用神经网络预测个体理赔金额，发现案情估计比支付记录更有效。
On the use of case estimate and transactional payment data in neural networks for individual loss reserving
- 对比前馈与循环神经网络，分析支付和案情估计数据的输入方式
- 案情估计可显著提升预测精度，但记忆机制改进有限
- 提供标准化方法评估不同保险公司案情估计的价值
神经网络在精算准备金估算中的应用日益广泛。本文研究如何最优地将历史支付数据输入神经网络模型，并扩展分析案情估计（case estimates）这一时间序列数据的预测能力。比较了基于汇总支付数据的前馈神经网络与能够分析完整支付或案情估计发展历史的循环神经网络。基于SPLICE（Avanzi, Taylor and Wang, 2023）生成的多个高度复杂、可比的数据集进行训练与对比。结果表明，案情估计能显著提升预测效果，而引入记忆机制仅带来微弱改善。尽管各保险公司案情估计的质量差异较大，本文提供了标准化方法以评估其价值。
原文摘要 · Abstract (English)
The use of neural networks trained on individual claims data has become increasingly popular in the actuarial reserving literature. We consider how to best input historical payment data in neural network models. Additionally, case estimates are also available in the format of a time series, and we extend our analysis to assessing their predictive power. In this paper, we compare a feed-forward neural network trained on summarised transactions to a recurrent neural network equipped to analyse a claim's entire payment history and/or case estimate development history. We draw conclusions from training and comparing the performance of the models on multiple, comparable highly complex datasets simulated from SPLICE (Avanzi, Taylor and Wang, 2023). We find evidence that case estimates will improve predictions significantly, but that equipping the neural network with memory only leads to meagre improvements. Although the case estimation process and quality will vary significantly between insurers, we provide a standardised methodology for assessing their value.
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