mining-based model for detecting fraudulent behavior in water consumption is a complex task that requires a multidisciplinary approach involving data scientists, domain experts, and IT professionals. It also requires a commitment to ongoing monitoring and improvement to stay effective in the face of evolving fraudulent tactics