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Life Insurance Prediction and Its Sustainability Using Machine Learning Approach

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  • Siti Nurasyikin Shamsuddin

    (Department of Mathematical Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
    Mathematical Sciences Studies, College of Computing, Informatics and Media, Universiti Teknologi MARA Cawangan Negeri Sembilan, Kampus Seremban, Seremban 70300, Malaysia)

  • Noriszura Ismail

    (Department of Mathematical Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • R. Nur-Firyal

    (Department of Mathematical Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

Abstract

Owning life insurance coverage that is not enough to pay for the expenses is called underinsurance, and it has been found to have a significant influence on the sustainability and financial health of families. However, insurance companies need to have a good profile of potential policyholders. Customer profiling has become one of the essential marketing strategies for any sustainable business, such as the insurance market, to identify potential life insurance purchasers. One well-known method of carrying out customer profiling and segmenting is machine learning. Hence, this study aims to provide a helpful framework for predicting potential life insurance policyholders using a data mining approach with different sampling methods and to lead to a transition to sustainable life insurance industry development. Various samplings, such as the Synthetic Minority Over-sampling Technique, Randomly Under-Sampling, and ensemble (bagging and boosting) techniques, are proposed to handle the imbalanced dataset. The result reveals that the decision tree is the best performer according to ROC and, according to balanced accuracy, F 1 score, and GM comparison, Naïve Bayes seems to be the best performer. It is also found that ensemble models do not guarantee high performance in this imbalanced dataset. However, the ensembled and sampling method plays a significant role in overcoming the imbalanced problem.

Suggested Citation

  • Siti Nurasyikin Shamsuddin & Noriszura Ismail & R. Nur-Firyal, 2023. "Life Insurance Prediction and Its Sustainability Using Machine Learning Approach," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10737-:d:1189319
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    References listed on IDEAS

    as
    1. Keshav Kaushik & Akashdeep Bhardwaj & Ashutosh Dhar Dwivedi & Rajani Singh, 2022. "Machine Learning-Based Regression Framework to Predict Health Insurance Premiums," IJERPH, MDPI, vol. 19(13), pages 1-15, June.
    2. Farrukh Saleem & Zahid Ullah & Bahjat Fakieh & Faris Kateb, 2021. "Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning," Mathematics, MDPI, vol. 9(17), pages 1-22, August.
    3. Siti Nurasyikin Shamsuddin & Noriszura Ismail & Nur Firyal Roslan, 2022. "What We Know about Research on Life Insurance Lapse: A Bibliometric Analysis," Risks, MDPI, vol. 10(5), pages 1-19, May.
    4. Stéphane C. K. Tékouabou & Ștefan Cristian Gherghina & Hamza Toulni & Pedro Neves Mata & José Moleiro Martins, 2022. "Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    5. Mmakgabo Pinkie Segodi & Athenia Bongani Sibindi, 2022. "Determinants of Life Insurance Demand: Empirical Evidence from BRICS Countries," Risks, MDPI, vol. 10(4), pages 1-14, April.
    6. Tomas Kabrt, 2022. "Life Insurance Demand Analysis: Evidence from Visegrad Group Countries," Eastern European Economics, Taylor & Francis Journals, vol. 60(1), pages 50-78, January.
    7. Alex Gramegna & Paolo Giudici, 2020. "Why to Buy Insurance? An Explainable Artificial Intelligence Approach," Risks, MDPI, vol. 8(4), pages 1-9, December.
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