Research

Research statement

My research focuses on developing quantitative methods for problems in actuarial science, insurance, and finance, particularly in settings where uncertainty, risk, affordability, and reliable decision-making are central. I am broadly interested in statistical modeling, representation learning, uncertainty quantification, and modern machine learning as tools for understanding and managing complex risks. My current work includes applications of neural network approaches to actuarial ratemaking and loss reserving, analysis of unstructured data, and numerical optimization of operational decisions under uncertainty.

Research themes

1. Representation learning for actuarial science

I study how actuarial models can better account for uncertainty when decisions depend on more than point prediction. This includes work on statistically grounded machine learning, probabilistic modeling, and uncertainty-aware representations.

2. Insurance and risk analytics

A major application area of my work is insurance and actuarial science. I develop models for loss prediction, risk segmentation, ratemaking, reserving, and portfolio-level decision-making, often using modern machine learning tools together with actuarial structure.

3. Decision-focused actuarial modeling

My research also examines how predictive models interact with practical decisions in finance, insurance, and related areas. The emphasis is not only on model fit, but on decision usefulness, robustness, and real-world constraints.

Publications

Working Papers

• Lee, G. Y.  and Weaver, J. (Working paper, 2026), A probabilistic representation-learning framework with embedding uncertainty.

• Lee, G. Y. and Li, J. (Working paper, 2026), County-linked frequency–severity modeling with event-informed hyper-parameters

2025 - 2026

• Lee, G. Y.  and Okine, N. (Forthcoming on Variance, 2026), Loss reserving with textual description of claims (funded by an individual grant from the CAS).

• Lee, G. Y. (2025) Long-tail modeling of crop insurance indemnities, Variance.

2022 - 2024

• Lee, G. Y. and Jeong, H. (2024) Nonparametric intercept regularization for insurance claim frequency regression models, The Annals of Actuarial Science.

• Lee, G. Y. (2023) Multivariate insurance portfolio risk retention using the method of multipliers, North American Actuarial Journal.

• Shi, P. and Lee, G. Y. (2022), Copula regression for compound distributions with endogenous covariates with applications in insurance deductible pricing, Journal of the American Statistical Association. (R and C++ Code, and Data)

2021

• Lee, G. Y. (2021), Regression shrinkage and selection for actuarial models, Variance.

• Manski, S. and Yang, K. and Lee, G. Y. and Maiti, T. (2021), Loss amount prediction from textual data using a double GLM with shrinkage and selection, European Actuarial Journal. (R and C++ Code, and Data)

• Manski, S. and Yang, K. and Lee, G. Y. and Maiti, T. (2021), Extracting information from textual descriptions for actuarial applications, The Annals of Actuarial Science.

• Gavagan, J. and Hu, L. and Lee, G. Y. and Liu, H. and Weixel, A. (2021), Optimal reinsurance with model uncertainty and Stackelberg game, Scandinavian Actuarial Journal.

2019 - 2020

• Lee, G. Y. and Manski, S. and Maiti, T. (2020), Actuarial applications of word embedding models, ASTIN Bulletin.

• Lee, G. Y. and Shi, P. (2019), A dependent frequency–severity approach to modeling longitudinal insurance claims, Insurance: Mathematics and Economics.

• Wang, K. and Ding, J. and Lidwell, K. R. and Manski, S. and Lee, G. Y. and Esposito, E. X.  (2019), Treatment level and store level analyses of healthcare data, Risks.

2016 - 2018

• Lee, G. Y. (2017), General insurance deductible ratemaking, North American Actuarial Journal.

• Frees, E. W. and Lee, G. Y. (2016), Rating endorsements using generalized linear models, Variance (Archive: old articles will be organized here soon).

• Frees, E. W. and Lee, G. Y. and Yang, L. (2016), Multivariate frequency-severity regression models in insurance, Risks.

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