Reinsurance Decisions and Monte Carlo Simulation

In Vol 1, Issue 2, 2010 edition of Insurance Markets and Companies: Analyses and Actuarial Computations, authors Lina S. Chan and Domingo Castelo Joaquin apply Monte Carlo simulation to support the reinsurance decision of a medical insurer. The article, entitled “Using simulation to support the reinsurance decision of a medical stop-loss provider,” presents a simplified but realistic example, where a medical insurer is evaluating a request for proposal to provide stop-loss coverage for a trust, which provides comprehensive medical coverage to employees of a major conglomerate. Using tools such as @RISK from Palisade, the authors model claims frequency and individual loss severity – analyzing risks – under the assumption that the distribution of trended claims in the most recent five-year period is a good approximation to the distribution of claims in the rating period. They then demonstrate how Monte Carlo simulation can be used to evaluate alternative reinsurance options for the stoploss provider. The authors incorporate risk and uncertainty about the true loss distribution through the use of alternative distributions to model total claims.

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