Researcher

My Expertise

Ethical Insurance Pricing, Insurance Data Science

Keywords

Fields of Research (FoR)

Insurance studies, Investment and risk management, Statistical data science, Marketing Management (incl. Strategy and Customer Relations), Business Ethics

SEO tags

Biography

Fei Huang is an Associate Professor in the School of Risk and Actuarial Studies, UNSW 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 UNSW in July 2020, she was a senior lecturer at the Australian National University.  She has received numerous awards for research and...view more

Fei Huang is an Associate Professor in the School of Risk and Actuarial Studies, UNSW 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 UNSW 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. 

Visit her personal website: https://www.feihuang.org/.

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 ARC Discovery Project "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 Australian Business Deans Council (ABDC) Award for Innovation and Excellence in Research (Emerging and Applied) and the North American Actuarial Journal Best Paper Award
  • Climate disaster insurance: tackling challenges related to affordability, sustainability, and fairness in climate disaster insurance policy and design. This research 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 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 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. 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 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 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. 


My Grants

  1. Australian Research Council (ARC) Discovery Project, (with Yanrong Yang, Samuel Muller), 2026-2028, $636,574, "Responsible Statistical Learning: Uncertainty, Fairness and Transparency".
  2. Australian Research Council (ARC) Discovery Project,  (with Jeremy Moss), 2025-2027, $439,488, "Dealing with Climate Disasters"
  3. National Industry PhD Program Award, Industry Researcher PhD category (for Laura Zhao), 2024-2032, $232,000 (max from government to support the project). "Personalised Risk Management for Australian Travellers utilizing Causal and Generative AI Model".
  4. Fairness Metrics for Life Insurance (with Milliman), SOA Research Grant, 2023, US$30,000, "Fairness Metrics for Life Insurance" 
  5. CAS Individual Grant Competition, collaborated with Joshua Loftus (LSE), US$17,554, "Interpretable and Fair Insurance Risk Pricing using Causal Models".

My Qualifications

  • Senior Fellow of Advance HE (SFHEA)
  • PhD, Australian National University
  • MPhil, University of Hong Kong
  • BSc, Xiamen University

My Awards


My Research Activities

  1. Climate Disaster Insurance - ARC Discovery Project (2025-2028) Dealing with Climate Disasters with Jeremy Moss
  2. Responsible AI -- Fairness and Discrimination in insurance pricing
    (1) The Discriminating (Pricing) Actuary (with Edward (Jed) Frees), NAAJ, 2023
    (2) Anti-discrimination Insurance Pricing: Regulations, Fairness Criteria, and Models (with Xi Xin), NAAJ,  2024 
    This paper won the inaugural Carol Dolan Actuaries Summit Prize, ASTIN Colloquium Best Paper Award, American Academy of Actuaries' Academy Award for Research, and North American Actuarial Journal Best Paper Award.
    (3) Welfare Implications of Fair and Accountable Insurance Pricing (with Hajime Shimao), 2024 (SSRN version)
    (4) Welfare Implications of Fairness Regulations in Insurance Cost Modeling: A Multi-Method Study (with Hajime Shimao and Warut Khern-am-nuai), 2025 (SSRN version)
    (5) Learning Fair Decisions with Factor Models: Applications to Annuity Pricing (with Junhao Shen, Yanrong Yang, and Ran Zhao), 2025 (arXiv version)
    (6) Marginal Fairness: Fair Decision-Making under Risk Measures (with Silvana M. Pesenti), 2025 (SSRN versionarXiv version)
  3. Responsible AI -- Transparency and Interpretability
    (1) Pitfalls in machine learning interpretability: Manipulating partial dependence plots to hide discrimination. (with Xi Xin and Giles Hooker) (arXiv version)
  4. Mortality and Retirement Income -- Socio-economic Longevity Differentials
    (1) Towards Fairer Retirement Outcomes: Socio-economic Mortality Differentials in Australia (with Francis Hui and Andres Villegas) (SSRN version / Australian Socio-Economic Mortality Dataverse /Australian Longevity Explorer )
  5. Mortality and Retirement Income -- Advanced-age mortality modelling
    (1) Modelling life tables with advanced ages: An extreme value theory approach (with Ross Maller and Ning Xu), Insurance: Mathematics and Economics, 2020
  6. Customer Churn Analysis and Customer Management
    (1) Multi-State Modelling of Customer Churn (with Yumo Dong, Edward (Jed) Frees, Francis Hui), ASTIN Bulletin, 2022 (SSRN version)
    (2) A Joint Model of Cost and Churn for Stochastic Cost Industries (with Yumo Dong, Edward (Jed) Frees, Francis Hui, and Harald Van Heerde) (SSRN version)

