CATDAT

Risklayer has access to the CATDAT including the Integrated Natural Catastrophe Database, one of the world’s largest and most detailed historical catastrophe loss databases developed by founding partner James Daniell since 2003 formally.

Layered historical information is at our fingertips and powers our various risk models. The CATDAT Database combines information from over 60,000 natural disaster events globally with 42,000 events since 1900, from information out of Online Archives, books, reports from institutions, scientific publications and other databases around the world, with over 30,000 original sources in over 90 languages.

When the 2019 Bahamas hurricane hit, 143 past impacting events were identified since 1800, including 68 of which were damaging in Abaco and Grand Bahama. Nearly all of these are tied to hurricane wind speeds and track information; and of course the historic losses. What does this mean? Well, it meant that within hours, we were able to establish that the event was going to have upwards of $2 billion damage.

But it is not just for the Bahamas and hurricanes:
The database ties to historical disaster footprints through which the hazard data ties to the historical losses all around the world for different disaster types. In addition, exposure metrics such as capital stock and population are present over time, giving the ability for detailed normalisation studies which power empirical loss curves which can be used for country, subnational entity, sector or company risk studies.

We carry out on-demand studies for multi-national companies globally to determine their future risk from their disaster history. Earthquake, volcano, flood, storm, wildfire, temperature impacts among other effects have been counted. In addition, many regional databases and smaller events have also been collected outside of this data for man-made and natural perils. As read about in over 1000 newspapers, magazines and TV broadcasts in 42 languages globally, CATDAT continues to reanalyse the past in order to examine trends for future impacts, and to feed into our rapid loss metrics pre-disaster and post-disaster.

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