Sectoral Brief: Addressing multi-hazard risk in the finance sector - learnings from the MYRIAD-EU project. MYRIAD-EU Sectoral Brief Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.5281/zenodo.17787017
Multi-hazards are a known risk with the finance sector – flooding from hurricanes, and the impact of that flooding, is explicitly modelled by the catastrophe models; flooding associated with European windstorms is accounted for when calculating the losses from windstorms; and hazards after an earthquake are also included, e.g. landslide, fire. These multi-hazards are also considered static when considering the impact of climate change, even though projections show an increase in the amount of moisture and thus potential flooding. As insurance losses continue to increase (Aon 2025 Climate and Catastrophe Insights Report) there is a need to consider the interaction between all perils, globally, both modelled and unmodelled. Recent insurance losses have highlighted the importance of multi-hazard considerations, e.g. European windstorm Eunice (2022), US Hurricane Helene (2024), and US Hurricane Milton (2024), which all saw significant flood contributions. The results from MYRIAD-EU have shown there’s a lot more complexity that needs to be considered when looking to model, and quantify the impact from, multi-hazards and multi-risks. The work within MYRIAD-EU has not only highlighted the dependencies between the perils (multi-hazards) it has also provided a methodology and toolkit to explore these multi-hazards. There is an increasing need for the finance sector to be able to quantify the impact of multi-hazards and multi-risks. Previously it has been difficult to define these impacts; the work from MYRIAD-EU will significantly help this process due to the methodology and toolkits. Recommendations: • There is a need to model multi-hazards in catastrophe models, explicitly identifying the relative contribution of each hazard to the total risk, as seen for flooding from hurricanes, but for other perils, e.g. European windstorms and flooding. • Vulnerability is often assumed to be static within the catastrophe models, especially for extreme weather events. Results from MYRIAD-EU have shown the time dependence of vulnerability that should be included in catastrophe models. • Multi-hazards and multi-risks will cross multiple insurance lines: property, business interruption and life. By improving our understanding of multi-risks, it may be possible to identify solutions to reduce the cross-line impact. • The spatial and temporal elements of multirisks need to be considered when reviewing risk diversification. • Catastrophe models should allow for a clearer distinction of the impacts from multi-hazards.
Related Topics
- Type
- article
- Landing Page
- https://doi.org/10.5281/zenodo.17787017
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7108361831
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7108361831Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17787017Digital Object Identifier
- Title
-
Sectoral Brief: Addressing multi-hazard risk in the finance sector - learnings from the MYRIAD-EU project. MYRIAD-EU Sectoral BriefWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
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2025-12-02Full publication date if available
- Authors
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Champion, Adrian, Madappatt, NiyasList of authors in order
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https://doi.org/10.5281/zenodo.17787017Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5281/zenodo.17787017Direct OA link when available
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Flooding (psychology), Work (physics), Flood myth, Flood insurance, Hazard, Actuarial science, Economics, Finance, Climate change, Reinsurance, Risk management, Business, Hazard analysis, Risk assessment, Process (computing), Risk analysis (engineering), Natural hazard, Natural disaster, Environmental science, Business interruption insurance, Environmental resource management, Economic impact analysisTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.process | 229 |
