Leveraging Weather Dynamics in Insurance Claims Triage Using Deep Learning

<b>In property insurance claims triage, insurers often use static information to assess the severity of a claim and to identify the subsequent actions. We hypothesize that the pattern of weather conditions throughout the course of a loss event is predictive of the insured losses, and hence appropriate use of weather dynamics improves the operation of insurers’ claim management. To test this hypothesis, we propose a deep learning method to incorporate dynamic weather information in the predictive modeling of the insured losses for reported claims. The proposed method features a hierarchical network architecture to address the challenges in claims triage due to the nature of weather dynamics. In the empirical analysis, we examine a portfolio of hail damage property insurance claims obtained from a major U.S. insurance carrier. When supplemented by dynamic weather information, the deep learning method exhibits substantial improvement in the hold-out predictive performance. We further design a cost-conscious decision strategy for triaging claims using the probabilistic forecasts of the insurance claim amounts. We show that leveraging weather dynamics in claims triage leads to a reduction of up to 9% and 6% in operational costs compared to when the triaging decision is based on forecasts without any weather information and with only static weather information, respectively. Supplementary materials for this article are available online.</b>

在财产保险理赔分诊（property insurance claims triage）场景中，保险公司通常仅依托静态信息评估索赔严重程度并确定后续操作流程。我们提出假设：灾害事件全过程中的天气状况模式对投保损失具有预测性，因此合理利用天气动态变化可优化保险公司的理赔管理运营。为验证该假设，我们提出一种深度学习（deep learning）方法，将动态天气信息融入已报案索赔的投保损失预测建模工作中。该方法采用分层网络架构（hierarchical network architecture），以应对天气动态特性给理赔分诊带来的各类挑战。在实证分析环节，我们使用从美国一家大型保险运营商获取的冰雹灾害财产保险索赔数据集开展研究。相较于仅使用静态天气信息的预测模型，融入动态天气信息的深度学习方法在预留样本预测性能上实现了显著提升。我们进一步基于保险索赔金额的概率预测结果，设计了兼顾成本效益的理赔分诊决策策略。研究表明，与仅基于无天气信息的预测、仅基于静态天气信息的预测所开展的分诊决策相比，在理赔分诊中利用天气动态信息可分别降低最高达9%和6%的运营成本。本文的补充材料可在线获取。