Frequently Asked Questions

Roaming margins often erode when operators lack timely visibility into wholesale tariffs, currency fluctuations, partner performance, settlement accuracy, and service quality issues. Regaining financial control requires combining roaming cost visibility, intelligent roaming policies, margin assurance, and automated reconciliation between retail charging and wholesale clearing data. By connecting these operational and financial processes, operators can reduce leakage, improve profitability, and make more informed commercial decisions.

Reducing wholesale roaming costs should not come at the expense of customer experience. Steering of Roaming helps operators direct subscribers to preferred partner networks that offer the right balance between commercial rates and service quality. Using real-time analytics, quality thresholds, and dynamic traffic steering, operators can adapt to changing network conditions while controlling costs and maintaining a consistent roaming experience.

Revenue leakage most commonly occurs where data moves between provisioning, usage capture, rating, billing, and settlement systems. When information becomes misaligned across customer, network, and charging platforms, usage can go unbilled or be charged incorrectly. Automated controls, real-time monitoring, and AI-driven assurance help operators identify discrepancies early, reducing financial losses and improving operational accuracy.

Preventing revenue leakage is often more effective than recovering lost revenue after the fact. Proactive revenue assurance helps operators identify billing issues before they affect customers or financial performance. By combining AI-powered testing with automated synthetic test calls and data sessions, operators can detect rating errors, configuration issues, and charging gaps before new services and offers are widely deployed.

Faster dispute resolution starts with greater visibility into traffic, routing, and settlement data. Near-real-time reconciliation provides operators and their partners with a shared operational view of usage and commercial records, making it easier to identify discrepancies and validate claims. This approach helps reduce operational costs, accelerate dispute resolution, and strengthen partner relationships.

As roaming agreements, wholesale routes, and settlement relationships become more complex, maintaining profitability becomes more challenging. Margin assurance helps operators connect cost, routing, quality, and settlement information into a single operational view. With consolidated analytics, teams can identify unprofitable routes, partners, and traffic patterns earlier, enabling corrective action before margin erosion affects business performance.

The telecom fraud types that create the greatest financial risk today include International Revenue Share Fraud (IRSF), SIM box and bypass fraud, roaming fraud, subscription fraud, Application-to-Person (A2P) SMS abuse, Artificially Inflated Traffic (AIT), Flash Calls, Call ID (CLI) spoofing, and impersonation scams. Because these threats span voice, messaging, signaling, and onboarding, operators need unified and AI-driven fraud management instead of siloed controls. This helps reduce fraud exposure and protect revenue.

International Revenue Share Fraud (IRSF) can generate significant losses within a very short period of time. Operators can reduce exposure by monitoring high-risk call destinations, abnormal traffic patterns, and signaling activity in real time. Combining behavioral analytics, machine learning, and automated intervention enables earlier detection and faster response before fraudulent traffic escalates.

Protecting interconnect revenue requires visibility into how traffic is routed across the network. Operators can identify SIM box, bypass, and grey-route fraud by correlating signaling activity, routing information, and traffic behavior. Synthetic test calling and behavioral analytics help reveal unauthorized routing practices, allowing operators to take action before revenue leakage becomes significant.

Roaming fraud can quickly affect both profitability and customer confidence if it is not detected early. Operators can identify suspicious activity by monitoring authentication events, signaling behavior, and roaming usage patterns in real time. Because Near Real-Time Roaming Data Exchange (NRTRDE) records may arrive too late for effective prevention, visibility at the signaling and protocol layers plays an important role in exposing anomalies such as impossible travel or simultaneous activity across locations.

Enterprise messaging revenue depends on maintaining trusted and secure communication channels. Operators can reduce A2P SMS fraud by identifying grey routes, spoofed sender identities, unauthorized traffic sources, and abnormal messaging behavior before messages reach their destination. Combining SMS firewalls, route intelligence, real-time traffic analysis, and active testing campaigns helps protect both messaging revenue and service integrity.

