Insurance Rate Changing : A Fuzzy Logic Approach

This article describes how fuzzy logic can be used to make insurance pricing decisions that consistently consider supplementary data, including vague or linguistic objectives of the insurer. The theory of fuzzy logic was developed in the 1970s to improve the accuracy and efficiency of expert systems, and through it, one can account for vague notions whose boundaries are not clearly defined. Using group health insurance data from an insurance company, I show how to build and fine-tune fuzzy logic models for changing rates to reflect supplementary data