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The large-scale and complex nature of climate change makes it difficult to assess and quantify the impact on insurance activities. Climate change is likely affecting the probability of natural hazard occurrence in terms of severity and/or frequency.Natural catastrophe risk is a function of hazard, exposure and vulnerability. As a (re)-insurer it is seen that changes in year-on-year losses are a function of all these components and not just the hazard.The present study focuses, in a first step, on assessing impacts of climate change on fluvial flood risks in Europe solely due to changes in hazard itself. A stochastic catalogue of future flood risk events is derived from Pan-European data sets of river flood probability of occurrence produced within EU FP7 RAIN project. The loss modelling framework internally developed at AXA is then used to provide a geographical view of changes in future flood risks.

Despite California being a highly seismic prone region, around 85% of people are not covered against this risk. This situation results from more than 100 years of evolution since the first earthquake insurance cover after the 1906 San Francisco earthquake. To understand this evolution, two analyses have been performed: the first one at the market level and the second one at the insured people level.At the market level, as many variables as the premium amount, the risk monitoring, the funding sources of prevention plans, the insurance company’s solvency and the attractiveness of earthquake insurance solutions, have been investigated. By cross-analysing data collected and analysing the evolution with time, three different phases have been identified in the earthquake insurance market history.At insured people level, a database is built from 18 different data sources about earthquake insurance, gathering data since 1921. Next, a new model is developed to assess the rate of homeowners insured against this risk, according to their risk awareness and the average annual insurance premium amount.These two analyses are finally used to investigate in which extent the California earthquake insurance market could reach again 40% of people insured, like in 1993 and 1996. Even if results show that a widespread belief that a devastating earthquake is imminent could bring such a situation, only a new earthquake insurance model will allow to achieve this goal in a sustainable way. In that respect, the efficiency of two current initiatives to bring more people to get an earthquake insurance: "Earthquake Brace and Bolt" and "JumpStart Recovery", is assessed at the light of the analyses performed previously in this paper.

International audience; In this study focused on France, we explore the uncertainties related to choices made while building a source model for hazard assessment and we quantify the impact on probabilistic hazard estimates. Earthquake recurrence models are initially built from the French Seismic CATalog (FCAT, Manchuel et al. in Bull Earthq Eng, 2018. https://doi.org/10.1007/s10518-017-0236-1). We set up a logic tree that includes two alternative seismogenic source models (ESHM13 and Baize et al. in Bull Soc Géol Fr 184(3):225–259, 2013), two versions of FCAT catalog, two alternative declustering algorithms, and three alternative minimum magnitudes for earthquake recurrence modeling. We calculate the hazard for six cities (i.e. Nantes, Lourdes, Clermont-Ferrand, Briançon, Nice and Strasbourg) that are located in source zones with a minimum amount of data to work with. Results are displayed for the PGA and spectral period 0.2 s, at return periods 475 and 5000 years. Exploration of the logic tree shows that the parameters with the most impact on hazard results are the minimum magnitude used in the recurrence modeling (up to 31%) and the selection of the seismogenic source model (up to 30%). We also use the SHARE European Earthquake Catalog (SHEEC, Woessner et al. in Bull Earthquake Eng, 2015. https://doi.org/10.1007/s10518-015-9795-1) to build earthquake recurrence models and compare hazard values obtained with the FCAT logic tree. Comparisons are limited because of the low number of events available in some sources in SHEEC; however, results show that, depending on the site considered, the earthquake catalog selection can also strongly impact the hazard estimates (up to 50%). The FCAT logic tree is combined with four ground-motion models (Bindi et al. in Bull Earthq Eng 12(1):391–430, 2014; Boore et al. in Earthq Spectra 30(3):1057–1085, 2014; Cauzzi et al. in Bull Earthq Eng 13(6):1587–1612, 2015. https://doi.org/10.1007/s10518-014-9685-y; Drouet and Cotton in Bull Seismol Soc Am 105(4):1883–1902, 2015) to account for the epistemic uncertainty on the prediction of ground-motion. Exploration of the logic tree shows that the contribution of ground-motion model uncertainties can be larger than, equivalent to, or lower than the contribution of the source-model uncertainties to the overall hazard variability. Which component controls overall uncertainty depends on the site, spectral period and return period. Finally, exploring the logic tree provides a distribution for the ratios between hazard levels at 5000 and 475 years return periods, revealing that the ratios only slightly depend on source-model uncertainties, vary strongly from site to site, and can take values between 3 and 5, which is significantly higher than what is commonly assumed in the engineering community.

Despite California is a highly seismic prone region, most of homeowners are not covered against this risk. This study analyses the reasons for homeowners to purchase or not an insurance to cover earthquake losses, with application in California. A dedicated database is built from 18 different data sources about earthquake insurance, gathering data since 1921. A new model is developed to assess the take-up rate based on the homeowners’ risk awareness and the average annual insurance premium amount. Results suggest that only two extreme situations would lead all owners to cover their home with insurance: (1) a widespread belief that a devastating earthquake is imminent, or alternatively, (2) a massive decrease in the average annual premium amount by a factor exceeding 6 (from $980 to $160, USD 2015). Considering the low likelihood of each situation, we conclude from this study that new insurance solutions are necessary to fill the protection gap.