地理定位的天气和建筑相关数据对洪水风险保险评估的贡献
Contributions of geolocated weather and building related data for insurance assessment of flood risks
AI总结：
本研究利用高分辨率地理定位数据，提升洪水风险评估模型的准确性，为保险公司提供改进洪水风险定价的实用框架。
摘要：
洪水是造成损失最严重的自然灾害之一，2013至2023年间导致超过1000亿美元的保险损失。在法国，自然灾害赔偿计划持续亏损，凸显了准确评估建筑物层面洪水风险的必要性。保险公司通常依赖频率-严重性模型，辅以灾害地图和区域气候指标。然而，先前研究显示，此类大尺度变量只能解释个别洪水损失变化的有限部分。本研究评估了多种地理定位数据层对建模洪水索赔发生和严重程度在大型法国家庭保险组合中的边际贡献。从基于标准承保信息的基准模型开始，我们依次引入气候专家变量、极端降雨指标和精细尺度地理定位的建筑和环境属性。分析聚焦于一个实际场景，即保险公司由于预算、许可或运营限制无法部署完整的水文学或水力灾害模型。结果显示，基于降雨的指标，特别是新构建的衡量强烈局部降水的指标，显著提高了索赔建模性能。建筑和环境变量进一步增强了发生预测。总体而言，研究结果展示了高分辨率地理定位数据如何改善暴露和脆弱性评估，补充官方洪水地图，并为保险公司提供了一个操作框架，以改进洪水风险评估和定价。
英文摘要：
Floods rank among the costliest natural hazards, causing over USD 100 billion in insured losses between 2013 and 2023. In France, persistent deficits in the natural catastrophe scheme highlight the need for accurate, building-scale flood risk assessment. Insurers typically rely on frequency-severity models supported by hazard maps and regional climate indicators. However, previous studies show that such large-scale variables explain only a limited share of the variability in individual flood losses. This study evaluates the marginal contribution of multiple georeferenced data layers to modeling flood claim occurrence and severity in a large French home insurance portfolio. Starting from a baseline model based on standard underwriting information, we sequentially introduce climate-expert variables, extreme rainfall indicators, and fine-scale geolocated building and environmental attributes. The analysis focuses on a practical setting in which insurers cannot deploy full hydrological or hydraulic catastrophe models because of budgetary, licensing, or operational constraints. Results show that rainfall-based indicators, particularly a newly constructed metric capturing intense local precipitation, substantially improve claim modeling performance. Building and environmental variables further enhance occurrence prediction. Overall, the findings demonstrate how high-resolution geolocated data improve exposure and vulnerability assessment, complement official flood maps, and provide insurers with an operational framework for refining flood risk evaluation and pricing.
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。
