Nos derniers articles

The paper presents a framework for individual claims reserving based on the projection-to-ultimate (PtU) method as an alternative to the traditional chain-ladder approach. It describes how reserving can shift from aggregate loss triangles to claim-level modeling by directly estimating ultimate claim costs. The approach is presented as compatible with classical actuarial structures while enabling the use of stochastic covariates and machine learning models, including neural networks and transformers. The authors emphasize decomposing reserves into Reported But Not Settled (RBNS) and Incurred But Not Reported (IBNR) components to maintain consistent claim cohorts. Case studies suggest that linear regression can perform robustly in individual-claim settings.

This research evaluates different regression models to predict #flood-induced #insuranceclaims, using the #us #national #floodinsurance Program (#nfip) dataset from 2000 to 2020. The models studied include #neuralnetworks (Conditional Generative Adversarial Networks), #decisiontrees (Extreme Gradient Boosting), and #kernel-based regressors (#gaussian Process). The study identifies key predictors for regression, highlighting factors that influence flood-related financial damages.