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Longevity Risk Profiling Based on Non-disease Specific Risk Factors Using Association Rules Mining

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Advances in Visual Informatics (IVIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11870))

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Abstract

The growing of ageing population causes many major negative impacts especially on pension plans and annuity providers. One of the impacts is on the exposure to longevity risk. There are various methods have been previously developed in order to quantify and classify longevity risk. However, there are some major drawbacks to these methods, especially in long-term mortality risk exposures studies. Therefore, this study is conducted in order to observe the potentiality of Association Rules Mining (ARM) in overcoming these drawbacks. The results show that ARM has an advantage of generating a less complex longevity profile based on the generated association rules; and can be used as an alternative method to the intricated statistical methods in profiling longevity risk exposure.

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Author information

Authors and Affiliations

  1. Institute of Visual Informatics, Universiti Kebangsaan Malaysia UKM, 43650, Bangi, Selangor, Malaysia

    Nur Haidar Hanafi & Puteri N. E. Nohuddin

  2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Seremban 3 Campus, 70300, Seremban, Negeri Sembilan, Malaysia

    Nur Haidar Hanafi

Authors
  1. Nur Haidar Hanafi
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  2. Puteri N. E. Nohuddin
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Corresponding author

Correspondence to Nur Haidar Hanafi .

Editor information

Editors and Affiliations

  1. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Halimah Badioze Zaman

  2. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  3. National Central University, Taoyuan City, Taiwan

    Timothy K. Shih

  4. Carlos III University of Madrid, Madrid, Spain

    Sergio Velastin

  5. Toyo University, Tokyo, Japan

    Tada Terutoshi

  6. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Nazlena Mohamad Ali

  7. Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Mohammad Nazir Ahmad

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Hanafi, N.H., Nohuddin, P.N.E. (2019). Longevity Risk Profiling Based on Non-disease Specific Risk Factors Using Association Rules Mining. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2019. Lecture Notes in Computer Science(), vol 11870. Springer, Cham. https://doi.org/10.1007/978-3-030-34032-2_19

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  • DOI: https://doi.org/10.1007/978-3-030-34032-2_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34031-5

  • Online ISBN: 978-3-030-34032-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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