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Risk Assessment for Personalized Health Insurance Based on Real-World Data

Author

Listed:
  • Aristodemos Pnevmatikakis

    (Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium)

  • Stathis Kanavos

    (Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium)

  • George Matikas

    (Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium)

  • Konstantina Kostopoulou

    (Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium)

  • Alfredo Cesario

    (Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium
    Scientific Directorate, Fondazione Policlinico A. Gemelli IRCCS, 00168 Rome, Italy)

  • Sofoklis Kyriazakos

    (Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium
    Business Development and Technology Department, School of Business and Social Sciences, Aarhus University, Birk Centerpark 15, 7400 Herning, Denmark)

Abstract

The way one leads their life is considered an important factor in health. In this paper we propose a system to provide risk assessment based on behavior for the health insurance sector. To do so we built a platform to collect real-world data that enumerate different aspects of behavior, and a simulator to augment actual data with synthetic. Using the data, we built classifiers to predict variations in important quantities for the lifestyle of a person. We offer a risk assessment service to the health insurance professionals by manipulating the classifier predictions in the long-term. We also address virtual coaching by using explainable Artificial Intelligence (AI) techniques on the classifier itself to gain insights on the advice to be offered to insurance customers.

Suggested Citation

  • Aristodemos Pnevmatikakis & Stathis Kanavos & George Matikas & Konstantina Kostopoulou & Alfredo Cesario & Sofoklis Kyriazakos, 2021. "Risk Assessment for Personalized Health Insurance Based on Real-World Data," Risks, MDPI, vol. 9(3), pages 1-15, March.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:3:p:46-:d:508517
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    References listed on IDEAS

    as
    1. Weidner, Wiltrud & Transchel, Fabian W.G. & Weidner, Robert, 2017. "Telematic driving profile classification in car insurance pricing," Annals of Actuarial Science, Cambridge University Press, vol. 11(2), pages 213-236, September.
    2. Elizabeth M. Joseph-Shehu & Busisiwe P. Ncama & Omolola O. Irinoye, 2019. "Health-Promoting Lifestyle Behaviour: A Determinant for Noncommunicable Diseases Risk Factors Among Employees in a Nigerian University," Global Journal of Health Science, Canadian Center of Science and Education, vol. 11(12), pages 1-15, November.
    3. Marjan Qazvini, 2019. "On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study," Risks, MDPI, vol. 7(3), pages 1-17, June.
    4. Lluís Bermúdez & Dimitris Karlis & Isabel Morillo, 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models," Risks, MDPI, vol. 8(1), pages 1-13, January.
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    Cited by:

    1. Ritu Srivastava & Anupama Prashar & S.Veena Iyer & Piyush Gotise, 2024. "Insurance in the Industry 4.0 environment: A literature review, synthesis, and research agenda," Australian Journal of Management, Australian School of Business, vol. 49(2), pages 290-312, May.

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