NHO Sundwall - presentation Natural Catastrophes Dorte Birkebæk, Swiss Re Corporate Solutions, Country Manager Nordics, 11 and 12 of November 2014
Table of Contents / Agenda 40 Years of Loss History Various other NatCat events Why do we need Nat Cat Models? Future Considerations in NatCat modeling Relevance for Nordics Trends CatNet Sigma explorer 2
40 Years of Loss History 3
Cat related losses 2013 and before
Number of events 1970-2012 300 250 200 150 100 50 0 1970 1975 1980 1985 1990 Man-made disasters 1995 2000 2005 2010 Natural catastrophes Source: Swiss Re, sigma No 2/2013, Figure 1 5
2004: Indian Ocean earthquake and tsunami 1991: Cyclone Gorky, Bangladesh 1976: Tangshan earthquake, China 1.000.000 1970: Bangladesh 10.000.000 2008: Cyclone Nargis, Myanmar 2010: Haiti earthquake Number of victims 1970-2013 100.000 10.000 1.000 1970 1975 1980 1985 Man-made disasters 1990 1995 2000 Natural catastrophes 2005 2010 Source: Swiss Re, Sigma 6
USD bn Insured catastrophe losses, 1980 2013 140 120 100 80 60 40 20 0 Man-made disasters w/o terror Nat Cat 10 per. Mov. Avg. (Man-made disasters w/o terror) 10 per. Mov. Avg. (Nat Cat) Source: Swiss Re, Sigma 7
Various other NatCat events 8
Recent Nat Cat Events Heavy rainfall caused by clashing cold air with warm and humid air from the south triggered severe flooding in northern Colorado. Roads, bridges, local oil and gas industries as well as thousands of homes. Colorado Floods - 2013 Warm and wet air from the Gulf was clashing with cold air from a jet stream dip. In Moore (Oklahoma), a tornado outbreak of the highest category caused the loss of 28 lives and insured claims of USD 1.8 billion Moore Tornado 2013
Recent Nat Cat Events USD 7 bn. EF -5 mile wide tornado was on the ground for 22 miles. Total tornado events, including Joplin, between April and May 2011 were approximately USD 14bn. Joplin Tornado - 2011 USD 8 bn. 8.8 magnitude EQ triggers Tsunami and over 200 aftershocks. Strict building codes saved lives but secondary loss agents - > Tsunami and BI for certain industries triggered high property losses. Chile Earthquake 2010
Recent Nat Cat Events Up to USD 1bn flood caused by heavy rain. Country music Hall of Fame Grand Ole Opry House flooded. Nashville Floods 2010 USD $21bn, US, Caribbean, Gulf of Mexico, including offshore damages. Ike was a strong Cat 2 storm when it made landfall near Galveston, Texas. Hurricane Ike - 2008
Copenhagen cloudburst 2 July 2011 Train station platform Number of claims > 85'000 Insured loss ~DKK 6,2bn Average loss ~ DKK 72'900
Nat Cat losses on the rise and: Massive gap between economic & insured losses 2005: Katrina, Rita, Wilma Natural catastrophe losses 1980-2011*, in USD billion 2005: Ivan, Frances, Charley 1992: Andrew Note: Loss amounts indexed to 2010 2011 Chile, New Zealand, Japan 2008: Ike, Gustav 1999: Lothar Source: Swiss Re sigma catastrophe database 13
World Wide Five Largest Natural Catastrophes in 2011
Unprecedentedly high flood losses in 2011
Learning from past floods Mostly Residential risks affected Public sector also with considerable losses Flooding is a significant contributor to the overall loss burden and can occur basically everywhere not just next to a major river or coast Most (re-) insurers do not have access to appropriate risk management tools Simple local flood defenses / protection measures are highly effective
Why do we need Nat Cat Models? 17
Impact of the different perils Insured Loss 6% 11% Economic Loss 13% 8% 35% 31% 70% 26% Storms Number of Catastrophes 20% Flood 35% 20% Number of Fatalities Earthquake Others 10% 35% 50% 25% 5% 18
Preliminary Estimates for H1 2013 Date Country Insured Loss, billion USD, at 2013 prices Jul Nov 2011 Thailand 15.32 Jun 2013 Germany, Czech Repubic et al. 4.10 Aug 2002 Germany, Czech Repubic et al. 2.95 Jun 2007 United Kingdom 2.75 Aug 2005 Switzerland 2.50 Jan 2011 Australia 2.30 Jul Aug 1997 Poland & Czech Republic 2.29 Jul 2007 United Kingdom 2.20 Dec 2010 Australia 2.16 Jun 2013 Canada 2.00
Nat Cat Models 20
Basic Concept of NatCat Modelling Also considering asset dependency spatial and temporal correlation Deductibles Covers Shares.
