Catastrophe AI System for Climate Risk Modeling
CATIA is a production-ready Python library for catastrophe risk modeling. It combines climate data ingestion, ML-based risk prediction, actuarial loss simulation, and mitigation optimization into a unified framework.
pip install -e .from catia.data_acquisition import fetch_climate_data from catia.risk_prediction import train_risk_model from catia.financial_impact import run_financial_impact_analysis from catia.mitigation import generate_mitigation_recommendations # Fetch climate data climate_data = fetch_climate_data(use_mock=True) # Train risk model model = train_risk_model(climate_data) # Run financial simulation results = run_financial_impact_analysis( annual_frequency=2.5, mean_severity=50_000_000, n_simulations=10_000 ) # Get mitigation recommendations recommendations = generate_mitigation_recommendations( expected_annual_loss=results['expected_loss'], budget=10_000_000 )
| Module | Description |
|---|---|
data_acquisition |
Climate data from NOAA, ECMWF; socioeconomic data from World Bank |
risk_prediction |
ML models for catastrophe probability and severity |
financial_impact |
Monte Carlo simulation with frequency-severity models |
extreme_value |
EVT/GPD tail modeling for 100-1000 year events |
uncertainty |
Bootstrap confidence intervals for all risk metrics |
correlation |
Copula-based multi-peril dependency modeling |
ensemble |
Voting and stacking ensembles for robust predictions |
explainability |
SHAP-based model interpretability |
backtesting |
Historical validation and model monitoring |
mitigation |
Budget-constrained optimization of risk reduction strategies |
pytest tests/ -v
- CAS Catastrophe Modeling Guidelines
- SOA Risk Management Framework
- NAIC Model Act (insurance applications)