Description: This is the abstract of the paper Risk Classification by Fuzzy Cluster. In this paper an algorithm for risk classification is proposed. This algorithm is based on the concepts of fuzzy cluster. We test our algorithm with two examples, the results are very promising.

Description: Traditional mean-variance method does not take skewness of the random rate of return into consideration. It only considers minimum variance for a given acceptable rate of return, which limited the probability of obtaining higher rate of return. If the skewness of return for the portfolio is incorporated into model of traditional approach, then the resulting non-linear programing model is not computational efficient, which certainly is complicated. On the other hand, possibilistic programming provides more efficient techniques to solve the imprecision nature of rate of return and also preserves the original linear model.

Description: This paper uses the possibility distribution approach, or fuzzy logic, to estimate the insurance loss amount. A special class of parametric possibility distributions is used to model insurance loss variables. The parameters of the possibility distribution are estimated by combining statistical analysis of sample data and domain knowledge provided by actuarial experts. Insurance premiums are calculated using possibilistic mean and possibilistic variation. Estimation of possibility distribution of aggregate loss amount is also discussed.