Multicriteria Recommender System for Life Insurance Plans based on Utility Theory

摘要

Background/Objectives: This paper proposes a recommender system for life insurance. Insurance is a way of managing risks and has been used as financial instrument for a long time. The remarkable increase in competition within the insurance sector of the India has resulted in an overwhelming number of insurance products being available in the market. Methods: Utility theory is applied to recommend most suitable policy to users. Grey Relation Analysis (GRA) is utilized in intuitionistic fuzzy environment to determine users’ preference over alternatives. Results/Findings: The proposed recommender system has been tested with approximately 600 potential customers of different region of Chhattisgarh (INDIA). The accuracy of the recommender system is 92.6%. Also, our recommendation system has been tested with different parameters by domain experts of different levels (Branch Manager, Insurance Advisor, Development officers). They also found the results significantly accurate. Improvement: Most existing recommender system are based on collaborative filtering technique or content based system, which mainly focuses on finding relations between products and between customers through machine learning techniques. They recommend products without concerning the users’ personalized requirement. Our recommender system takes users’ current need in account and recommends most suitable policy to them. Application: The proposed recommendation technique has worked efficiently with the life insurance products and it can also be successfully applied on the products with specific preference like medical insurance, personal vehicles, and electronic home appliances.