The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these...

The lasso is applied in an attempt to automate the loss reserving problem. The regression form contained within the lasso is a GLM, and so that the model has all the versatility of that type of model, but the model selection is automated and the parameter coefficients for selected terms will not...

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development.This is best understood against the context of the evolution of these...