Data analysis and individual policy-level modeling for insurance involves handling large data sets with strong spatiotemporal correlations, non-Gaussian distributions, and complex hierarchical structures. In this research, we demonstrate that by utilizing gradient-based Markov chain Monte Carlo (MCMC) techniques accelerated by graphics processing units, the trade-off between complex model structures and scalability for inference is overcome at the million-record size. By implementing our model in NumPyro, we leverage its built-in MCMC capabilities to fit a model with multiple sophisticated components such as latent conditional autoregression and spline-based exposure adjustment, achieving an 8.8x speedup compared to CPU-based implementations. We apply this model to a case study of 2.6 million individual policy-level claim count records for automobile insurance from Brazil in 2011. We illustrate how this modeling approach significantly advances current risk assessment processes for numerous, closely related outcomes. The code and data are available at https://github.com/ckrapu/bayes-at-scale.

翻译：保险领域的数据分析与个体保单级建模需要处理具有强时空相关性、非高斯分布及复杂层级结构的大规模数据集。本研究证明，通过利用图形处理器加速的基于梯度的马尔可夫链蒙特卡洛（MCMC）技术，可在百万级记录规模下克服复杂模型结构与推断可扩展性之间的权衡。通过在NumPyro中实现模型，我们利用其内置MCMC能力拟合包含潜条件自回归和样条基暴露调整等多重复杂组件的模型，相较于基于中央处理器的实现实现了8.8倍加速。我们将该模型应用于2011年巴西260万条汽车保险个体保单级索赔计数记录案例研究，揭示了该建模方法如何显著推进当前对大量高度相关结果的风险评估流程。代码与数据详见https://github.com/ckrapu/bayes-at-scale。