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Fei Huang is an Associate Professor in the School of Risk and Actuarial Studies, 91ɫ��Ƭ Business School. She received her BSc. in Mathematics from Xiamen University, MPhil in Actuarial Science from the University of Hong Kong, and��PhD��in Actuarial Studies from the Australian National University.��Before joining 91ɫ��Ƭ in July 2020, she was a senior lecturer at the Australian National University.�� She has received numerous awards for research and educational excellence, recognising her contributions to actuarial science, data science, and responsible AI.��

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Fei’s research focuses on responsible AI and data-driven decision-making, with particular emphasis on insurance, risk management, and actuarial applications. She draws on tools from statistics, machine learning, economics, and marketing to design solutions that are not only accurate and interpretable, but also fair, stable, and privacy-preserving. A central aim of her work is to ensure that insurance and superannuation (retirement income products) remain equitable, affordable, and sustainable in the face of advancing AI and a changing climate. Her recent research spans three key areas:

• Responsible AI and Fair Insurance Pricing: advancing understanding and quantitative adoption of key responsible AI principles, especially in the insurance and superannuation industries, including accuracy, fairness, interpretability, uncertainty quantification, privacy, and stability, to ensure trustworthy and ethical data-driven decision making. This research is funded by an "Responsible Statistical Learning: Uncertainty, Fairness and Transparency". Her��research on antidiscrimination insurance pricing has received several prestigious awards from both academia and professional bodies, including the����and the��.��
• Climate disaster insurance: tackling challenges related to affordability, sustainability, and fairness in climate disaster insurance��policy and design. This research is funded by an ��"Dealing with Climate disasters" (2025-2028).
• Mortality and retirement income: examining socio-economic mortality differentials and their implications for��retirement income and annuity systems. She led��the development of the interactive dashboard,�������Ի����DZ���-����������� to help Australians, industry, and government better understand longevity patterns, using linked individual-level national datasets.

Her work has been published in leading actuarial journals and received several prestigious research awards, including the North American Actuarial Journal Best Paper Award, National Industry PhD Program Award, ABDC Innovation and Excellence Award for Research (Emerging Applied Category), Carol Dolan Actuaries Summit Prize, Amecian Academy of Actuaries' Award for Research, ASTIN Colloquium Best Paper Award, the Actuaries Institute's Volunteer of the Year Award in the Spirit of Volunteering category, and the 91ɫ��Ƭ Business School SDG Research Impact Award. Her research has been funded by multiple international and domestic institutions, including the Australian Research Council (Discovery Project), the Society of Actuaries, and Milliman. She received the Dean’s Award for Distinction (the highest individual honour) in 2025, recognising her��exceptional impact across the faculty and beyond, and demonstrating breadth, depth, and influence.

Fei teaches actuarial data science and statistical machine learning at 91ɫ��Ƭ.����By collaborating with industry partners, she incorporates contemporary industry challenges into the course syllabus to offer a unique industry-engaging experience for students.��Her educational excellence has been recognised by winning the 91ɫ��Ƭ John Prescott Award for Outstanding Teaching Innovation (2022), the ANU Vice-Chancellor’s Award for Teaching Excellence in the Early Career Category (2018), and the ANU College of Business and Economics Award for Teaching Excellence in the Early Career Category (2017).����Fei is a Senior Fellow of Advance HE (SFHEA).

Fei is a leading voice in the profession on responsible AI in insurance, serving as a columnist for Actuaries Digital and working widely with industry and government on issues ranging from fairness in pricing to longevity risk and climate resilience. Her work bridges research, policy, and practice, shaping regulatory thinking and supporting the responsible use of data-driven decision systems. She has advised regulators internationally, including serving as an invited expert providing testimony to the New York State Assembly Public Hearing on AI in Insurance.��