Munich Re’s
Climate Financial Impact Edition

Climate Financial Impact Edition is an intuitive to use modular SaaS solution, transforming data into clear structures for individual and portfolio risk assessment.

Building on one of the world’s most comprehensive databases for natural disasters as well as hazard modelling under different climate scenarios, this edition provides detailed information on the physical risk exposure and expected financial impacts of climate change (Climate Expected Loss) for all locations – worldwide. You not only have access to Munich Re’s extensive data collection, but can also incorporate your own data. Integrated into your digital workflows, Climate Financial Impact Edition supports your long-term investment decisions and portfolio steering. Use Climate Financial Impact Edition and benefit from Munich Re’s claims experience when quantifying the potential financial impact of physical risks on your portfolio.

Climate Expected Loss (CEL), also known as average annual loss, is the expected loss per year due to physical damage to buildings and their contents resulting from specific natural hazard events.

The Climate Expected Loss models of the Climate Financial Impact Edition of the Location Risk Intelligence Platform are currently available for the perils of tropical cyclone and river flood, with more perils to follow, for both current and future climatic conditions. The future expected loss reflects the projected altering of the probability and severity of natural hazard events for the different perils at location level, and is available for projection periods 2030, 2050 and 2100, under RCPs (Representative Concentration Pathways) 4.5 and 8.5. With the expected loss being a key metric for risk management, the CEL makes it possible to quantify increased structural losses and repair costs associated with climate change.

Methodology:

Climate Expected Loss is calculated by combining the expected intensity of a hazard (e.g. flood inundation for river flood or peak wind speeds for tropical cyclones) for each return period within the relevant grid cell, with asset vulnerability curves. These vulnerability curves relate the severity of a hazard event to the expected caused damage, which is expressed as a damage ratio, i.e. the ratio of the cost of repairing the damage to the total cost of replacing the building and its contents entirely (if it is totally destroyed). The vulnerability curves depict the estimated damage to buildings in different parts of the world on the basis of differences in building type, materials and local building codes. This yields the location-specific exceedance probabilities (EP). Climate Expected Loss is the expected value of the modelled loss distribution, which can be seen as the area under the exceedance probability curve.

Sometimes more is simply more. Especially when it comes to well-designed functionalities that facilitate risk assessment on the one hand and reduce your workload on the other. These outstanding functionalities include single location assessment, location geocoding, map services, portfolio assessment, saving of locations, elevation profiles, traffic light visualisation, location set filtering, REST API and areas and lines scoring.