Tsunami the largely unmodeled peril Significant damage to infrastructure, railroads and utilities The effects of the Tohoku Tsunami (in places over 37m) and resulting damage are far worse than those due to ground shaking in coastal zones. Far reaching effects of Tohoku Tsunami, e.g. damage/loss in California Each of the five M 9.0 events since 1900 also created a damaging Tsunami (plus the M8.8 2010 Chile EQ) => Tsunami is a key loss driver in areas such as Japan, Chile, Peru, Pacific Northwest.
Relevance for Nordics 23
2011 Nat Cat insured losses in the Nordics Cloudburst Copenhagen (02.07.2011) DKK 6.2bn Winter storm Yoda/Berit (27.11.2011) NOR (NOK275m), SWE (SEK500m) Winter storm Friedhelm (08.12.2011) US$420m (UK loss) Winter storm Dagmar / Tapani (25.-26.12.2011) US$ 370m Norway (NOK 1.2bn) Sweden (SEK 350m) Finland ( 96m) Estonia (SEK 6.4m) Source: Insurance Industry reported losses, Press reports, SR estimates.
The effects of climate change: Storm damage in Europe on the rise Expected increase in annual loss Source: Swiss Re, http://media.swissre.com/documents/storm_damage_in_europe_on_the_rise_en.pdf
The effects of climate change: Increase in coastal flood damage in Northern Europe Expected increase in annual loss Source: Swiss Re, http://media.swissre.com/documents/the_effects_of_climate_change_an_increase_in_coastal_flood_damage_in_northern_europe.pdf
The effects of climate change: Increase in coastal flood damage in Northern Europe Increase in annual expected loss Source: Swiss Re, http://media.swissre.com/documents/the_effects_of_climate_change_an_increase_in_coastal_flood_damage_in_northern_europe.pdf
Case study: Copenhagen Cloudburst, July 2011 Istedgade, Copenhagen
Copenhagen cloudburst Status for the 2 July 2011 cloudburst is a DKK 6,2bn in market loss and 85,000 individual claims The most expensive Nat Cat in Europe in 2011 Most expensive disaster in Denmark since winter storm Anatol 3 Dec 1999 The market estimate for the 14 Aug 2010 cloudburst is DKK 1,3bn
Trends 30
Insurance is key to reducing the risk and uncertainties of climate change Raising awareness and promoting risk management Protecting economies against the financial cost of climate related disasters Delivering new innovative products and solutions Unlocking investments in renewable energy and low carbon economy
Challenges for Insurers/Clients Policy Conditions Product costing Operational... Emergency management Fast response... reduced recovery period and therefore claims cost Well developed and tested response plans Supplier relationships well established with expert firms, e.g. loss adjusters Suppliers ability to support how are suppliers protection? Cross training of claims staff to increase capacity R&D
R&D in practice: Understanding flood hot spots globally Insured loss from 2011 Thailand floods was higher than expected. To minimise future flood surprises, we identified countries with similar economic characteristics to Thailand high exposure accumulations significant exposure to industries heavily embedded in the international supply chain Top flood hot spots are in High Growth Markets with China as number 1
Future Considerations Be aware about potential surface flood risk EVERYWHERE: Work out emergency plans to mitigate live toll and losses Investigate options for protection measures Global events can cause severe supply chain issues Thailand Flood came as a huge surprise Think about additional hotspots and "bottlenecks" Sea level rise and its impact on: Raising investment for protection measures Higher loss potentials especially for Storm Surge exposed cities / risk Global warming and its effects on: Wind intensity and frequency Precipitation patterns and cloud burst scenarios
What To Do About Business Interruption Going Forward? Awareness and proper allocation of BI sums insured Carefully consider bottlenecks Differentiate BI-sensitive industry segments: Just-in-time production (no storage) Difficult to replace equipment Dependence on off-site utility Parallel- or Serial Production? Potential for prolonged downtime etc. Examples of high BI-sensitive industries: Semiconductor, Car, Pulp and Paper
Corporate Solutions CatNet 36
What is CatNet? Swiss Re's online natural hazard information and mapping system Professional overview and assessment of natural hazard exposure worldwide Preparation of local, regional and cross-regional risk profiles
Corporate Solutions Sigma explorer http://www.sigma-explorer.com/
Corporate Solutions Thank you
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