Rising AI Adoption in the Telecommunications Sector
The artificial intelligence (AI) and machine learning (ML) market is set for substantial growth, driven by increasing demand for intelligent automation, predictive analytics, and innovative solutions in various industries. As industry competition intensifies, data traffic increases exponentially, and the demand for exceptional customer service rises, AI and machine learning technologies are becoming increasingly important in the telecommunications sector. The global AI in telecommunications market was valued at USD $1.82 billion in 2023 and is expected to grow at a CAGR of 28.3%.
AI and machine learning play a crucial role in enabling agility and innovation in the 5G era. These systems utilize learning algorithms and data to process information, to identify patterns, make predictions and adjust to new information in changing circumstances. What sets machine learning apart from traditional analytical algorithms is its adaptability. Machine learning algorithms continuously evolve as they process more data, becoming more accurate and staying relevant without needing human expertise or re-programming.
Telecommunications companies, which manage vast communication networks and infrastructures with high data flow, are leveraging AI and machine learning technologies to gain valuable insights into every aspect of their business. This includes operations, network quality, and customer behavior, creating new revenue opportunities and more. The growing demand for AI in telecommunications, especially for fraud detection, highlights the necessity for advanced solutions. Emerging fraudulent activities, including Wangiri, SIM box fraud, interconnect bypass fraud, subscription fraud, SMS phishing, SIM swapping, International Revenue Share Fraud (IRSF), and account takeovers, persistently contribute to revenue losses for telecommunications operators.
Neural Technologies’ ActivML (AI and machine learning) solution stands at the forefront, offering a powerful suite of self-automated capabilities to proactively and comprehensively safeguard businesses from fraud risks and potential revenue leakage activities. This cutting-edge solution leverages machine learning models and AI analytics to address fraud challenges, featuring real-time anomaly detection and predictive analytics to both identify and explain unusual patterns and anticipate potential fraud attempts. Automation delivers advanced AI/ML driven solutions for business users without requiring in-depth skills on AI or machine learning toolkits.
ActivML in Revenue Assurance and Fraud Management (RAFM)
Neural Technologies has been delivering pioneering machine learning solutions for over 30 years. Our extensive work across the globe has demonstrated the significant impact these technologies have on enhancing revenue assurance and fraud management, particularly within the telecommunications sector.
The challenges for telecom operators arise not only from the sheer volume of data but also from its variability and velocity. The 6Vs of big data—veracity, value, variety, volume, velocity, and variability are crucial aspects of data management.
Revenue Assurance and Fraud Management, is a combined approach to managing revenue-related financial risk in the communication services industry. The goal of RAFM is to minimize financial losses and ensure the accuracy of revenue streams.
Proactive RAFM relies on the power of data analytics, serving as the vital link between revenue assurance and fraud management. Through the analysis of extensive data, companies can pinpoint patterns and anomalies that could signal fraudulent activities, as well as uncover discrepancies or errors in the revenue stream leading to revenue leakage.
Neural Technologies' RAFM solutions leverage AI and machine learning to continually learn and adapt, effectively safeguarding against both known and unknown threats in real-time. By harnessing data analytics and monitoring, our solutions can proactively identify potential risks and anomalies in revenue streams, empowering businesses to take strategic measures to mitigate financial risk.
Proven Use Cases: How ActivML Tackles Fraud
For telecom operators, managing the ever-growing volume, variety, and velocity of data presents a significant challenge. Not only do they struggle to keep pace with sophisticated fraud schemes and revenue leakage, leading to significant financial losses, but implementing complex AI models often requires a specialized skill set that can be scarce and expensive to acquire.
Neural Technologies’ ActivML is a powerful AI and machine learning solution equipped with a comprehensive suite of advanced machine learning models combined with AI-driven explainability analysis, specifically designed to address the unique complexities of telecommunications business operations. Unlike complex, resource-intensive AI models, ActivML is designed for ease of use.
One of the most significant challenges in AI adoption is the lack of readily available skilled manpower. ActivML addresses this head-on with its MLOps capability, making it accessible for business users and non-specialized experts to build, train, and deploy AI/ML models. This empowers telecommunications companies to integrate AI and machine learning easily into their operations.
ActivML (AI and machine learning) solution key features include:
- Business-Enabled Automated Model Building, Training and Deployment
- Self-learning Structured Analytical Profiling
- Unconstrained Anomaly Detection
- Predictive Classification
- Explainable AI Analytics (XAI)
- Continuous Learning from Live Data
Here's how ActivML tackles the real-world fraud challenges:
#1 Combating Dealer Fraud and Abuse Case Study
A Communication Service Provider (CSP) faced commission fraud and abuse challenges within its indirect sales channels dealer network, which accounted for 80% of their sales. Traditional methods struggled to detect the abuse and fraudulent activities rapidly.
Neural Technologies deployed the ActivML solution to address the specific abuse or fraud challenges and even pinpoint the dealers and activated MSISDNs associated with the abuse or fraudulent activity quickly. With the traditional detection method, these fraudulent activities were only detected eight months later.
ActivML’s advanced analytics and anomaly detection capabilities helped the CSP:
- Uncover hidden patterns of the dealers’ abuse or fraudulent activities
- Gain real-time Insights into suspicious dealers’ activities
- Precise anomaly detection to minimize false positives
#2 SIM Box or Bypass Fraud Detection and Prediction Case Study
Traditional methods for detecting SIM Box and Bypass frauds are often ineffective as fraudsters continuously adapt their tactics, making detection challenging.
With the deployment of the ActivML solution, the SIM Box and Bypass frauds were successfully identified even within the camouflage activities employed by the bad actors.
ActivML’s powerful combination of Structural Distinctiveness Analysis and Predictive Analysis provides the capabilities to:
- Identify unusual anomaly patterns of the SIMs’ activities
- Gain Real-Time Insights into fraudster ever-changing camouflage techniques
- Pinpoint atypical SIM’s behavior
- Predict and discover SIM frauds
Neural Technologies’ ActivML provides ready machine learning models for other use cases which include customer segmentation, customer churn, network management and optimization, recommendation engines, real-time streaming analytics, price optimization and more.
