论文标题
使用长期短期存储网络的一般保险索赔保留一般保险索赔
Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network
论文作者
论文摘要
当保险索赔数据的汇总和结构三角形以保留损失时，有关个人索赔的详细信息将完全忽略。为了希望从各个主张特征中提取预测能力，研究人员最近提出要摆脱这些宏观级别的方法，以支持微观损失保留方法。我们介绍了一个离散的个人保留框架，该框架将颗粒信息纳入了一个名为长期记忆（LSTM）神经网络的深度学习方法中。在每个时间段内，网络都有两个任务：首先，分类是付款还是恢复，其次，预测相应的非零金额（如果有）。我们在模拟和真正的一般保险数据集上说明了估计程序。我们使用预测的未偿损失估计值及其实际值将我们的方法与链条聚合方法进行比较。基于超过阈值超额付款的广义帕累托模型，我们调整LSTM储备预测以说明极端付款。
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.