| abstract_inverted_index.results | 139 |
| abstract_inverted_index.spatial | 343 |
| abstract_inverted_index.toolkit | 187 |
| abstract_inverted_index.weather | 290 |
| abstract_inverted_index.European | 29, 119, 271 |
| abstract_inverted_index.Insights | 90 |
| abstract_inverted_index.business | 319 |
| abstract_inverted_index.consider | 97 |
| abstract_inverted_index.continue | 82 |
| abstract_inverted_index.elements | 346 |
| abstract_inverted_index.flooding | 10, 26, 263 |
| abstract_inverted_index.identify | 334 |
| abstract_inverted_index.impacts; | 220 |
| abstract_inverted_index.included | 305 |
| abstract_inverted_index.increase | 69, 84 |
| abstract_inverted_index.modelled | 21, 105 |
| abstract_inverted_index.moisture | 74 |
| abstract_inverted_index.multiple | 315 |
| abstract_inverted_index.possible | 332 |
| abstract_inverted_index.provided | 183 |
| abstract_inverted_index.quantify | 159, 205 |
| abstract_inverted_index.relative | 251 |
| abstract_inverted_index.temporal | 345 |
| abstract_inverted_index.Hurricane | 124, 129 |
| abstract_inverted_index.MYRIAD-EU | 141, 169, 224, 294 |
| abstract_inverted_index.accounted | 32 |
| abstract_inverted_index.difficult | 216 |
| abstract_inverted_index.flooding, | 18 |
| abstract_inverted_index.flooding. | 78, 274 |
| abstract_inverted_index.globally, | 103 |
| abstract_inverted_index.improving | 324 |
| abstract_inverted_index.included, | 47 |
| abstract_inverted_index.insurance | 80, 109, 316 |
| abstract_inverted_index.potential | 77 |
| abstract_inverted_index.property, | 318 |
| abstract_inverted_index.reviewing | 354 |
| abstract_inverted_index.solutions | 335 |
| abstract_inverted_index.there’s | 144 |
| abstract_inverted_index.toolkits. | 235 |
| abstract_inverted_index.windstorm | 120 |
| abstract_inverted_index.Previously | 212 |
| abstract_inverted_index.associated | 27 |
| abstract_inverted_index.complexity | 148 |
| abstract_inverted_index.considered | 55, 153, 352 |
| abstract_inverted_index.cross-line | 339 |
| abstract_inverted_index.dependence | 299 |
| abstract_inverted_index.earthquake | 44 |
| abstract_inverted_index.especially | 287 |
| abstract_inverted_index.explicitly | 20, 248 |
| abstract_inverted_index.importance | 114 |
| abstract_inverted_index.increasing | 195 |
| abstract_inverted_index.landslide, | 49 |
| abstract_inverted_index.multirisks | 348 |
| abstract_inverted_index.windstorms | 30, 272 |
| abstract_inverted_index.Catastrophe | 89, 358 |
| abstract_inverted_index.calculating | 35 |
| abstract_inverted_index.catastrophe | 24, 246, 285, 307 |
| abstract_inverted_index.considering | 58 |
| abstract_inverted_index.distinction | 365 |
| abstract_inverted_index.highlighted | 112, 173 |
| abstract_inverted_index.hurricanes, | 12, 265 |
| abstract_inverted_index.identifying | 249 |
| abstract_inverted_index.interaction | 99 |
| abstract_inverted_index.methodology | 185, 233 |
| abstract_inverted_index.multi-risks | 312 |
| abstract_inverted_index.projections | 66 |
| abstract_inverted_index.significant | 135 |
| abstract_inverted_index.unmodelled. | 107 |
| abstract_inverted_index.windstorms; | 39 |
| abstract_inverted_index.contribution | 252 |
| abstract_inverted_index.dependencies | 175 |
| abstract_inverted_index.interruption | 320 |
| abstract_inverted_index.multi-hazard | 116 |
| abstract_inverted_index.multi-risks, | 328 |
| abstract_inverted_index.multi-risks. | 165, 211 |
| abstract_inverted_index.Multi-hazards | 0, 310 |
| abstract_inverted_index.Vulnerability | 276 |
| abstract_inverted_index.multi-hazards | 52, 163, 209, 244 |
| abstract_inverted_index.significantly | 226 |
| abstract_inverted_index.understanding | 326 |
| abstract_inverted_index.vulnerability | 301 |
| abstract_inverted_index.contributions. | 137 |
| abstract_inverted_index.multi-hazards. | 191, 370 |
| abstract_inverted_index.(multi-hazards) | 179 |
| abstract_inverted_index.considerations, | 117 |
| abstract_inverted_index.Recommendations: | 236 |
| abstract_inverted_index.diversification. | 356 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.79602741 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |