基于长短期记忆网络的一般保险索赔的微观准备金
Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network
摘要：
当保险索赔数据被聚合并结构化在损失准备金的发展三角形中时，关于个人索赔的详细信息被完全忽略。为了从个人索赔特征中提取预测能力，研究人员最近提出了从这些宏观层面的方法转向微观层面的损失准备金方法。我们引入了一个离散时间个体保留框架，该框架将粒度信息引入到一种名为长短期记忆（LSTM）神经网络的深度学习方法中。在每个时间段，网络有两个任务：第一，分类是否有支付或恢复，第二，预测相应的非零金额，如果有的话。我们说明了一个模拟和一个真正的一般保险数据集的估计过程。我们比较我们的方法与链梯聚合方法使用的预测未决损失估计和它们的实际值。基于超过阈值的超额支付的广义帕累托模型，我们调整了LSTM储备预测以考虑极端支付。
英文摘要：
Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to move away from these macro-level methods in favor of micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments.
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。
