NEW DELHI: We live in a world where artificial intelligence And machine learning dominate and meet the needs of every business and industry. The telecommunications sector is no different from this need. Technological advancements in the field of artificial intelligence help us solve our problems by combining computing and large databases with human problem-solving and decision-making capabilities.
Likewise, advances in machine learning make it possible to use computer systems based on algorithms and draw models from data, making the work of human beings relatively easier.
Lately, the telecommunications industry used AI and ML as well, to advance developments in the world of telecommunications. These uses are mentioned as follows, according to Intraway and Techsee:
Customer service and operational support: Navigating customer service has always been a challenge for operators and telecom companies. The influx of customer complaints and demands can be easily addressed using machine learning-based chatbots that can quickly assist customers with the help of a ticketing system. Ticketing data arriving via servers along with real-time inputs provided by customers can help telcos resolve issues faster. Additionally, chatbots can also provide site maintenance by reducing the need for technical visits and reducing business costs.
Network automation and optimization: As communications networks become increasingly difficult to manage and with the implementation of 5G, network automation and optimization is bound to be more difficult. However, through the application of ML technologies, operators can leverage advanced automation in their networks to optimize network architecture. ML and AI can help identify network bottlenecks and apply fixes that improve reliability.
Predictive maintenance: Predictive maintenance helps telecommunications companies improve service, quality and reliability. With AI and AI technologies, businesses can predict outcomes using historical data. AI can also be used to predict future failures based on past models. Additionally, these technologies can be used on versatile sources such as hardware, cloud, open source, and neural networks.
Churn reduction and voice services: Voice services are an essential part of the telecommunications industry and are typically established using machine learning algorithms, allowing voice services to automate and scale individual conversations efficiently. Additionally, churn reduction can be improved through ML, which is a frequent experience for operators and many of them, therefore, are investing in pattern matching solutions. The churn rate ranges from 10% to 67% for telecoms and this rate is alarming. ML helps telecoms integrate algorithms that help predict churn by segmenting customers who show signs of service termination and being vigilant about them so that proactive steps can be taken to retain customers.
Robotic process automation: Telecommunications companies have millions of customers engaged in multiple transactions daily, each of which is subject to human error. Robotic Process Automation (APR) automates the business using AI technology and brings greater efficiency to telecoms by enabling them to streamline their back-office processes by eliminating repetitive tasks, thereby enabling their employees to perform more high value-added tasks. According to Statista, the RPA market could potentially grow by $13 billion by 2030.
Fraud prevention: With the increase in fraud, including spam calls and messages as well as cyber fraud, consumer cybersecurity is gradually becoming one of the main concerns of all telecommunications companies. AI and ML algorithms can detect anomalies in real-time and effectively reduce telecom fraud. The system then automatically blocks access to hackers or fraudsters and blocks sensitive data and information. Companies like Real callerhave recently introduced AI-based SMS protection features for their users and telcos are also using AI and ML to prevent such frauds.
Increase in income: There is a wide range of data readily available in the telecom industry for telcos and operators, based on devices, networks, mobile applications, geolocation data, detailed customer profiles, service usage and billing data. This immense variety of data allows telecom operators to dig deeper into consumer problems and offer specific, detailed solutions to customers. Using AI-powered customer analytics, telcos can increase their ARPU through intelligent upselling and cross-selling of their services.
Here are some of the uses of AI and ML technologies in the telecom industry. Leading telecom companies in India are currently working with AI and ML in various ways, from detecting anomalies in the network, predicting future problems with equipment, to creating robots based on AI for Small Business (Jio Saarthi), build the next generation IoT (Azure Digital Twins) and AI-based solutions to combat fraud messages. This allows the telecom sector to grow by leaps and bounds.
