Application of measures of fuzziness to risk classification in insurance

The authors show how measures of fuzziness can be used to classify risks considered for insurance purposes, using the example of life insurance. A risk given is described as a fuzzy preferred risk, and then its fuzziness is measured to indicate its classification. They compare various measures of fuzziness, including the classical entropy measure of De Luca and Termini (1992), the distance measure of Yager (1979), and the axiomatic product measure (see e.g., Ebanks (1983)), and discuss their applicability to risk classification