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Upcoming Events
AI & ML in Production Workshop
Learn how to deploy, monitor, and maintain machine learning models in production environments with a focus on MLOps best practices.
Training: Master Your Telecom and Mobile Money Data & Analytics
A 5 to 10 days, hands-on training in data engineering, lakehouse architectures, and advanced analytics, to help your teams turn telecom and mobile money raw data into actionable insights for supervision, fraud detection, and revenue assurance and mobile money monitoring and more.
Past Events
Addressing Flash Call: Data Monitoring Solutions for Preventing a Growing Risk
Flash calls are a rising trend for user verification but can become a loophole for bypassing A2P billing. Explore detection methods and best practices for telecom operators and regulators.
View RecordingProtecting A2P SMS Revenues: How to Detect and Prevent Bypass Fraud with Real-Time Data
A2P bypass continues to rise, undermining operator revenues. Discover how proper tracking and analytics give operators visibility into unauthorized traffic.
View RecordingWangiri Scams: How Data Monitoring Can Stop Real Losses from One-Ring Fraud
Wangiri scams exploit missed calls to lure users into calling premium-rate numbers. Learn how data-driven detection and real-time alerts help operators and regulators identify wangiri patterns and stop fraud before it spreads.
View RecordingCLI Spoofing Exposed: Protecting Call Identity and Revenue with Data-Powered Strategies
CLI spoofing undermines trust and enables fraud across telecom networks. Discover how real-time data monitoring helps operators and regulators detect spoofed calls.
View RecordingSIMBOX Fraud Unmasked: How Data Monitoring Can Stop Illegal Call Termination
SIMBOX fraud remains one of the most damaging threats to telecom networks, learn how real-time analytics and behavioral monitoring help operators and regulators detect and block SIM boxes before revenue is lost.
View RecordingPreventing Revenue Leakage: Core vs Intelligent Network Reconciliation
A 1 hour session to discover best practices for reconciling data between core network elements (voice, SMS and data) and the Intelligent Network (IN).
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