Recent Studies from Georgia State University Add New Data to Insurance (Statistical Learning of Trade Credit Insurance Network Data With Applications To Ratemaking and Reserving): Insurance

2026 JAN 22 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Policy and Law Daily -- Fresh data on Insurance are presented in a new report. According to news reporting originating from Atlanta, Georgia, by NewsRx correspondents, research stated, “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.”

Our news editors obtained a quote from the research from Georgia State University, “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.”

According to the news editors, the research concluded: “Supplementary Material for this article are available as an online supplement.”

This research has been peer-reviewed.

For more information on this research see: Statistical Learning of Trade Credit Insurance Network Data With Applications To Ratemaking and Reserving. Annals of Applied Statistics, 2025;19(4):2852-2877. Annals of Applied Statistics can be contacted at: Inst Mathematical Statistics-ims, 3163 Somerset Dr, Cleveland, OH 44122, USA.

The news editors report that additional information may be obtained by contacting Woongchae Yoo, Georgia State University, Maurice R Greenberg Sch Risk Sci, Atlanta, GA 30302, United States. Additional authors for this research include Tsz Chai Fung and Spark C. Tseung.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1214/25-AOAS2106. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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