Abstract:
Nowadays, telecom services are becoming essential communication and business facilitators.
However, the development of telecom services motivates fraudsters for illegal use. Hence, Telecom
fraud becomes a serious challenge in the telecommunication sector which leads telecom
companies to lose yearly profits and to deliver unfortunate quality of services for their Subscribers.
Subscription fraud is a common and significant type of telecom fraud in today's business. The
primary goal of the fraudsters is to make money illegally or to obtain telecom services with the
intent of not paying for the service they used. Ethiotelecom is one of the oldest service providers
in Africa and the sole service provider of Ethiopia which offers telecommunication services and
products for the enhancement and development of the nation. In this paper, we use a predictive
model using deep learning techniques to detect subscription fraud. To build a predictive deep
learning model, we used convolutional auto-encoder and deep neural network (DNN) techniques.
The fraud detection experimental result showed that the performance evaluation of the
experimental result is 99.14% in terms of accuracy measurement using DNN technique on
1048576 voice datasets.
