Academic Profile

Ken Seng Tan, ASA, CERA, is a Professor of Actuarial Science in the Division of Banking and Finance, Nanyang Business School, Nanyang Technological University (NTU). Formerly he held the Canada Research Chair Professor in Quantitative Risk Management in the Department of Statistics and Actuarial Science, University of Waterloo, Canada, and the Associate Director of the Waterloo Research institute in Insurance, Securities and Quantitative finance (WatRISQ). He was affiliated with the Central University of Finance and Economics, Beijing, China, as the Hon. Director of China Institute for Actuarial Science, a key research institute as designated by the Ministry of Education, China.

Professor Tan is very active in conducting research, reforming education and strengthening the profession, including serving as the member of the Natural Sciences and Engineering Research Council of Canada Discovery Grant’s Mathematics and Statistics Evaluation Group, elected council member for the Society of Actuaries (SOA)’s International Section, Joint Risk Management Section, and Investment Section, member of the Research Council Committee, member of the Canadian Institute of Actuaries' task force on Liaison with Banks and Trusts, and the Chief Actuarial Advisor for “Risk Management, Economic Sustainability, and Actuarial Science Development in Indonesia (READI),” a government-to-government development project between Canada and Indonesia.

Professor Tan is the co-editor of North American Actuarial Journal (NAAJ) and the Associate Editor of the Annals of Actuarial Science and Agricultural Finance Review. He publishes in top-tier actuarial, insurance and finance journals including Journal of Risk and Insurance, Insurance: Mathematics and Economics, NAAJ, Journal of Banking and Finance, and Management Science.

Professor Tan has received several awards, including the 1996-97 Redington Prize, the NAAJ Annual Prizes 2001 and 2003, and the 2012 Charles A. Hachemeister. In 2007 he was among the few actuaries to be granted the first Chartered Enterprise Risk Analyst (CERA) credential by the SOA, based on his years of leadership in the field of enterprise risk management.
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Prof Tan Ken Seng
Director, Insurance Risk and Finance Research Centre
Professor, College of Business (Nanyang Business School) - Division of Banking & Finance
Deputy Head of Division (Actuarial Science), College of Business (NBS)

Professor Tan’s research interests lie at the intersection of actuarial science, insurance, finance, mathematics, and statistics. Much of his work relates to the development and implementation of innovative approaches to risk management, (re)insurance, and computational finance. Some of these include

• modelling and analyzing the risks involved in long-term insurance contracts with embedded financial guarantees;
• modelling and analyzing mortality and longevity risks;
• designing optimal (re)insurance policies;
• designing innovative approaches to agricultural insurance and agricultural risk management;
• providing state-of-the-art quasi-Monte Carlo algorithms for solving high-dimensional computational problems.
  • 1) Portfolio construction: the trade-off between diversification and dimension reduction 2) Optimal dynamic reinsurance policy under Mean-CVaR premium principle
  • Zhang, J., K.S. Tan and C. Weng. (2019). Optimal Index Insurance. Astin Bulletin, 49(2), 491-523.

  • Fang, M., K.S. Tan, and T. Wirjanto. (2018). Sustainable Portfolio Management under Climate Change. Journal of Sustainable Finance & Investment, 9(1), 45-67.

  • Zhu, W., K.S. Tan and C. Wang. (2017). Modeling Multi-population Longevity Risk with Mortality Dependence: A Lévy Subordinated Hierarchical Archimedean Copulas (LSHAC). Journal of Risk and Insurance, 84, 477-493.

  • Zhou, R., J.S.H. Li and K.S. Tan. (2015). Modeling Trades in the Life Market as Nash Bargaining Problems: Methodology and Insights. Economic Modelling, 51, 460-472.

  • Wang, X. and K.S. Tan. (2013). Pricing and Hedging with Discontinuous Functions: Quasi-Monte Carlo Methods and Dimension Reduction. Management Science, 59(2), 376-389.