Colloquium - TSZ Chai (Samson) Fung - October 16, 2025

Speaker: TSZ Chai (Samson) Fung, GSU
Date/Time: Thursday, October 16, 2025, 10:00 AM - 11:00 AM ET

Title: Statistical Learning of Trade Credit Insurance Network Data with Applications to Ratemaking and Reserving

Abstract:Trade credit insurance (TCI) is a specialized line of property and casualty insurance, protecting businesses against financial losses due to buyer's insolvency. Predictive modeling for TCI claims poses formidable challenges due to the data’s complexity, yet remains underexplored in the literature. Leveraging six years of detailed TCI data from an Asian TCI insurer, we develop a bivariate, network-augmented Generalized Linear Mixed Model (GLMM) to jointly model claim probability and reporting time gaps. Our model integrates extended-order degree centrality and random effects at the business and policy levels, adjusted for data incompleteness, to capture claim histories, reporting time gaps, and network relationships specific to TCI data. To implement a feasible workaround for the high-dimensional integrations required by individual random effects, we propose a scalable Stochastic Expectation-Maximization (SEM) algorithm. Data analysis using this TCI dataset demonstrates that our model significantly outperforms benchmark models in both model fit and predictive accuracy, highlighting the effectiveness of our approach for improved ratemaking and reserving in TCI.

Bio: Tsz Chai (Samson) Fung, Ph.D., FSA is a tenure-track Assistant Professor of Actuarial Science in Maurice R. Greenberg School of Risk Science at Georgia State University. He is experienced in constructing predictive models and developing statistical inference methods for non-life insurance data, with a particular focus on actuarial applications such as ratemaking, reserving, risk management, and optimal reinsurance. He also has intensive experience in solving emerging actuarial challenges with a wide variety of real insurance datasets, including climate risk insurance data and trade credit insurance data with social network components, among others. His extensive publications span a wide range of top-tier academic journals in actuarial science (e.g., NAAJ, IME, SAJ, and ASTIN), risk management and insurance (e.g., Journal of Risk and Insurance), operational research (e.g., European Journal of Operational Research), statistics (e.g., Journal of the Royal Statistical Society), and machine learning (e.g., Neurocomputing).

Website: https://robinson.gsu.edu/profile/tsz-chai-fung/