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

TL;DR

This paper demonstrates that integrating high-resolution geolocated weather and building data significantly enhances flood risk assessment and claim modeling for insurers, especially when detailed hydrological models are unavailable.

Contribution

It introduces a practical framework for insurers to improve flood risk modeling using georeferenced data, beyond traditional hazard maps and climate indicators.

Findings

Rainfall indicators, especially local precipitation metrics, improve claim prediction.

Building and environmental data further enhance flood occurrence modeling.

High-resolution geolocated data complement official flood maps for better risk assessment.

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.