Automation Workflow for Roaming Fraud Detection and Blocking

Automated real-time analysis of TAP files and signaling data identifies inflated traffic, subscription fraud, and gateway attacks before they are billed. By integrating directly with steering of roaming (SoR) platforms to block fraudulent partners, this workflow prevents 5-15% of typical roaming revenue leakage, directly improving EBITDA. It also automates the initiation of financial reconciliation claims with fraudulent partners.

Eliminates the manual, analyst-intensive process of sifting through daily TAP files and CDRs for anomalies. The AI-driven workflow autonomously triages thousands of records per second, escalating only high-confidence, complex cases for human review. This reduces the fraud management team's investigative workload by 60-80%, allowing them to focus on strategic threat hunting and partner management.

Proactive detection and blocking of fraud (like SIM box bypass) protects legitimate subscribers from unexpected bill shocks and service degradation. Automated subscriber notification workflows for suspected compromise (e.g., potential SIM swap) demonstrate security commitment, directly improving Net Promoter Score (NPS) and reducing churn driven by security or billing complaints.

Reduces Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) from days to seconds. Upon fraud pattern recognition, the workflow automatically pushes blocking rules to firewalls, updates partner blacklists in the SoR, and isolates suspicious traffic—all without waiting for a SOC analyst ticket. This containment speed limits the blast radius of an attack, protecting network resources and partner relationships.

Built on orchestration frameworks like LangGraph, the workflow is designed for the scale and complexity of 5G and IoT roaming. Its modular agent design allows for easy integration of new data sources (e.g., 5G service-based architecture logs) and fraud patterns without a full rebuild. This creates a durable competitive moat, lowering the long-term cost of adapting to evolving fraud tactics.

Automatically generates immutable audit trails for every detection, decision, and blocking action, complete with the AI's reasoning and data evidence. This is critical for regulatory compliance and for defending chargeback disputes with roaming partners. The workflow enforces configurable approval gates for high-stakes actions, ensuring human oversight is preserved where required by policy.

This component continuously ingests Transfer Account Procedure (TAP) files from roaming partners via SFTP or API. An agent parses the complex CDR structures, normalizes fields, and enriches records with subscriber profile data from the HLR/HSS. It handles varying file formats and validates for completeness before streaming records to the detection engine, ensuring no fraudulent traffic slips through due to ingestion delays or parsing errors.

The core analytical layer runs multiple detection models in parallel. A rules engine checks for known fraud patterns (e.g., Wangiri, IRSF). A graph model analyzes relationships between IMSIs, partners, and destinations to uncover subscription fraud rings. An anomaly detection model establishes per-partner behavioral baselines for traffic volume and call duration, flagging deviations. This ensemble approach balances precision and recall, routing high-confidence fraud to automated action and lower-confidence signals to a review queue.

Upon a confirmed fraud verdict, this orchestrator executes the containment action. It integrates directly with the Steering of Roaming (SoR) platform via API to dynamically update routing policies, blocking traffic from the fraudulent IMSI or partner network. It also triggers updates to signaling firewalls (e.g., Diameter Edge Agent) and internal blacklists. The orchestrator includes a sandbox to test blocking rules and a rollback mechanism in case of false positives, ensuring network stability.

Automation extends to the financial and operational fallout. This component automatically creates a case in ServiceNow or a similar ITSM, tagging it with all relevant evidence. It interfaces with the Billing System (BSS) to flag disputed charges, initiate reconciliation processes with the roaming partner, and adjust financial accruals. It generates audit-ready reports for partner settlements and internal compliance, turning detection into direct cost recovery.

Not all decisions can be fully autonomous. This queue presents lower-confidence alerts and complex edge cases to fraud analysts via a dedicated dashboard. Analysts can review enriched evidence, approve/override automated actions, and provide feedback that retrains the detection models. The system enforces role-based approval gates for high-value blocks and maintains a complete audit trail of all human interventions for governance and model improvement.

The operational backbone monitors the entire workflow's health, data quality, and model drift. It tracks key metrics: detection rates, false positives, block effectiveness, and revenue saved. An automated pipeline uses analyst feedback and newly confirmed fraud patterns to periodically retrain the ML models. This closed-loop system ensures the fraud defense adapts to evolving tactics without requiring a full manual rebuild, sustaining ROI over time.