CATIA

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.

Installation

pip install -e .

Quick Start

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
)

Key Capabilities

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

Running Tests

pytest tests/ -v

Compliance

• CAS Catastrophe Modeling Guidelines
• SOA Risk Management Framework
• NAIC Model Act (insurance applications)