Fraudulent call detection for mobile networks

Telecommunication industry has witnessed an enormous growth in terms of number of subscribers and revenue over the past few years. Still there are certain trends in the revenue of the telecommunication that show an instant fall, reason being change in customer behavior. Telecom operators are subjected to fraud in various forms, among the leading are subscription and superimposition fraud. In the U.S the sum of losses caused by fraudulent activity for the telecom industry is over 650 million dollars a year. The aim in this work is to cater the subscription fraud and bring the figures well within the desired range. In this work we use machine learning techniques to address the issue. Our solution uses a neural network to detect fraudulent behavior for subscription fraud. The neural network takes as input time series data of individual customers to predict their normal behavior. The crucial aspects of the network's predictions being accurate are the fraud profiles; some test cases are created which are used to make the neural network learn a fraudulent behavior.