Abstract:
"During the last decade, machine learning technology has accelerated the development
of Predictive analytics, Anomaly detection, and forecasting. There are more than
thousands of telecommunication companies in the world. And they earn a massive
amount of revenue per year (million/ trillion) through providing multiple services to
customers. In order to give effective service to customers, the company has its own
service centers (internal agents), and companies have appointed external sales agents
and give targets to them on monthly basis. Based on the target achievement, their
commission will be paid. When a person or agent makes false activations/sales in order
to obtain a high commission, it is identified as fraud. The total cost of sale fraud is
approx. 4.3Mn per year. Hence, the detection of fraudulent sale agents is a challenging
problem in the telco industry. The traditional approach for fraud detection is known as
a rule-based engine. Detection of external fraud agents is the most prominent compared
with internal agents. In this paper, focuses on identifying the external fraud sale agents
by using supervised machine learning techniques, and GridsearchCV is used to find the
best hyper-parameter. Also, model performance will be evaluated by the confusion
matrix and ROC curve. This approach helps to calculate the best accuracy, precision,
recall, and F1 score."
