Developing a unified and consistent database for historical natural events, and subsequent losses, within a catastrophe modeling framework

The insurance industry faces highly complex P&C challenges, among which natural catastrophe risk, also labeled as “CAT” risk. Among disasters, climatic and seismic events show large variability in size and frequency, with devastating consequences; not to mention climate change which brings added uncertainty for the future. Global insurance groups, such as AXA, must develop a sound understanding of the frequency, intensity, and impacts of natural hazard events, to protect their economic capital and ensure their solvency.

At the AXA Group Risk Management, the CAT modeling process consists in 1) collecting CAT exposure data (geographical, physical, and financial information) on a per-entity (AXA France, AXA Mexico…) and per-location basis (houses, factories, vehicles…) and 2) assessing the risk on a per-entity per-peril per-geography basis (cyclones, earthquakes, floods, hailstorms...) to finally aggregate it at Group level. This process constitutes a technical challenge through the data collection of 50 million of policies, the combination of multiple modeling solutions, and the production of millions of stochastic event losses. Alongside this process, the collection and analysis of “scenarios”, either historical events, or potential future disasters, improves the robustness and understanding of risk assessment. However, there is currently no unified and consistent database recording the characteristics of natural events (a unique identifier, their spatial and temporal extent, their intensities, and the location affected) and their actualized economic and industry impacts. This work aims at developing a database for that would first gather an exhaustive inventory of historical natural events (cyclones, storms, floods, earthquakes…) and, throughout the integration within the existing CAT modeling ecosystem, automatize model validation, back-testing, and risk analysis with respect to market and as-if losses.