A Recommender System for Insurance Packages Based on Item-Attribute-Value Prediction - Chawuthai, Rathachai; Choosak, Chananya; Weerayuthwattana, Chutika (My University, 2021)
Finding a proper insurance package becomes a challenging issue for new customers due to the variety of insurance packages and many factors from both insurance packages’ policies and users’ profiles for considering. This paper introduces a recommender model named INSUREX that attempts to analyze historical data of application forms and contact documents. Then, machine learning techniques based on item-attribute-value prediction are adopted to find out the pattern between attributes of insurance packages. Next, our recommender model suggests several relevant packages to users. The measurement of the model results in high performance in terms of HR@K and F1-score. In addition, a web-based proof-of-concept application has been developed by utilizing the INSUREX model in order to recommend insurance packages and riders based on a profile from the user input. The evaluation against users demonstrates that the recommender model helps users get start in choosing right insurance plans.
Keywords: Attribute-value prediction; Data analytics; Insurance package; Machine learning; Recommender system