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Hypersense Fraud Management System
Need for AI-Powered Anti-fraud Tools in Telecom: A Risk Practitioner’s Perspective
A case study on combating Mobile Money Fraud
1. Advanced machine learning methodologies
Leverage machine learning and develop advanced supervised and unsupervised models with historic data and can help the operator to profile the calls and SMS for any deviations and detect anomalies in real-time with an accuracy of 98.5%. Machine learning allows the operator to make decisions based on information as it happens, empowers them to anticipate and take proactive action.
2. Signaling Intelligence
Operators FMS system should monitor signaling traffic from layer 3 to layer 7 in real-time to secure the network signaling exploitation on Voice, and SMS services. With signaling intelligence operator can detect and prevent scam calls like Wangiri, IRSF and CLI spoofing in real-time.
3. Real-time threat Intelligence
Operators should have access to real-time threat intelligence of hotlists to block the scam calls in real-time.
4. Voice and SMS Firewalls
Operators should install a carrier-grade threat-focused firewall capable of subverting threats. The firewall monitors the outgoing and incoming traffic from/to your network and blocks malicious/spam calls depending on the rules configured within the firewall.
5. Subscriber/Customer Awareness
As a proactive approach, the operators should frequently make the customers aware of the increased scam calls and how not to be the victim of these calls. This will help in improving the customer experience by reducing the monetary losses of customers.