Distributional refinement network: Distributional forecasting via deep learning

Abstract

A key task in actuarial modelling involves modelling the distributional properties of losses. Classic (distributional) regression approaches like Generalised Linear Models (GLMs) are commonly used, but challenges remain in developing models that can (i) allow covariates to flexibly impact different aspects of the conditional distribution, (ii) integrate developments in machine learning and AI to maximise the predictive power while considering (i), and, (iii) maintain a level of interpretability in the model to enhance trust in the model and its outputs, which is often compromised in efforts pursuing (i) and (ii). We tackle this problem by proposing a Distributional Refinement Network (DRN), ..