Sherry S. Chan

EY

FSA, EA, MAAA, FCA 
Managing Director in Actuarial Advisory Practice

Sherry Chan is an award-winning actuary with decades of outstanding leadership in strategic policy, operations, relationship management, and communications, currently serving as a Managing Director in EY’s actuarial advisory practice.

Prior to joining EY, Ms. Chan was the Chief Strategy Officer of Atidot, an insurtech specializing in state-of-the-art AI for the life insurance ecosystem to better understand their policyholder behavior throughout the value chain.

Previously, Ms. Chan was chosen unanimously by New York City elected officials and union presidents to serve as the City’s 5th Chief Actuary in its 100+ year history, becoming one of the highest ranked Asian-American officials in the City’s administration. Ms. Chan also previously served as the Chief Actuarial Officer for the Ohio Public Employees Retirement System and as Chief Actuary for the State Teachers Retirement System of Ohio, the two largest state pension systems in Ohio.

Ms. Chan is on the Boards of the Society of Actuaries, The Ohio State University (OSU) College of Arts & Sciences, and Abacus Actuaries, while also serving on the US Railroad Retirement Board’s Actuarial Advisory Committee. Ms. Chan established OSU’s first actuarial endowment in its 150+ history, the Sherry S. Chan Actuarial Endowment. She is also the original co-founder of Abacus Actuaries, an international non-profit organization dedicated to supporting Asians to achieve success in the actuarial profession.

Panel: Actuarial Research Funding Landscapes: Opportunities and Guidance

Panelists: 

• Morgan Bugbee (Casualty Actuarial Society, Vice President-Research & Development) 
• Ian Duncan (Society of Actuaries, President-Elect and Vice Chair) 
• Steve Jackson (American Academy of Actuaries, Director of Research)
• Barbara Ransom (National Science Foundation, Program Director)

Abstract: This plenary session brings together representatives from organizations that support actuarial and related research to discuss current funding opportunities, proposal priorities, and effective strategies for developing competitive applications. Panelists will share perspectives on what distinguishes strong proposals, common challenges in the review process, and ways researchers can better align their ideas with available funding programs. The session is intended to provide practical guidance for faculty, students, and researchers seeking to navigate the evolving landscape of actuarial research support.

Daniel Bauer

Wisconsin School of Business, University of Wisconsin–Madison

Professor of Risk and Insurance and Hickman-Larson Distinguished Chair in Actuarial Science  
Senior Associate Dean for Programs 

Dani Bauer is the Senior Associate Dean for Programs at the Wisconsin School of Business and the Hickman-Larson Distinguished Chair in Actuarial Science in the Department of Risk and Insurance.

Dani’s research appears in leading journals in actuarial science, economics, finance, management, and statistics. He has been recognized with several research and teaching awards. Dani has served as co-editor and in other senior editorial roles at leading journals in actuarial science and risk management. He has taught classes in actuarial science, quantitative finance, risk management, data analytics, and machine learning, and has mentored doctoral students who have gone on to faculty positions at leading universities. In his current role as Senior Associate Dean for Programs, Dani oversees the entire program portfolio of the Wisconsin School of Business and the teams managing the undergraduate and graduate programs. Before stepping into the dean’s office, he chaired the Department of Risk and Insurance. He has served as an adviser and expert on insurance and insurance-linked investments for government agencies and industry.

Dani earned his MS in Statistics from San Diego State University and his PhD in Mathematics from Ulm University. Prior to joining the faculty at UW-Madison, Dani was the Dai-ichi Life Insurance Company Endowed Chair in Actuarial Science and Risk Management at Culverhouse College of Commerce at the University of Alabama and Robert W. Batten Chair in Actuarial Science at the J. Mack Robinson College of Business at Georgia State University.

Presentation: What Is a Life Worth? Multi-State Actuarial Models for Valuing Health and Longevity

Abstract: The Value of Statistical Life (VSL) is one of the most consequential numbers in public policy, shaping investments in medical research, environmental protection, and pandemic preparedness, and underlying cost-effectiveness thresholds in health systems around the world. Yet VSL is largely absent from actuarial training and research — even though the models economists use to derive it are close cousins of the models actuaries build every day.

