COGNITIVE AI SYSTEM

Transformer-Based Cognition Engine

Domain-Specific AI for Complex Catastrophe Risk Interpretation

INCOMPLETE DATA Raw Input Stream Missing Fields • Noise COMPLEX DATA Multi-Source Unstructured • Mixed CONTEXT DATA Domain-Specific Historical • Geospatial TRANSFORMER CORE MULTI-HEAD ATTENTION Pattern Recognition FEED-FORWARD NETWORK NORMALIZATION + DROPOUT CATASTROPHE OPTIMIZATION Domain-Tuned Weights Risk-Aware Loss Function Processing: Real-Time EXPERT KNOWLEDGE Catastrophe Domain Built-in Expert Models REGULATORY RULES Compliance Engine Policy & Standards SELF-LEARNING Adaptive AI Continuous Improvement RISK TYPE MODULES Natural Hazards (Hurricane, Flood) Climate Events (Wildfire, Drought) Infrastructure Failures Economic Disruptions Pandemic & Health Crises Geopolitical Risks INTERPRETED OUTPUT Risk Assessment Confidence Scores • Gaps Filled ACTIONABLE INSIGHTS Decision Support Recommendations • Priorities MODEL UPDATES Learning Loop Weight Adjustments • Tuning ← FEEDBACK LOOP: Continuous Learning

Expert Knowledge

Domain Expertise

Built-in catastrophe models trained on historical events, damage patterns, and expert assessments

Regulatory Rules

Compliance Engine

Embedded regulatory frameworks, insurance standards, and compliance requirements for each jurisdiction

Self-Learning

Adaptive Intelligence

Continuous model refinement through outcome feedback and real-world event validation

Technical Architecture

TRANSFORMER COMPONENTS

  • ▸ Multi-head self-attention for context understanding
  • ▸ Positional encoding for temporal data
  • ▸ Layer normalization and residual connections
  • ▸ Domain-specific loss functions for catastrophe risk

COGNITIVE CAPABILITIES

  • ▸ Gap-filling for incomplete data using learned patterns
  • ▸ Cross-domain knowledge transfer between risk types
  • ▸ Uncertainty quantification and confidence scoring
  • ▸ Real-time model adaptation based on outcomes