基于混合神经网络的随机储备叠加模型
Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network
摘要：
目前，法律要求保险公司更加重视监测与承保和资产管理活动有关的风险。关于承保风险，保险公司必须管理的主要不确定因素是保费是否足以应付未来的申索，以及现有储备是否足以支付未决申索。由于其性质，这两种风险都使用随机模型进行校准。本文介绍了一种基于梯度提升、随机森林和人工神经网络等机器学习技术的预约模型。这些算法和其他广泛使用的储备模型叠加在一起，以预测径流的形状。为了计算之前预测的偏差，将对数正态方法与建议的模型相结合。实证结果表明，所提出的方法可以用来改善传统的准备金技术的基础上贝叶斯统计和链梯的性能，从而更准确地评估准备金风险。
英文摘要：
Currently, legal requirements demand that insurance companies increase their emphasis on monitoring the risks linked to the underwriting and asset management activities. Regarding underwriting risks, the main uncertainties that insurers must manage are related to the premium sufficiency to cover future claims and the adequacy of the current reserves to pay outstanding claims. Both risks are calibrated using stochastic models due to their nature. This paper introduces a reserving model based on a set of machine learning techniques such as Gradient Boosting, Random Forest and Artificial Neural Networks. These algorithms and other widely used reserving models are stacked to predict the shape of the runoff. To compute the deviation around a former prediction, a log-normal approach is combined with the suggested model. The empirical results demonstrate that the proposed methodology can be used to improve the performance of the traditional reserving techniques based on Bayesian statistics and a Chain Ladder, leading to a more accurate assessment of the reserving risk.
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。