Flash calls are increasingly used as an alternative authentication mechanism, creating both risks and commercial opportunities. Operators can detect flash calls by identifying the ultra-short call patterns associated with authentication traffic. Real-time detection enables operators to decide whether to block, allow, or monetize this activity based on business objectives, regulatory requirements, and market conditions.

Reducing fraud at the point of customer acquisition helps prevent downstream financial and operational losses. Operators can strengthen onboarding by combining AI-driven risk scoring, identity verification, behavioral analysis, and dealer oversight. When these controls operate in real time, fraudulent accounts can be identified before services are activated or revenue exposure increases.

As digital onboarding expands, protecting eSIM provisioning processes becomes increasingly important. Operators can reduce eSIM fraud by monitoring activation workflows, device identities, provisioning events, and profile lifecycle activity in real time. Strong controls around remote provisioning help identify misuse early while supporting trusted and scalable eSIM adoption.

The transition to 5G Standalone (5G SA) introduces new security challenges alongside traditional telecom risks. Key threats include protocol abuse, API exploitation, subscriber data exposure, and attacks targeting cloud-native network functions. Because 5G SA relies on HTTP/2-based interfaces and service-based architectures, operators need visibility across both telecom and cloud environments to strengthen security across the 5G core. Additionally, bad actors usually expose the grey space between new and legacy protocols, as such, cross-protocol firewall functionality becomes even more important to assess the observed behaviors across all Radio Access Technologies and signaling protocols.

The Security Edge Protection Proxy (SEPP) is a key security component within 5G Standalone roaming environments. It helps secure signaling exchanges between operators by protecting, validating, and governing inter-operator communications. When combined with broader observability, testing, and security controls, SEPP helps operators strengthen roaming security while supporting compliance with 5G security requirements.

Signaling attacks continue to target critical telecom infrastructure and subscriber information. Operators can defend against SS7 and Diameter threats through signaling firewalls, continuous monitoring, threat intelligence, and policy enforcement. These controls help identify and block unauthorized requests, subscriber tracking attempts, and malicious signaling activity before they affect network operations.

Modern telecom threats rarely stay confined to a single protocol or network layer. As attacks increasingly span SS7, Diameter, GTP, and 5G HTTP/2 environments, operators need visibility across multiple signaling domains. Cross-protocol signaling firewalls help correlate activity across legacy and modern networks, providing stronger protection for the core network and a more complete view of emerging threats.

Customer trust depends on the ability to identify and block fraudulent communications. Operators can protect customers from voice spoofing, phishing, and robocall attacks by combining caller authentication, voice analytics, and real-time fraud detection. Industry frameworks such as STIR/SHAKEN, together with detection of Calling Line Identification (CLI) manipulation, help reduce abusive calling activity before it reaches subscribers.

Compliance risks often arise when traffic records, revenue data, invoicing systems, and tax reporting processes become misaligned. Tax assurance and regulatory reporting controls help operators reconcile these records more accurately through automation and continuous validation. This improves audit readiness, strengthens reporting consistency, and reduces the risk of penalties associated with reporting errors or reconciliation gaps.

Preventing customer dissatisfaction starts with identifying service degradation before it affects large groups of subscribers. By combining real-time monitoring, active testing, and customer-experience analytics, operators can detect issues such as authentication delays, packet loss, and service instability earlier. This proactive approach helps improve service quality, reduce support costs, and lower churn risk.

Subscriber churn is often preceded by a decline in service quality. Service quality analytics help operators identify customers whose experience is deteriorating by analyzing metrics such as speed, latency, dropped calls, and service consistency. Turning these indicators into churn-risk insights enables more targeted retention actions, helping improve customer lifetime value and reduce churn.

Understanding the customer experience requires more than monitoring network elements alone. Active end-to-end testing simulates real user journeys across devices, applications, locations, and networks to reveal how services perform under actual conditions. This provides operators with deeper visibility into customer-impacting issues and supports faster troubleshooting and service assurance. Additionally, passive monitoring provides an unbiased view of what’s happening in the network across all the monitored interfaces and paint a complete picture of the user journey and experience.

