We estimate that only 15% of countries worldwide are currently represented by mainstream vendor catastrophe flood models. The likes of the UK, mainland Europe and North America have multiple flood models available from various model vendors, providing the market with a range of views on flood risk.
But what about the other 85%?
The territories mentioned above are unfortunately the exception, not the norm. Many regions are less well represented, with a large proportion left unmodelled. Although deterministic approaches are used in some regions to help with risk quantification, they are often not as effective as catastrophe models for pricing business.
With growing global portfolios and the lack of flood models, especially in high-risk regions, insured flood losses remain high. In July 2018, Japan's main island of Honshu experienced severe flooding which resulted in USD $2.4 billion of insured loss (Munich Re, 2019). Similarly, Townsville, Australia, experienced widespread flooding in 2019 which resulted in insured losses of over A$ 1 billion (USD $740 million) (Evans, 2019). And, of course, USD $16 billion of insured loss occurred in Thailand in 2011 (Munich Re, 2013a). These three countries are all under-represented by flood model availability.
Furthermore, correlations of flood losses between countries may be impeded by the lack of consistent model coverage - flooding doesn’t stop at geopolitical borders, as was evident in the 2013 Central European floods which affected multiple countries and caused some USD $3.9 billion in insured losses (Munich Re, 2013b). Without a consistent model available for every country worldwide, re/insurers are potentially exposed to large correlated losses across their portfolios that they may be unaware of.
Filling the catastrophe model coverage gap
In last month’s blog in this series, we introduced our Global Flood Model powered by JBA's revolutionary modelling technology, which combines our market-leading Global Flood Map and Global Flood Event Set to generate probabilistic loss results for 99.98% of the world’s land mass.
The Global Flood Model not only fills gaps left by current catastrophe models, but for the first time enables consistent comparison of loss across national and continental borders.
Loss results from an example global portfolio using the Global Flood Model. Dark blue is highest in loss. Total Insured Value per country is represented by GDP and the number of locations is weighted by population.
You can see the Global Flood Model in action in the map above. A global aggregate portfolio was run to generate loss results at a speed of around a million risks per hour, so not only can it generate losses on a larger scale than ever before, it can also do it quickly. For most of the top 10 countries exposed to flood in this portfolio, no catastrophe model previously existed, so we asked the question, 'Which country is most exposed to flood?'
Top 10 countries by AAL/GDP for example portfolio
When looking at AAL/GDP, the answer for this portfolio is Cambodia. Among the top 10 are countries like Bangladesh, Myanmar, Vietnam, Egypt and the Philippines.
Top 10 countries by AAL for example portfolio
If we plot the same results as absolute AALs, the results look very different, as expected, with GDP playing a greater role. China’s at the top, followed by the US, Germany, India and Japan.
As demonstrated in these simple examples, our Global Flood Model allows you to calculate your risk to flood anywhere in the world. In the next blog in our series, we’ll explore how the Global Flood Model works in more depth and how we generate these losses.
For more information on our model or to request a test and evaluation, get in touch.
References
Evans, S. 2019. Townsville, Australia flood re/insurance losses hit A$ 1.04bn. Artemis. [online] 20 March 2019. Available at: https://www.artemis.bm/news/townsville-australia-flood-re-insurance-losses-hit-a1-04bn/ [Accessed 16 October 2019]
Munich Re. 2013a. Media Information: Floods and typhoons are the biggest weather risks in Eastern Asia. [online] Available at: https://www.munichre.com/en/media-relations/publications/press-releases/2013/2013-11-11-press-release/index.html [Accessed 8 October 2019]
Munich Re. 2013b. Press Release: Floods dominate natural catastrophe statistics in first half of 2013. [online] Available at: https://www.munichre.com/en/media-relations/publications/press-releases/2013/2013-07-09-press-release/index.html [Accessed 9 October 2019]
Munich Re. 2019. Media Information: Extreme storms, wildfires and droughts cause heavy nat cat losses in 2018. [online] Available at: https://www.munichre.com/en/media-relations/publications/press-releases/2019/2019-01-08-press-release/index.html [Accessed 8 October 2019]
