The Life Asia Sub-committee ensures a strong research output in the life practice area through member-led research

The Life Asia Sub-committee is the first regional Sub-committee of the IFoA formed outside the UK, with the specific purpose of supporting Life actuaries in the APAC region (particularly regarding their career growth), deliberating issues in the region as well as promoting and raising awareness of the profession in the region as a whole. This will provide strong foundation for similar forums for other regions outside the UK, and for other actuarial disciplines.


  • Review existing CPD available to the actuarial community in Asia, and propose suitable IFoA CPD, including local provision and online content.
  • Respond on behalf of the Life Board and the IFoA to life assurance focused consultations which are Pan-Asian.
  • Collate existing research materials relating to the relevant fields of insurance risks of the Asian market, and identify gaps for new research ideas.
  • Develop links with IFoA Regional Societies in Asian locations, and use these to promote local CPD and research.
  • Present relevant research findings at relevant IFoA and non-IFoA conferences, such as the IFoA Asia Conference or other suitable regional actuarial events.

Research survey to identify consumer protection needs in Asia, and the extent to which these are being met in the Asian market

Working with its members, the IFoA has commissioned a piece of research that will identify consumer protection needs, and explore the extent to which these are being met in the Asian market.  The Life Insurance markets in Asia are diverse with different levels of private and Government-backed coverage. We are taking a deeper look into selected Asian markets to compare and contrast the level of development and customer needs. A key focus will be on what customer needs exist and how these are being met with the current insurance offering in each market.  Further to this, the research will consider the experience where customer outcomes have been unsatisfactory and how this has shaped the development of the markets.

It is expected that the outputs of this research will provide the IFoA and its members with an independent (non-commercial) and focussed set of data points to enable a better understanding life insurance customer’s risk appetite, challenges, expectations, experiences, knowledge and requirements, by social demographic class.  The research outputs should provide timely and relevant population sample data to enable conclusions and make recommendations regarding the topic, fit for today’s forward looking environment. 

Membership: 2020/2021

  • Chantal Bond (Chair) -Singapore
  • TBC (Deputy Chair)
  • Bharat Khurana - India
  • Kai Zhu - China
  • Raju Seetharaman – India
  • Leong Soon Tan - Australia
  • Aman Sanganeria - India
  • Ashish Ranjan - India
  • Yuki Ishikawa - Japan
  • Thomas Sutcliffe - Hong Kong
  • Kelvin Kwok - Hong Kong
  • Suhui Wang - Hong Kong

To find out more, contact the IFoA Communities team.

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If you have any questions about a practice area, its working parties, Member Interest Groups or Board and sub-committees, please contact the Communities Team:

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Events calendar

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    13 October 2021 - 8 December 2021

    Fully booked.

    This practical course is aimed at actuaries at any stage of their career who want to develop their own growth mindset and apply it to their work setting and personal or professional lifelong learning. The content of the course builds on the lecture given by Dr Helen Wright on Growth Mindset as part of the President’s 2021 Lecture series, and will be delivered over a period of 2 months, from mid-October to early December.

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    The webinar will be presented by Cobus Daneel (Chair of Mortality Projections Committee) and Matthew Fletcher (Chair of SAPS Committee).

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    In this webinar, the authors of the 2021 Brian Hey prize winning paper present a new deep learning model called the LocalGLMnet. While deep learning models lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models, the disadvantage is that deep learning solutions are difficult to interpret and explain, and variable selection is not easily possible.

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