By using computer-stored random numbers, the Monte Carlo Simulation technique creates a population and performs repeated samplings. For self­-funders, the technique is particularly useful for testing stop-loss premiums for different attachment points. Because the goal of the exercise is the testing of premiums, such exercise is generally done by an actuary.

This powerful statistical simulation tool has a near-perfect application to the self-funded health care plan.

Monte Carlo Simulations include:

1. What the expected claims would be if the plan were run without stop-loss of any kind.  Such expected claims are ranged at 67%, 95% and 99.7% comfort levels.
2. What the expected claims would be if the plan were run with specific stop-loss only.  Such expected claims are ranged at 67%, 95% and 99.7% comfort levels.
3. What the expected claims would be if the plan were run with both specific and aggregate stop-loss.  The likelihood of an aggregate claim is shown.
4.  The economic value of the specific and aggregate stop-loss and its comparison with gross stop-loss premiums is by product.
5. Of particular utility is the economic value of fine-timing stop-loss terms.