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

Operating Intelligence

How Scam Call Detection runs once it is live

AI runs the operating engine in real time.

Humans govern policy and overrides.

Measured outcomes feed the optimization loop.

Confidence88%
ArchetypeOptimize & Orchestrate
Shape6-step circular
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapecircular

Step 1

Sense

Step 2

Optimize

Step 3

Coordinate

Step 4

Govern

Step 5

Execute

Step 6

Measure

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6 Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in Scam Call Detection implementations:

Real-World Use Cases

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