Integrating Artificial Intelligence into Telecom Data Infrastructure for Anomaly Detection and Revenue Recovery

• Author(s): Oyejide Timothy Odofin ; Abraham Ayodeji Abayomi ; Abel Chukwuemeke Uzoka ; Bolaji Iyanu Adekunle ; Oluwademilade Aderemi Agboola; Samuel Owoade
• Paper ID: 1708763
• Page: 222-234
• Published Date: 31-08-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 2 August-2021

Abstract

This paper explores the integration of Artificial Intelligence (AI) into telecom data infrastructures for anomaly detection and revenue recovery. As telecom networks become more complex and generate increasing volumes of data, traditional methods for detecting anomalies—such as fraud, billing errors, and service disruptions—are often inadequate. AI techniques, including machine learning, deep learning, and natural language processing, offer a more dynamic and accurate approach to identifying these anomalies in real-time. By automating anomaly detection processes, AI enables telecom operators to detect complex patterns, reduce false positives, and prevent revenue leakage. Additionally, AI-driven systems enhance financial recovery by addressing issues such as fraudulent activities, billing discrepancies, and network performance degradation. The implementation of AI not only optimizes operational efficiency but also leads to increased profitability through improved resource allocation and pricing strategies. This paper examines key AI models used in anomaly detection, the implications of AI adoption for telecom operators, and case studies demonstrating the successful application of AI for anomaly detection and revenue recovery. Future research directions focus on the refinement of AI models, integration with emerging technologies like 5G and IoT, and ethical considerations surrounding AI implementation in telecom environments.

Keywords

Artificial Intelligence, Anomaly Detection, Telecom Data Infrastructure, Revenue Recovery, Machine Learning, Fraud Detection

Citations

IRE Journals:
Oyejide Timothy Odofin , Abraham Ayodeji Abayomi , Abel Chukwuemeke Uzoka , Bolaji Iyanu Adekunle , Oluwademilade Aderemi Agboola; Samuel Owoade "Integrating Artificial Intelligence into Telecom Data Infrastructure for Anomaly Detection and Revenue Recovery" Iconic Research And Engineering Journals Volume 5 Issue 2 2021 Page 222-234

IEEE:
Oyejide Timothy Odofin , Abraham Ayodeji Abayomi , Abel Chukwuemeke Uzoka , Bolaji Iyanu Adekunle , Oluwademilade Aderemi Agboola; Samuel Owoade "Integrating Artificial Intelligence into Telecom Data Infrastructure for Anomaly Detection and Revenue Recovery" Iconic Research And Engineering Journals, 5(2)