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Application of Advanced Hybrid Models to Identify the Sustainable Financial Management Clients of Long-Term Care Insurance Policy

Author

Listed:
  • You-Shyang Chen

    (College of Management, National Chin-Yi University of Technology, Taichung City 411, Taiwan)

  • Chien-Ku Lin

    (Department of Business Management, Hsiuping University of Science and Technology, Taichung City 412, Taiwan)

  • Jerome Chih-Lung Chou

    (Department of Information Management, Hwa Hsia University of Technology, New Taipei City 235, Taiwan)

  • Su-Fen Chen

    (Department of Management and Information, National Open University, New Taipei City 247, Taiwan
    Department of Business, National Open University, New Taipei City 247, Taiwan)

  • Min-Hui Ting

    (Graduate Institute of Management, Chang Gung University, Taoyuan 333, Taiwan)

Abstract

The rapid growth of the aging population and the rate of disabled people with physical and mental disorders is increasing the demand for long-term care. The decline in family care could lead to social and economic collapse. In order to reduce the burden of long-term care, long-term care insurance has become one of the most competitive products in the life insurance industry. In the previous literature review, few scholars engaged in the research on this topic with data mining technology, which was motivated to trigger the formation of this study and hoped to increase the different aspects of academic research. The purpose of this study is to develop the long-term insurance business from the original list of insurance clients, to predict whether the sustainable financial management clients will buy the long-term care insurance policies, and to establish a feasible prediction model to assist life insurance companies. This study aims to establish the classified prediction models of Models I~X, to dismantle the data with the percentage split and 10-fold cross validation, plus the application of two kinds of technology as feature selection and data discretization, for the data mining of twenty-three kinds of algorithms in seven different categories (Bayes, Function, Lazy, Meta, Misc, Rule, and Decision Tree) through the data collected from the insurance company database, and to select 20 conditional attributes and 1 decisional attribute (whether to buy the long-term insurance policy or not). The decision attribute is binary classification method for empirical data analysis. The empirical results show that: (1) the marital status, total number of policies purchased, and total amount of policies (including long-term care insurance) are found to be the three important factors affecting the decision attribute; (2) the most stable models are the advanced hybrid Models V and X; and (3) the best classifier is Decision Tree J48 algorithm for the study data used.

Suggested Citation

  • You-Shyang Chen & Chien-Ku Lin & Jerome Chih-Lung Chou & Su-Fen Chen & Min-Hui Ting, 2022. "Application of Advanced Hybrid Models to Identify the Sustainable Financial Management Clients of Long-Term Care Insurance Policy," Sustainability, MDPI, vol. 14(19), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12485-:d:930573
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    References listed on IDEAS

    as
    1. Tiago Cravo Oliveira Hashiguchi & Ana Llena-Nozal, 2020. "The effectiveness of social protection for long-term care in old age: Is social protection reducing the risk of poverty associated with care needs?," OECD Health Working Papers 117, OECD Publishing.
    2. Pal, Shanoli Samui & Kar, Samarjit, 2019. "Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 162(C), pages 18-30.
    3. Wei Yang & Junkai Zhou & Huihua Chen, 2021. "Service Innovation of Insurance Data Based on Cloud Computing in the Era of Big Data," Complexity, Hindawi, vol. 2021, pages 1-10, July.
    4. Kim, Hongsoo & Kwon, Soonman, 2021. "A decade of public long-term care insurance in South Korea: Policy lessons for aging countries," Health Policy, Elsevier, vol. 125(1), pages 22-26.
    5. Karagiannidou, Maria & Wittenberg, Raphael, 2022. "Social insurance for long-term care," LSE Research Online Documents on Economics 114896, London School of Economics and Political Science, LSE Library.
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