Probabilistic loss reserving prediction via denoising diffusion model
提出一种改进的扩散模型，利用索赔数据的流量三角形作为图表示，预测保险损失准备金，相比传统模型提高了准确性和效率，并提供概率预测。
This paper introduces an innovative approach to predicting loss reserves in the insurance industry through a revised diffusion model. This model leverages run-off triangles of claim data as graphical representations, highlighting the interconnections among data points within the triangle. Unlike the traditional cross-classified over-dispersed Poisson (ccODP) model, our proposed diffusion model not only enhances accuracy and efficiency but also provides probabilistic forecasts. Through comprehensive simulation and empirical studies, we demonstrate the superior forecasting capabilities of our diffusion model compared to existing methods. These findings indicate that using network-based interactions within run-off triangles can significantly improve loss reserve forecasting.