Predictive analytics uses historical data to build forecasts. Knowledge of customer preferences and needs gives a better understanding of the customer.
Predictive analytics is applied by the telecommunication companies to get valuable insights to become faster, better and make data-driven decisions. The efficiency of this technique is very high because it allows to provide an almost real-time response to the suspicious activity. The algorithms define the anomalies and with the help of data visualization techniques present them as alerts to the analysts in real time. By applying unsupervised machine learning algorithms to an immense amount of customer and operator data to spot the characteristics of normal traffic you can prevent fraud. Therefore, fraud detection systems, tools, and techniques found wide usage. Fraud has a direct influence on the relationship established between the company and the user.
The most widespread cases of fraud in the telecom area are illegal access, authorization, theft or fake profiles, cloning, behavioral fraud, etc. Telecommunication industry being the one attracting almost the most significant number of users every day is a vast field for fraudulent activity. In this article, we attempt to present the most relevant and efficient data science use cases in the field of telecommunication. Therefore, old techniques and methods are no longer relevant. The amounts of data passing through various communication channels are getting larger every minute. Key activities of the companies working in the telecommunication sector are strongly related to data transfer, exchange, and import. Within the telecom industry data science applications are widely used to streamline the operations, to maximize profits, to build effective marketing and business strategies, to visualize data, to perform data transfer and for many other cases. Due to these circumstances, they cannot afford not to use data science. Telecommunication companies are not an exception.
Nowadays data is a fuel needed for a successful company. Data scientists find more and more new ways to implement big data solutions in daily life. In the course of time, data science has proved its high value and efficiency.
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