Forecasting
#5Monte Carlo Forecasting Engine
Probabilistic forecasting with uncertainty quantification
Bayesian Cost Trajectories - 7 Scenarios
Probabilistic cost forecasts with Bayesian updating across multiple scenarios
Utilization Monte Carlo Paths - 5 Scenarios
Stochastic utilization projections with confidence bands
Risk-Adjusted Distributions - 6 Scenarios
Tail risk modeling with Value-at-Risk calculations
Overview
Advanced Monte Carlo simulation engine that generates thousands of possible future scenarios to quantify uncertainty in healthcare cost projections. Uses Bayesian updating and stochastic modeling to provide probability distributions rather than point estimates.
Key Capabilities
- 10,000+ scenario simulation runs
- Bayesian prior updating with new data
- Correlated variable modeling
- Tail risk quantification (95th, 99th percentiles)
- Confidence interval generation
- Sensitivity analysis across input parameters
- Value-at-Risk (VaR) calculations
Required Inputs
- •Historical claims distributions
- •Trend assumptions and ranges
- •Utilization probability distributions
- •Cost driver correlations
- •External factor uncertainties
- •Prior probability distributions
Generated Outputs
- •Probability density functions for costs
- •Confidence intervals (80%, 90%, 95%)
- •Expected value with standard deviation
- •Tail risk metrics (VaR, CVaR)
- •Scenario probability weightings
- •Risk-adjusted forecasts