This study is pleased to present a seminal study examining reserve management strategies across 45197 company-line-year observations from 2000-2012. The research pioneers line-of-business-level analysis to disentangle insurers' multidimensional incentives—tax optimization versus solvency management—through structural reserve adjustments. Employing two-way clustered fixed-effects models and regulatory policy shocks, the study establishes causal evidence of strategic reserve allocation patterns previously undocumented in actuarial literature.

Researchers from Nanjing University of Science and Technology have developed a novel computational method called BlastGraphNet, which uses graph neural networks to predict the distribution of blast loads on complex 3D buildings. This data-driven approach offers significant improvements in accuracy and efficiency compared to traditional methods, with potential applications in structural design, civil defense, and safety assessments. The study, published in Engineering, demonstrates the model’s ability to provide rapid and precise predictions of blast loads, supporting more effective risk management and engineering solutions.