insurance analysts

Abstract: In this paper, we propose a model-based boosting approach for fitting structured additive distributional copula regression models with bivariate responses, focusing particularly on the joint modeling of claim frequency and claim severity. The proposed approach is based on generalized additive models for location, scale, and shape (GAMLSS), which extend the general idea of GAMs by allowing multiple parameters of a response distribution, rather than only its conditional mean, to depend on covariates. The model is therefore intrinsically interpretable at the level of its additive predictors, providing greater transparency and auditability than black-box alternatives. We evaluate the proposed framework using synthetic and real-world data. We demonstrate its ability to recover the underlying covariate effects and dependence structure, identify informative variables, and achieve competitive predictive performance relative to benchmark models. We also illustrate how the fitted model can be interpreted.

Abstract: Global building and settlement datasets increasingly support population mapping, exposure assessment, urban monitoring, and other analyses of the built environment, yet comparative evidence remains fragmented across products, geographic regions, reference datasets, spatial scales, and evaluation methods. We benchmark seven global or near-global products, including Overture Maps, Global Building Atlas, 3D-GloBFP, Google Open Buildings 2.5D Temporal (OBT), Microsoft TEMPO, GHSL, and WSF Tracker, against harmonized reference footprints across 135 study areas. The evaluation combines complementary measures of detection, geometric agreement, and aggregate quantity accuracy, together with stratified analyses of settlement characteristics and diagnostic experiments on error size and temporal alignment. Overture achieved the highest median city-level vector F1 (0.786). Raster rankings were resolution-dependent: OBT achieved the highest median F1 at 10m (0.642), whereas WSF Tracker led at 100m (0.862). However, WSF Tracker substantially overestimated built-up area, emphasizing that when using raster products, it is important for the user to understand whether the raster identifies only buildings or includes additional impervious surfaces. Raster accuracy increased consistently with building density (Spearman \r{ho} = 0.58-0.75), while small candidate buildings were disproportionately associated with false positives in the vector products. Temporally aligning WSF Tracker with reference imagery increased mean F1 by 0.060 (median +0.037), indicating that the reported accuracies are conservative in rapidly growing areas. The study establishes a reproducible benchmark for comparing heterogeneous global urban and settlement layer datasets across geographic and settlement contexts.

Abstract: Traditional catastrophe (CAT) risk models rely on costly manual construction to generate extreme weather scenarios, an approach largely unchanged since the 1990s. As climate extremes intensify, this creates mounting challenges to the entire risk transfer chain. This study proposes the TAISE framework, which repurposes AI weather forecasting models to produce coherent extreme weather sequences at a fraction of traditional costs. Through self-iterative generation, the framework produces continuous global atmospheric fields from which extreme events emerge. A proof-of-concept experiment demonstrates an order-of-magnitude reduction in computational cost compared with conventional methods, while capturing temporal continuity and cross-regional correlations absent in snapshot-based approaches. These findings suggest a pathway toward democratising catastrophe risk quantification and enabling dynamic, comprehensive portfolio assessment for insurers, reinsurers, ILS fund managers and public-sector risk managers.