Design of Insurance Contract Model for Lapse Risk Prediction based on Bigdata and Artificial Intelligence

    Choong Hyong Lee
    Keywords: Big Data; Insurance Contract Lapse; Machine Learning; Platform Development; Reinforcement Learning; Risk Prediction ,

    Abstract

    Abstract
    Small and medium sized insurance companies are reducing the need for Big Data analytics mixed AI
    Tech., but they are hesitant to adopt Big Data analytics because there are no successful cases. The
    domestic financial sector has a large and diverse amount of internal data, with most transactions
    occurring in the online environment. Accordingly, Big Data activation level is higher than that of other
    industries. This means that the potential value and utilization of data analysis is very high. Accordingly, this
    study seeks to develop a model that predicts the effective lapse risk of maintenance contracts. We
    conducted an analysis to identify the effective probability of each contract and the effective causes for
    defense. In addition, the predictive model was developed using supervised machine learning, and the
    effective predictive model was updated by applying unsupervised reinforced learning. By use of Machine
    Learning, Reinforcement Learning and Clustering Analysis, we could perform an experiment on lapse
    cause mapping to get significant lapse causes

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