EO FOR RE/INSURANCE
Earth Observation in Re/Insurance Business
The re/insurance industry has evolved into one where it is at the forefront in quantifying risks, especially with the increasing needs and pressure to disclose climate-related finances, Environment Social and Governance (ESG) ratings, and moving to a net zero economy. The need to understand disasters and the impact of climate change has never been greater. By Tina Thomson BSc PhD CCRA FRSPSoc CGeog
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he use of Earth Observation (EO) in the re/insurance industry, and particularly catastrophe modelling, is not new. The need for analytical tools and datasets became quickly evident in the aftermath of natural catastrophe events in the late 80s and early 90s, which hit re/insurers hard and resulted in some companies failing. It was clear that the infrequent nature of these types of perils, which are challenging to capture through statistical modeling alone due to lack of sufficient historical claims data, meant that insurers underestimated costs. Disciplines such as hydrology, atmospheric physics, seismology, Geographical Information Science (GIS), and engineering entered the industry to quantify the frequency and severity of loss from natural hazards. EO and emerging spatial data handling technologies played an important role in the analyses of different perils, and became fundamental to creating better diversified insurance portfolios. For example, accumulations of insured exposure at risk could be quickly identified and related to potential sources of natural hazard, be it a flood plain or fault line. Over time, natural catastrophe models have become increasingly sophisticated, by incorporating a set of artificial events simulated over tens of thousands of years to represent the full spectrum of possible events beyond those observed in history. Supported by increased computational power and highly detailed and quality data, it is now possible to model perils at national or even international scale, capturing correlations and peril interdependencies, such as flooding across catchments or hurricane-induced rainfall. 26 | www.geospatialworld.net | May-June 2022
Figure 1: Realistic Disaster Scenario under climate change conditions derived from NASA’s Global Precipitation Measurement (GPM) data, digital elevation models, CORINE land cover data, and hydraulic modelling to derive flood extents and depths for the town of Cannes on the French Riviera
From exposure to loss Understanding the exposures is the starting point and includes the assessment of geospatial asset and exposure data, their availability, and the quality requirements for modeling. In re/insurance, data consists typically of policyholder information, sums insured, location information and, to some degree, additional attributes describing the types of assets. The level of granularity in location information is important to increase the accuracy of modeled results with respect to the associated hazard intensity. Additional risk attributes, such as type of occupancy, year built, height, etc., are important for applying the right damage ratios between the exposure and the hazard. Exposure information can be enriched or distributed to finer granularity through a variety of sources
