汽车索赔频率数据的动态在线预测模型及其应用
Dynamic online prediction model and its application to automobile claim frequency data
摘要：
索赔频率的预测模型是非寿险定价和风险管理的一项重要任务，需要随着投保人口、监管立法和技术的变化而经常更新。现有的方法要么是在一个特设的方式，如参数模型校准，或较少这样的预测的目的。在本文中，我们开发了一个动态泊松状态空间（DPSS）模型，可以不断更新的参数，每当新的索赔信息变得可用。DPSS模型允许时变和时不变系数。为了说明时变系数随时间的平滑趋势，使用平滑样条来对时变系数建模。采用极大似然法客观地选择平滑参数。该模型使用在预先指定的时间间隔累积的批量数据进行更新，这允许更好地近似底层泊松密度函数。该方法也可以推广到零膨胀泊松分布和负二项分布的假设。在模拟中，我们表明，新模型具有显着更高的预测精度相比，现有的方法。我们将这种方法应用于中国六年来的真实汽车保险索赔数据集，并通过与文献中竞争模型的结果进行比较来证明其优越性。
英文摘要：
Prediction modelling of claim frequency is an important task for pricing and risk management in non-life insurance and needed to be updated frequently with the changes in the insured population, regulatory legislation and technology. Existing methods are either done in an ad hoc fashion, such as parametric model calibration, or less so for the purpose of prediction. In this paper, we develop a Dynamic Poisson state space (DPSS) model which can continuously update the parameters whenever new claim information becomes available. DPSS model allows for both time-varying and time-invariant coefficients. To account for smoothness trends of time-varying coefficients over time, smoothing splines are used to model time-varying coefficients. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at pre-specified time intervals, which allows for a better approximation of the underlying Poisson density function. The proposed method can be also extended to the distributional assumption of zero-inflated Poisson and negative binomial. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply this methodology to a real-world automobile insurance claim data set in China over a period of six years and demonstrate its superiority by comparing it with the results of competing models from the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