Improving customer experience often starts with understanding how network performance compares across markets, competitors, and roaming partners. Telecom benchmarking enables operators to measure throughput, latency, call setup success, and other service quality metrics against relevant peers. These insights help prioritize investments, address performance gaps, and improve customer retention.

High-value travelers expect the same level of service quality abroad as they receive at home. Operators can improve the roaming experience by performing roaming analytics combining real-time quality monitoring, partner benchmarking, and premium-segment experience management. This helps identify underperforming roaming corridors, resolve issues more quickly, and maintain service quality for subscribers who generate significant roaming revenue.

Meeting Service Level Agreement (SLA) commitments is essential for retaining enterprise and private network customers. Operators can assure SLA performance through continuous testing, real-time monitoring, and analytics covering latency, availability, and service quality. Ongoing validation helps identify degradation before it results in customer impact or SLA violations, strengthening customer confidence and supporting long-term business relationships.

Periods of unusually high demand can quickly expose network capacity constraints. Operators can maintain service quality during major events and traffic surges by using event-focused testing, location-aware analytics, and proactive capacity management. These capabilities help identify congestion risks early and support faster optimization decisions, improving network stability and customer experience during peak usage periods. Additionally, when the event is planned (such as Olympics or Football World Cup), operators can assess the performance of each of their roaming partners and make traffic allocation decisions accordingly.

A successful digital onboarding experience depends on reliable provisioning processes. Operators can validate eSIM and remote SIM provisioning workflows by testing across devices, operating systems, activation scenarios, and roaming environments before launch. End-to-end validation helps identify onboarding issues early, reducing support costs, and improving customer adoption.

Maintaining voice service quality during network transformation requires continuous validation across legacy and next-generation environments. Operators can assure Voice over LTE (VoLTE), Voice over New Radio (VoNR), and IP Multimedia Subsystem (IMS) services through active testing, real-time diagnostics, and ongoing service validation. These capabilities help preserve call quality, call setup performance, and emergency service continuity throughout the migration process.

As networks become more complex, manual monitoring alone is no longer sufficient to identify and resolve issues efficiently. AI helps operators move toward more autonomous operations by correlating network, roaming, fraud, and service assurance events, enabling earlier identification of potential issues and root causes. By reducing reliance on static alarms and manual investigation, operators can improve operational efficiency and shorten resolution times across network, roaming, security, and revenue domains.

Effective fraud management depends on identifying genuine threats without overwhelming analysts with unnecessary alerts. AI improves fraud detection accuracy by distinguishing suspicious activity from legitimate customer behavior more effectively than traditional rule-based systems. Machine learning models can identify emerging fraud patterns across voice, messaging, roaming, and digital channels while reducing false positives, helping fraud teams focus on higher-priority investigations and respond more efficiently.

Revenue assurance teams often face large volumes of alerts, discrepancies, and potential leakage events. AI helps prioritize these risks by assessing factors such as financial impact, confidence level, affected services, and likely recovery value. This allows teams to focus resources on the issues with the greatest potential financial impact, improving recovery rates and operational efficiency.

Reducing Mean Time to Repair (MTTR) requires faster identification of the underlying causes of network issues. Automated network analysis accelerates troubleshooting by correlating data from multiple sources, identifying patterns, and highlighting likely root causes. This helps engineering teams isolate faults more quickly, improve operational efficiency, and restore services faster.

Successful 5G service launches depend on validating performance and service behavior before customers are affected. Automated testing helps operators verify interoperability, latency, bandwidth performance, slice isolation, and Service Level Agreement (SLA) requirements in a repeatable manner. This reduces deployment risk, improves launch readiness, and supports more reliable delivery of network slicing services.

Beyond improving internal operations, AI can help operators create new revenue-generating services for enterprise and small and medium-sized business (SME/SMB) customers. By offering AI-powered capabilities such as conversational assistants, automated customer engagement, and workflow automation, operators can help businesses improve productivity while creating new recurring revenue opportunities. This allows operators to expand their value beyond connectivity and strengthen long-term customer relationships.