Scam Call Detection

This application area focuses on identifying, blocking, and preventing fraudulent and spoofed voice calls across telecommunications networks. It ingests call metadata, signaling information, historical fraud patterns, and sometimes voice characteristics to determine in real time whether a call is likely to be a scam. The system then enforces actions such as blocking calls, flagging them to end users, throttling suspicious traffic sources, or alerting fraud operations teams. This matters because mass scam campaigns erode consumer trust in phone channels, drive significant financial fraud losses, and expose telecom operators to regulatory and reputational risk. By using advanced analytics and AI models to detect coordinated fraud patterns across multiple operators and large volumes of traffic, telecoms can intervene earlier and more accurately than with rule-based systems alone, improving customer protection while minimizing false positives and operational overhead.

The Problem

Real-time scam call scoring and enforcement across telecom signaling traffic

Organizations face these key challenges:

1

High complaint volume despite static blocklists and STIR/SHAKEN-style attestation

2

Fraudsters rotate numbers/providers quickly, causing blocklists to go stale

3

False positives block legitimate businesses, driving churn and regulatory risk

4

Fraud ops teams drown in alerts with limited explainability or case linkage

Impact When Solved

Real-time scam call scoringReduced false positives by 30%Enhanced customer satisfaction

The Shift

Before AI~85% Manual

Human Does

  • Manual investigations of alerts
  • Updating static blocklists
  • Handling customer complaints

Automation

  • Basic rule-based filtering
  • Threshold checks
  • Periodic blocklist updates
With AI~75% Automated

Human Does

  • Investigating edge cases
  • Final approval for flagged calls
  • Providing strategic oversight

AI Handles

  • Real-time pattern recognition
  • Calibrated risk scoring
  • Continuous learning from analyst feedback
  • Automated enforcement actions

Solution Spectrum

Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.

1

Quick Win

Carrier Blocklist + Heuristic Scam Flagging Console

Typical Timeline:Days

Stand up a lightweight detection layer using configurable rules on CDR/signaling aggregates (e.g., high call attempts per minute, low ASR, short ACD, high unique callee count) combined with curated blocklists and known-bad trunk/source checks. Outputs are a scam-likelihood label and a recommended action (flag, throttle, block) for a small subset of traffic. This level is ideal for rapid validation and immediate reduction of the noisiest fraud patterns.

Architecture

Rendering architecture...

Key Challenges

  • Rules are brittle against adversarial adaptation and number rotation
  • High false positives if thresholds aren’t tuned per segment/route/time-of-day
  • Limited linkage of related events into campaigns
  • Hard to prioritize actions without calibrated risk scores

Vendors at This Level

TwilioSinchVonage

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Market Intelligence

Technologies

Technologies commonly used in Scam Call Detection implementations:

Real-World Use Cases