Life Insurance-Based Recommendation System for Effective Information Computing

Abstract

Due to the rapid advancements in information and communication technologies, the digital data is exponentially growing on the internet. The insurance industry with tough competition has emerged as information rich domain based on health, assets, and life insurance for public. Customers expect to receive personalized services that match their needs, preferences, and lifestyles. But a large portion of population is still unfriendly to the insurance selection. Major reasons could be the time and complexities involved in selection of suitable policies. This paper presents the state of the art of the research done in insurance recommendation systems at national and international levels. Multi-criteria decision-making methods are compared with collaborative filtering and data mining techniques. Their suitability to the field of life insurance recommendation is analyzed. The paper identifies the lack of public dataset of customers and life insurance policies and highlights the need for a personalized, neutral, and unified model for effective information computing for life insurance recommendations.