In this talk, I will introduce VSL and its generalization, the Value of Statistical Illness (VSI), and show how actuarial tools — continuous-time multi-state models, life contingent valuation, and life-cycle optimization under incomplete markets — can be combined to produce novel frameworks for valuing health and longevity. I will illustrate with recent results explaining why people pay more per quality-adjusted life-year when sick, and why conventional valuations of medical advances can substantially overstate aggregate welfare gains.

Phillip Yam

Department of Statistics and Data Science, The Chinese University of Hong Kong

Professor
Assistant Dean (Education)
Co-director, Interdisciplinary Major Program in Quantitative Finance and Risk Management Science
Fellow, Centre for Promoting Science Education, Faculty of Science

Phillip Yam holds a BSc in Actuarial Science with First Class Honours from The University of Hong Kong, an MASt with Distinction from University of Cambridge, and a DPhil from University of Oxford. He was awarded the E. M. Burnett Prize in Mathematics from Cambridge, and junior research fellowship from Erwin Schrödinger International Institute for Mathematics and Physics at University of Vienna. He is now Professor and Director of Quantitative Finance and Risk Management Science programme at Department of Statistics and Data Science, Assistant Dean (Education) at Faculty of Science, and Fellow of Centre for Promoting Science Education in The Chinese University of Hong Kong. He has been a research fellow at Hausdorff Research Institute for Mathematics in University of Bonn, a Visiting Professor at both Columbia University and Naveen Jindal School of Management at University of Texas at Dallas, and a Distinguished Visiting Scholar at University of New South Wales. He is an Elected Member of the International Statistical Institute, and Editor of a leading actuarial journal: Insurance: Mathematics and Economics. He has published around 120 articles in leading journals in actuarial science, applied mathematics, control and optimization, data analytics and statistics, engineering management, mathematical finance, and operations research, as well as pioneering monographs on mean field theory and financial data analytics, the latter "Financial Data Analytics: with Machine Learning, Optimization and Statistics" is published in Wiley Finance Series, one of the most renowned financial book series worldwide. Some of his representative works appear in flagship journals such as Annals of Statistics, Journal of the European Mathematical Society, Journal of the Royal Statistical Society: Series B, and Proceedings of the National Academy of Sciences. He has supervised 30+ students and postdocs, many now prominent in academia and industry worldwide. His research project “Comonotone-independence Bayes Classifier (CIBer)” was awarded a Silver Medal in the 48th International Exhibition of Inventions Geneva in 2023. For more information, please visit https://www.sta.cuhk.edu.hk/scpy.

Presentation: A Way of Quantifying Cyber Risk: from Model Building to Product Pricing

Abstract: As a major challenge in emerging risk modelling in FinTech and InsurTech, the actuarial community is so eager for more effective methods in predicting claim numbers/severities of cyber attacks based on limited real data; indeed, conventional statistical tools fail to apply for these cyber risk datasets due to the dominant presence of categorical covariates. To address this challenge, our talk proposes a novel superposed marked Hawkes process integrating categorical covariate information to infer hidden clustering structures; particularly, by employing classifiers such as CIBer, CART, and MLP, we iteratively optimize both model parameters and cluster partitions using common machine learning tools, such as mini-batch stochastic gradient descent method. The effectiveness of this approach is demonstrated through empirical studies with benchmark cyber risk datasets, leading to notably improven prediction for frequencies (also for severities). Meanwhile, we can also provide a statistical diagnosis of the underlying model parameters. Last but not least, with this new process, all existing pricing methods should be revisited; while we here highlight the use of Fourier-COS method to effectively price different insurance products against cyber risk, namely via a finite series involving the Laplace functional of the corresponding compound process.

Erica Baird

Milliman

PHD, FSA, MAAA
Principal & Consulting Actuary

Erica is a principal and consulting actuary with the Minneapolis office of Milliman. She joined the firm in 2013. 

