A Research For Insurance Pricing Based On Fuzzy MC Model

Insurance pricing is one of the core issues in the insurance studies, traditional pricing issues are built on the theory of probability and statistics and stochastic processes. Considering that the judgment of the decision-makers of the market is subjective, this paper introduced the thought of fuzzy mathematic to deal with the issue of insurance pricing.On one hand, basing on the Myers-Cohn model, which is classical in insurance pricing, this paper introduced fuzzy variables, fuzzifies the classical model, derives an equation relative to fuzzy MC premium, and solved the fuzzy premium by the algorithm of fuzzy numbers and an iterative approach. Solving the model gives the acceptable premium range. It is advantage to the decision-makers to consider various factors and ultimately determine the specific premium.At first, for the classical Myers-Cohn model transfers the entire taxes of the insurance companies to the policyholders, which increases the investment risk of insured, this article improved the original model and raised that returning a part of investment income of the insurer back to the policyholders, which ultimately reflected in the reduction in the premium. To estimate the investment income of the insurer, the optimal fuzzy portfolio was introduced. Using the satisfaction of fuzzy constraints as the risk measurement of investment, under a given risk tolerance level, based on the nature of fuzzy mathematics, this article converted fuzzy programming model to definite linear programming problem, and solved the optimal return ratio.Finally, a membership function which can involve random and fuzzy information was constructed in this paper. Random information is reflected in the optimal return ratio, while fuzzy information is reflected in the fuzzy degree of the membership function. Then the new form of membership function was introduced into the improved fuzzy MC model, and the solution of the premium can contain more information.