Fung, T.C., Li, Y., Peng, L. and Qian, L. (2026). “Statistical inference for systemic risk-driven portfolio selection,” Journal of Econometrics, 253, 106127.
Yoo, W., Tseung, S. and Fung, T.C. (2025). “Statistical Learning of Trade Credit Insurance Network Data with Applications to Ratemaking and Reserving,” Annals of Applied Statistics, 19(4), 2852–2877.
Asimit, V., Fung, T.C., Peng, L. and Yang, F. (2025). “Diversification Effect in Multivariate Optimal Risk Transfer,” Insurance: Mathematics and Economics, 125, 103156.
Fung, T.C. (2025). “Improving Detections of Serial Dynamics for Longitudinal Actuarial Data with Underwriting-Controlled Testing,” Insurance: Mathematics and Economics, 123, 103111.
Fung, T.C., Jeong, H. and Tzougas, T. (2025). “Investigating the effect of climate-related hazards on claim frequency prediction in motor insurance with incomplete data,” Journal of Royal Statistical Society: Series C, 74(5), 1444–1475.
Fung, T.C. and Tseung, S.C. (2025). “Mixture of experts models for multilevel data: modelling framework and approximation theory,” Neurocomputing, 626, 129357.
Aboagye, E., Asimit, V., Fung, T.C., Peng, L. and Wang, Q. (2024). “A revisit of the optimal excess-of-loss contract,” European Journal of Operational Research, 322(1), 341–354.
Fung, T.C. (2024). “Robust estimation and diagnostic of generalized linear model for insurance losses: a weighted likelihood approach,” Metrika, 88(2), 149–182.
Fung, T.C., Li, Y., Peng, L. and Qian, L. (2024). “Testing constant serial dynamics in two-step risk inference for longitudinal actuarial data,” North American Actuarial Journal 28(4), 861–881. (Supported by CKER Research Grant 2022–2023 from SOA and CAS.)
Fung, T.C., Jeong, H. and Tzougas, T. (2024). “Soft splicing model: Bridging the gap between composite model and finite mixture model,” Scandinavian Actuarial Journal 2024 (2), 168–197.
Li, Y., Fung, T.C., Peng, L. and Qian, L. (2023). “Diagnostic tests before modeling longitudinal actuarial data,” Insurance: Mathematics and Economics 113, 310–325.
Jeong, H., Tzougas, T. and Fung, T.C. (2023). “Multivariate claim count regression model with varying dispersion and dependence parameters,” Journal of Royal Statistical Society: Series A 186(1), 61–83.
Tseung, S.C., Chan, I.W., Fung, T.C., Badescu, A.L. and Lin, X.S. (2023). “Improving risk classification and ratemaking using mixture of experts models with random effects,” Journal of Risk and Insurance 90(3), 789–820.
Fung, T.C., Tzougas, G. and Wuthrich, M.V. (2023). “Mixture composite regression model with multi-type feature selection,” North American Actuarial Journal 27(2), 396–428.
Fung, T.C. (2022). “Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models,” Insurance: Mathematics and Economics 107, 180–198.
Fung, T.C., Badescu, A.L. and Lin, X.S. (2022). “Fitting censored and truncated regression data using the mixture of experts models,” North American Actuarial Journal 26 (4), 496–520. (Supported by CKER Research Grant 2020–2021 from SOA and CAS.)
Tseung, S.C., Badescu, A.L., Fung, T.C. and Lin, X.S. (2021). “LRMoE.jl: A package for insurance loss modelling using mixture of experts regression model,” Annals of Actuarial Science 15 (2), 419–440. (Supported by Canadian Institute of Actuaries Research Grant 2020–2021.)
Fung, T.C., Badescu, A.L. and Lin, X.S. (2021). “A new class of severity regression models with an application to IBNR prediction,” North American Actuarial Journal 25 (2), 206–231. (Supported by CKER Research Grant 2017–2018 from SOA and CAS.)
Fung, T.C., Badescu, A.L. and Lin, X.S. (2019). “A class of mixture of experts models for general insurance: theoretical developments,” Insurance: Mathematics and Economics 89, 111–127.
Fung, T.C., Badescu, A.L. and Lin, X.S. (2019). “A class of mixture of experts models for general insurance: application to correlated claim frequencies,” ASTIN Bulletin: The Journal of IAA 49 (3), 647–688.
