Adjusting Indicated Insurance Rates: Fuzzy Rules That Consider Both Experience and Auxiliary Data

This paper describes how an actuary can use fuzzy logic to adjust insurance rates by considering both claim experience data and supplementary information. This supplementary data may be financial or marketing data or statements that reflect the philosophy of the actuary's company or client. The paper shows how to build and fine-tune a rate-making model by using workers compensation insurance data from an insurance company.