Telecommunication Subscription Fraud Detection Using Neural Network

Telecommunications fraud costs carriers billions of revenue dollars annually. The telecom fraud criminal is often ingenious and is typically motivated by money, anonymity,
or both. Through use of the Internet, the fraud community is becoming more collaborative, and as a result, more ingenious. Because of this, fraud detection applications must become more sophisticated to keep pace with the criminals. A system to prevent subscription fraud in fixed telecommunications with high impact on long-distance carriers is proposed. The system consists of a neural network technique, using Multilayer Perceptron (MLP). A total of 158 data samples from Telecom Malaysia Bhd were
collected trained and tested using model. The prediction module allows identifying potential fraudulent customers at the time of subscription. The analysis of the data shows
a reasonably strong correlation between the input variable, which consist of severity, user certainty factor, indicator case and target. The result shows that 78% of prediction
accuracy has been obtained. From the result that has been produce, neural network has a potential to be used for detecting fraud in telecommunication.