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:
High complaint volume despite static blocklists and STIR/SHAKEN-style attestation
Fraudsters rotate numbers/providers quickly, causing blocklists to go stale
False positives block legitimate businesses, driving churn and regulatory risk
Fraud ops teams drown in alerts with limited explainability or case linkage
Impact When Solved
The Shift
Human Does
- •Manual investigations of alerts
- •Updating static blocklists
- •Handling customer complaints
Automation
- •Basic rule-based filtering
- •Threshold checks
- •Periodic blocklist updates
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.
Carrier Blocklist + Heuristic Scam Flagging Console
Days
Feature-Engineered Scam Risk Scoring Service
Multimodal Scam Campaign Detection Engine
Autonomous Scam Mitigation Orchestrator
Quick Win
Carrier Blocklist + Heuristic Scam Flagging Console
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
Technology Stack
Data Ingestion
All Components
6 totalKey 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
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Market Intelligence
Technologies
Technologies commonly used in Scam Call Detection implementations:
Real-World Use Cases
AI-Driven Scam Call Detection and Blocking Pact in UK Telecommunications
This is like all the big UK phone companies agreeing to share a smart spam filter that spots scam calls in real time and blocks them before they ever ring your phone, using AI to detect dodgy patterns and fake identities.
AI-Driven Scam Call Detection and Prevention for UK Mobile Networks
Imagine every phone network in the country sharing a smart security guard who listens for patterns of scam calls and blocks them before they ever reach your phone. This AI guard learns what new scams look like and helps all networks protect their customers together.