How WorldNOC Generates Risk Signals

Our methodology combines real-time event monitoring with sector-specific risk models to deliver actionable intelligence.

1

Continuous Global Event Ingestion

We continuously ingest structured global event data from GDELT (Global Database of Events, Language, and Tone) and other verified sources. GDELT monitors news media worldwide, translating and analyzing events in real-time.

  • • 15-minute update cycles
  • • Coverage of 100+ languages
  • • Geographic precision to city level
2

Structured Normalization

Raw events are normalized by geography, category, and severity. We filter noise and aggregate related events to identify emerging patterns.

  • • Geographic clustering (country, region, city)
  • • Event categorization (conflict, disaster, economic, health)
  • • Goldstein scale intensity mapping
3

Sector-Specific Classification

Each event is analyzed through sector-specific risk models using AI classification. We map events to their likely impact on insurance lines, facility operations, supply chains, and employee safety.

  • • Insurance: Property, BI, Marine, Cyber, Auto
  • • Operations: Facilities, Logistics, Personnel
  • • Time horizon estimation (24h to 7d)
4

Probability and Severity Calibration

We assign probability scores (0.0 to 1.0) and severity bands (Low, Medium, High, Critical) based on historical event outcomes and current indicators.

  • • Calibrated against historical loss data
  • • Confidence intervals provided
  • • Continuous model refinement
5

Decision Recommendations

Every risk signal includes explicit, actionable recommendations tailored to your sector. We focus on what you can do now, not just what might happen.

  • • Underwriting freeze triggers
  • • Facility closure recommendations
  • • Supply chain reroute guidance

Our Principles

Probabilities, Not Politics

We focus on operational outcomes and avoid political commentary or speculation.

Decisions, Not Headlines

Every signal includes actionable recommendations, not just news summaries.

Transparency

We show our confidence levels and data sources. You know what we know.

Continuous Improvement

Our models are refined based on outcome feedback and new data sources.