Fei Huang is an Associate Professor in the School of Risk and Actuaries Studies and Lead - Data and AI Tech of UNSW Business AI Lab. 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 UNSW in July 2020, she was a senior lecturer at the Australian National University. Fei is a columnist writing Responsible Data Science series for Actuaries Digital - Actuaries Institute Magazine. She has received more than ten awards for research and educational excellence since 2017, recognising her contributions to actuarial science, data science, and responsible AI.

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: advancing understanding and quantitative adoption of key 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. Her research on antidiscrimination insurance pricing has received several prestigious awards from both academia and professional bodies, including the Australian Business Deans Council (ABDC) Award for Innovation and Excellence in Research (Emerging and Applied).
• Climate disaster insurance: tackling challenges related to affordability, sustainability, and fairness in climate disaster insurance policy and design. This work is funded by an ARC Discovery Project "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 Australian Longevity Explorer and open-access Australian Socio-Economic Longevity Dataverse 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 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 UNSW 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.

Fei teaches actuarial data science and statistical machine learning at UNSW.  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 UNSW 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 works with insurers, consulting firms, and government agencies for transformative research and education projects, covering a wide range of topics. Examples of such collaborations include mortality modelling, fairness metrics for life insurance, Interpretable and fair insurance pricing using causal models, personalised customer management, bushfire risk modelling, multi-coverage bundled insurance pricing, claims inflation forecasting, and property insurance pricing with high-cardinality features.