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Robocalls Calls, TDoS and Communications Fraud Protection

How it works

Problem Statement: Huge surge in robocalls and spam calls causing heavy productivity and compliance losses. Telco failing to come up with a scalable and enterprise-grade solution. STIR/SHAKEN framework in its early stages with several known limitations. Statistical or DID-based solutions are no more helpful.

Solution: Unique combination of statistical, crowdsourced, and deep learning models with call control remediation

  • Driven by Context, CoR, PoLP, Call MetaData (15+ features), and Business requirement
  • Identifies and remediate vulnerabilities within call control and communication path
  • Performs features extraction on massive datasets using deep learning models
  • Global POP locations* collect, integrate and train data models to optimize their efficiency with 80-90% confidence
  • Built-in intelligence and auto-correlation proactively detects robocalls with great precision
  • Block, terminate, or re-route the robocalls (to VM or null interface) based on enterprise policy
  • Keep track of calls blocked, re-routed to VM, the reason for a block with time stamps
  • Keep evolving statistical and deep learning models to protect from Robocalls.                       The outcome for Customer:
    • No more DID/Telephone number or CDR based approach
    • Reduce 80-90% of Robocalls and TDoS with continuously evolving data models
    • Reduce MTTD, MTTR by up to 80%
    • Save $M in production and financial losses as well as compliance failures and penalties

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