A Decision Model for Insurance Advisors: A Case Study

This study presents a decision model using fuzzy inference system (FIS) for insurance advisors to identify and suggest appropriate policies to potential or existing clients which can minimize the subjective prejudice of the insurance advisors. The proposed model consist of four main process- (i) selecting inputs and outputs, (ii) identifying membership functions, (iii) constructing fuzzy rule base and (iv) validating the rule base. The decision model is developed based on five types of insurance policies under the Family Plan. Five attributes are selected which are age, gender, marital status, monthly income and job risk. These inputs are transformed into fuzzy variables using triangular membership functions and then used to construct the fuzzy rule base. Apart from machine learning, an expert is also engaged to verify the model. To validate the model, records of new policy subscriber are applied. Results and findings are also discussed at the end of this paper.