Software Packages

STLT: STLT fits the Smooth Threshold Life Table (STLT) and Dynamic Smooth Threshold Life Table (DSTLT) as outlined in Modelling life tables with advanced ages: An extreme value theory approach. It also provides S3 methods for predicting using fitted STLT and DSTLT models, as well as plotting the fitted lines.

Open Dataset

 Australian Socio-Economic Mortality Dataverse

Online Interactive App 

Australian Longevity Explorer: Understanding Australian Socio-Economic Longevity and its Retirement Income Implications


My Research Supervision


Supervision keywords


Areas of supervision

  • Responsible Data-driven Decision Making
    * Insurance discrimination and pricing fairness
    * Algorithmic bias mitigation
    * Interpretable machine learning 
    * Mortality modelling
  • Climate Disaster Insurance
    * Affordability and fairness
  • Customer Relationship Management
    * Pricing
    * Customer Retention
    * Customer Lifetime Value

Currently supervising

  • Yuan Zhuang (primary supervisor), PhD student at UNSW Sydney
  • Laura Zhao (primary supervisor), part-time PhD student at UNSW Sydney, National Industry PhD Program
  • Xi Xin (primary supervisor), PhD student at UNSW Sydney
  • Yumo Dong (primary supervisor), PhD student at the Australian National University, graduated.

My Engagement

Recent Invited Industry Talks:

  1. Young Actuaries Conference, Actuaries Institute, 2025
  2. Navigating The Future: Understanding AI Risk & Opportunities From An Actuarial Perspective, Institute and Faculty of Actuaries (IFoA), 2025
  3. Understanding Bias in Actuarial Data-driven Decision Making, Society of Actuaries, 2025 (close to 700 attendees)
  4. Model Risk Management and Ethical AI Adoption, International Actuarial Association, 2025

 

Media Coverage:

I am a columnist writing "Responsible AI" series for Actuaries Digital, the magazine of the Actuaries Institute. Read my articles here

Fair Insurance Pricing under AI and Climate Risk

Responsible AI Adoption

How Insurance Works

Climate Disaster Insurance

Longevity, Superannuation, and Retirement Income System

Industry-engaged Teaching Innovation


My Teaching

  1. ACTL4305/5305 Actuarial Data Science Applications (2020 - now)
    Datathon project (2024): Competing for Pet Insurance Customers: A Pricing Competition. Industry Partners: Fetch, Airtree, Finity. Event video; Media Coverage (When Students Becomes Starts, Actuaries Digital)
    Sandbox project (2023): Understanding Bushfire Event Risk Across Australia. Industry Partner: Suncorp. Event Video
    Sandbox project (2022): Multi-coverage Claim Modelling for Insurance Packaged Products. Industry Partner: IAG. Event Video
    Sandbox project (2021): Develop Pricing Models for SME Building Insurnace with Features Having High Cardinality. Industry Partner: Suncorp. Media Coverage
  2. ACTL3142/5110 Statistical Machine Learning for Risk and Insurance Applications (2021-2022)
    Sandbox project (2022): Predicting Claims Inflation for Commercial Auto Insurance Pricing. Industry Partner: IAG. Event Video
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