Distributional refinement network: Distributional forecasting via deep learning
提出分布细化网络（DRN），结合可解释的基线模型与改进的深度分布回归方法，灵活细化整个基线分布，提升预测性能并保持可解释性，适用于精算损失分布建模。
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), which combines an inherently interpretable baseline model (such as GLMs) with a flexible neural network–a modified Deep Distribution Regression (DDR) method. Specifically, our approach flexibly refines the entire baseline distribution. As a result, the DRN captures varying effects of features across all quantiles, improving predictive performance while maintaining adequate interpretability. Using both synthetic and real-world data, we demonstrate the DRN’s superior distributional forecasting capacity. The DRN has the potential to be a powerful distributional regression model in actuarial science and beyond.