Quantitative risk assessment employs mathematical models, historical data analysis, and simulation techniques to assign numeric values to risk likelihood and consequences, enabling cost-benefit analysis of controls and aggregation of enterprise risk exposure. Organizations use methods like Value at Risk, Monte Carlo simulation, and loss distribution modeling to quantify potential losses in monetary terms. Insurers calculate expected annual loss from natural disasters using catastrophe models incorporating geographic exposure, building characteristics, and historical event frequency. Unlike qualitative heat maps, quantitative approaches support capital allocation decisions, risk-adjusted performance measurement, and regulatory capital calculations. The methodology requires substantial data quality, appropriate model selection, and expert judgment to interpret results. Organizations often combine quantitative rigor with qualitative insights for comprehensive risk understanding.