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Why to Buy Insurance? An Explainable Artificial Intelligence Approach

Author

Listed:
  • Alex Gramegna

    (Department of Economics and Management, Neosurance and University of Pavia, 27100 Pavia PV, Italy)

  • Paolo Giudici

    (Department of Economics and Management, Neosurance and University of Pavia, 27100 Pavia PV, Italy)

Abstract

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well.

Suggested Citation

  • Alex Gramegna & Paolo Giudici, 2020. "Why to Buy Insurance? An Explainable Artificial Intelligence Approach," Risks, MDPI, vol. 8(4), pages 1-9, December.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:4:p:137-:d:461564
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    References listed on IDEAS

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    1. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
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    Cited by:

    1. Javier Sada Bittini & Salvador Cruz Rambaud & Joaquín López Pascual & Roberto Moro-Visconti, 2022. "Business Models and Sustainability Plans in the FinTech, InsurTech, and PropTech Industry: Evidence from Spain," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
    2. Raval, Khushi Jatinkumar & Jadav, Nilesh Kumar & Rathod, Tejal & Tanwar, Sudeep & Vimal, Vrince & Yamsani, Nagendar, 2024. "A survey on safeguarding critical infrastructures: Attacks, AI security, and future directions," International Journal of Critical Infrastructure Protection, Elsevier, vol. 44(C).
    3. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
    4. Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
    5. Alex Gramegna & Paolo Giudici, 2022. "Shapley Feature Selection," FinTech, MDPI, vol. 1(1), pages 1-9, February.
    6. Esther Salmerón-Manzano, 2021. "Legaltech and Lawtech: Global Perspectives, Challenges, and Opportunities," Laws, MDPI, vol. 10(2), pages 1-9, April.
    7. 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.

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