E73 - Katja Hanewald - From the Frontier of Population Ageing Research

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Katja Hanewald is a Senior Lecturer in the School of Risk and Actuarial Studies at UNSW Sydney, which has been ranked 1st in the world for Actuarial Science, Risk Management and Insurance research. She is also the Coordinator of the Actuarial Co-op Program at the university. Apart from that, Katja is the Director of Research of the Ageing Asia Research Hub, which is hosted by the ARC Centre of Excellence in Population Ageing Research (CEPAR).

Katja’s major expertise and research interests are risk management for retirement financial products and insurance responses to population ageing. Her research papers and articles have been published in all leading international insurance and actuarial journals and major economics journals.

Katja has been a research-driven academician for over a decade from the very beginning of her career. After obtaining her doctoral degree from the School of Business and Economics at Humboldt-Universität zu Berlin in November 2010, she worked as a postdoc researcher in the ARC Centre of Excellence in Population Ageing Research (CEPAR) at UNSW Sydney from 2011 to 2013. She moved back to Germany in 2013 to work for the German Federal Ministry of Finance from 2013 to 2015. In 2016, she returned to Sydney to re-join CEPAR and UNSW Sydney, and is now a Senior Lecturer in the School of Risk & Actuarial Studies.

This episode starts with Katja sharing with us about her background in research and what led her to focus on economics, actuarial, insurance, and risk management.

Following this, the discussion moves through the following areas:

  • How the School of Risk and Actuarial Studies at UNSW is helping actuarial students to explore different career paths, such as traditional actuarial works as well as data science.
  • Katja’s research work around retirement insurance product development.
  • Deep dive into creating a new model for the insurers, combining the General Linear Model (GLM) and neural network.
  • How the new model addresses some of the shortcomings of the GLM.
  • How the model allows analytics professionals and actuaries to incorporate additional factors, linear and nonlinear relationships among variables, and emphasize expert opinion.
  • How the insurers could benefit from this new model in multiple ways, including understanding the mortality modelling using individual-level data, solving risk classification problems for insurance claims, microsimulation health models, and long term care insurance pricing.

If you are an actuary or data scientist working on building analytics models for your applications, not only will you find Katja’s proposals to be uniquely refreshing, but you will also learn new perspectives in combining different models to solve the problems you may have at hand.

If you are a senior executive in the insurance or reinsurance industry, this could be useful in understanding and developing a new insurance product for the ever-changing consumer needs.

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