Design of Insurance Product Recommendation Model based on Bigdata and Artificial Intelligence

    Choong Hyong Lee ,Sangwon Lee
    Keywords: Artificial Intelligence, Bigdata, Collaborative Filtering, Insurance Product, Platform Development, Recommendation. ,


    Past research into insurance businesses is limited to current status. There is a lot to be desired about
    actively developing and sharing analytical models using Bigdata. The purpose of this study is to develop
    a model that recommends collateral and subscription amounts at the customer group level. First, we
    clustered our customers into a group of customers of similar economic size. We compared the collateral
    and subscription amount of the products customers in the cluster. This allowed the recommendation of
    collateral and subscription amounts to the level of customers in the same cluster. Customer mappings
    were mapped to clusters closest to the customer’s cluster variables using the R Package ‘Kohonen’.
    Through this research model with SOM-based Two Step Clustering and Collaborative Filtering, insurance
    companies can provide opportunities to internalize their analysis. In addition, it will be possible to improve
    the effectiveness of life insurance and ICT executives’ work, and to support higher-level decisions using AI

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