While healthcare claim costs are fairly predictable for large populations, existing pricing models often prove inadequate for that portion of the risk that is the most variable: large or “excess loss” claims typically covered by employer stop loss and other forms of reinsurance for high-cost claims. Even when rating and underwriting applications are able to accurately forecast expected claim costs, they are typically not structured to measure the variability in such claim costs from year to year. This is problematic when conducting detailed enterprise risk studies or estimating capital and surplus requirements for health insurance programs. This free live webcast will illustrate some applications of @RISK to solving these problems. Examples will include simulation models designed to quantify capital and surplus requirements for a health reinsurance captive; simulation models designed to price aggregate employer stop loss insurance; and simulation models designed to price aggregating specific or “inner aggregate” corridors in employer stop loss insurance.