Contributions of geolocated weather and building related data for insurance assessment of flood risks

2026-03-02

AI Summary

This study addresses the limitations of traditional insurance models, which rely on regional-scale hazard maps and climate indices and thus struggle to accurately assess flood risk at the individual building level, leading to biased claims predictions. Leveraging residential insurance claim data from France, we propose a practical framework that bypasses the need for complex hydrological modeling. Starting from basic underwriting information, our approach integrates novel localized heavy precipitation indicators, high-resolution geolocated building attributes, and surrounding environmental features. Through geospatial analysis and statistical modeling, the framework jointly predicts both the occurrence and severity of flood losses at the building scale under real-world operational constraints. The proposed indicators substantially improve claims modeling performance, offering insurers an actionable, high-resolution solution for flood risk pricing.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• flood risk assessment
• building-scale modeling
• insurance claims
• geolocated data
• natural hazards

Innovation

• geolocated data
• flood risk assessment
• extreme rainfall indicators
• building-scale vulnerability
• insurance modeling