Monte Carlo Simulation runs thousands or millions of iterations, each time randomly selecting input values from defined probability distributions, to generate a distribution of potential results. This method quantifies risk by showing not just expected outcomes but also probability of extreme events, confidence intervals, and sensitivity to key variables. Organizations use it for project cost estimation, portfolio risk analysis, and capital adequacy modeling. A construction firm might run 10,000 simulations of a bridge project, incorporating uncertainty in material costs, weather delays, and labor productivity, determining an 80% probability of completion within budget and a 10% risk of cost overruns exceeding 25%.