A Data Mining Based Model for Detection of Fraudulent Behaviour in Water Consumption

Abstract

Drinking water fraud is a major issue for water delivery businesses and authorities. This
behaviour results in a significant loss of income and is responsible for the bulk of non-technical losses.
Finding appropriate criteria for detecting fraudulent behaviour has been a prominent focus of research in
recent years. Intelligent data mining methods can be used by water delivery companies to detect fraudulent
activity and reduce losses. This study looks into the usage of two classification techniques (SVM and KNN)
to discover suspicious water fraud clients. The main purpose of this research is to assist the Yarmouk Water
Company (YWC) in Irbid, Jordan, in overcoming their earnings shortfall. The SVM-based technique uses
customer load profile attributes to expose known abnormal behaviour.