Fung, T.C., Badescu, A.L. and Lin, X.S. (2019). “Multivariate Cox hidden Markov models with an application to operational risk,” Scandinavian Actuarial Journal 2019 (8), 686–710.
Chen, L., Fung, T.C., Li, Y. and Peng, L. (2024). “Fitting heavy-tailed distributions to mortality indexes for longevity risk forecasts,” Journal of Mathematical Study.
Tseung, S. C., Badescu, A.L., Fung, T. C., and Lin, X.S. (2021). “A Better Fit: To GLM or not to GLM in Insurance Ratemaking,” The Actuary. United Kingdom: Institute and Faculty of Actuaries.
Yoo, W. and Fung, T.C. (2026). “Network Auto-Logistic Regression Model for Trade Credit Insurance Longitudinal Claim Data,” North American Actuarial Journal, revise and resubmit. (Supported by CKER Research Grant 2023–2024 from SOA.)
Fung, T.C., Ma, L., Peng, L. and Yang, F. (2026). “Quantifying social inflation in liability insurance with advanced statistical methods,” working paper.
Fung, T.C., Ling, C., Luo, J., Peng, L. and Yang, F. (2026). “Optimal Reinsurance Inference for Zero-Inflated Loss Data,” submitted under review.
Chi, Y., Fung, T.C. and Peng, L. (2026). “Nonparametric Tests for Diversification Benefits of Multi-line Quota-Share Reinsurance,” submitted under review.
Chi, Y., Fung, T.C., Peng, L. and Wang, Q. (2026). “Statistical Solutions of Optimal Multivariate Reinsurance under Distortion Risk Measures,” submitted under review.
RCB Societal Impact Initiative RFP: AI for Societal Good (2026–2027)
Robinson College of Business, Georgia State University. Funding amount: US$20,400. Role: Solo-PI.
Project title: Detecting Organized Insurance Fraud Rings with Responsible AI
CAS Individual Grant (2026–2027)
Casualty Actuarial Society. Funding amount: US$15,000. Role: Solo-PI.
Project title: Micro-Level Insurance Claim Dynamic Modeling through Hidden Markov Renewal Model
SOA Research: Health Care Cost Trends RFP: Advanced Reserving Techniques (2026–2027)
Society of Actuaries. Funding amount: US$31,000+. Role: Lead-PI (Co-PI: Stephanie Lu).
Project title: Individual-Level Reserving for Healthcare Claims with ML/AI
CAS Research: Custom AI Development For Reinsurance Pricing Workflow (2025–2026)
Casualty Actuarial Society. Funding amount: US$24,000. Role: Lead-PI (Co-PI: Phoebe Choi).
Project title: Custom AI Development For Reinsurance Pricing Workflow
CAS Research: Quantify Reinsurance Costs from Social Inflation (2025–2026)
Casualty Actuarial Society. Funding amount: US$74,500 (data cost included). Role: Lead-PI (Co-PIs: Lie Ma, Liang Peng and Fang Yang).
Project title: Unraveling Social Inflation In Liability Insurance: Novel Diagnostic Approaches And Advanced Predictive Models
Committee on Knowledge Extension Research (2023–2024)
Society of Actuaries. Funding amount: US$12,000. Role: Lead-PI (Co-PIs: Spark Tseung and Woongchae Yoo).
Project title: Statistical Learning of Trade Credit Insurance Network Data with Applications to Ratemaking and Reserving
Committee on Knowledge Extension Research (2022–2023)
Society of Actuaries and Casualty Actuarial Society. Funding amount: US$12,000. Role: Lead-PI (Co-PIs: Liang Peng and Yinhuan Li).
Project title: Diagnostic tests before modeling longitudinal actuarial data
CIA Research Grant (2020–2021)
Canadian Institute of Actuaries. Funding amount: CA$20,000. Role: Co-PI (Lead PI: X. Sheldon Lin; Other Co-PIs: Spark Tseung and Andrei L. Badescu).
Project title: LRMoE: an R package for flexible actuarial loss modelling using mixture of experts regression model
Committee on Knowledge Extension Research (2020–2021)
Society of Actuaries and Casualty Actuarial Society. Funding amount: US$14,000. Role: Co-PI (Lead PI: Andrei L. Badescu. Other Co-PI: X. Sheldon Lin).
Project title: Fitting censored and truncated regression data using the mixture of experts models