Erica has extensive experience designing and calibrating predictive models, and she is involved in ongoing research and development of the models included in the Milliman Advanced Risk Adjusters (MARA) software. She has also developed custom predictive models for application in unique circumstances, and has worked with models in the context of commercial, Medicare, and Medicaid populations.

In addition to predictive modeling, Erica also has experience evaluating outcomes for payers and providers for the purposes of trend guarantees or shared savings arrangements. She also has experience pricing and filing premium rates for group health insurance plans subject to the Affordable Care Act’s healthcare reforms and in developing bids for Medicare Advantage and Part D plans.

Prior to joining the firm, Erica taught mathematics while pursuing her PhD.

Presentation: The intersection between health equity and risk adjustment in Medicaid

Abstract: Persistent health disparities have prompted increased attention to health equity among federal and state policymakers. State Medicaid agencies, as major purchasers of care for diverse and often underserved populations, are uniquely positioned to influence equity through financing and policy decisions. To support these efforts, we examined how clinical, demographic, geographic, and social factors—including race and ethnicity—are associated with patterns of healthcare utilization in Medicaid populations.

A central challenge in this work is the incomplete and inconsistent collection of race and ethnicity data, which constrains the measurement and interpretation of inequities. Prior to this analysis, and through research sponsored by the Society of Actuaries Research Institute, we conducted a separate line of research evaluating racial and ethnic imputation methods currently in use, including Bayesian Improved Surname Geocoding (BISG), with a case study using Medicaid data. This earlier work provided an important perspective on the strengths, limitations, and appropriate applications of imputation algorithms in actuarial research.

Together, these complementary research streams highlight the interplay between methodological choices and equity analysis. This presentation reflects on how advances in data and modeling—when applied thoughtfully—can help actuaries and policymakers better understand disparities, while underscoring the limitations and challenges associated with the use of imputed race and ethnicity data in equity‑focused decision‑making.

Fei Huang

School of Risk and Actuarial Studies, UNSW Business School

Associate Professor of Risk and Actuarial Studies

Dr. Fei Huang is an Associate Professor of Risk and Actuarial Studies at UNSW Business School. Her research lies at the intersection of responsible AI, insurance, and data-driven decision-making, with a focus on ensuring that insurance and retirement income systems remain fair, sustainable, and resilient to climate risk and technological change. Drawing on statistics, machine learning, economics, and actuarial science, she develops insurance solutions that are accurate, interpretable, and equitable. Her work has received multiple academic and professional awards and is supported by major competitive funding. Fei works closely with industry and regulators on responsible AI in insurance. Her work bridges research, policy, and practice, shaping regulatory thinking and supporting the responsible use of data-driven decision systems. For more information, please visit www.feihuang.org. 

Presentation: The Fair Pricing Playbook: A Practical Framework for Responsible AI in Algorithmic Pricing Systems

Abstract: Advances in AI and data science are transforming insurance pricing, enabling increasingly granular risk assessment and improved predictive accuracy. However, these developments raise fundamental challenges for fairness, transparency, and accountability. Pricing models can inadvertently embed indirect discrimination through complex data relationships and opaque algorithms, creating tensions between actuarial principles, regulatory requirements, and societal expectations. Regulators are responding, for example, the EU AI Act classifies insurance risk assessment as a high-risk AI application, and proposals in Colorado, New York, and other jurisdictions now require insurers to test pricing algorithms for unfair discrimination.

This talk presents The Fair Pricing Playbook (fair.feihuang.org), an open-source practical framework that translates research from actuarial science, economics, statistics, and machine learning into a concrete four-step workflow: defining a fairness criterion appropriate to the regulatory context, building a fair pricing model that meets it, measuring the welfare implications for consumers and the firm, and auditing a deployed system. Drawing on recent work in anti-discrimination insurance pricing, fairness testing, and welfare analysis, the talk examines how fairness objectives can be meaningfully and responsibly integrated into modern data-driven pricing systems, and highlights open research and policy challenges for the actuarial profession.