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Parametric Insurance & ITS Application To Various Industries - Yavuz Ölken (2017)
- 1. PARAMETRIC INSURANCE & ITS APPLICATION TO VARIOUS INDUSTRIES Yavuz ÖLKEN 5-6 October 2017 CONFIDENTIA L
- 2. Climate change means higher volatility for economy Winter 14/15 was the coldest ever recorded in theUS/Canada Winter 15/16 was thewarmest ever recorded in France andUK Industries impacted: • Airports and Transportation • Construction Industriesimpacted: • Energy companies • Clothing companies 2 | CONFIDENTIA L
- 3. Parametric insurance is a new approach that is simple for the end customers but requires new capabilities from insurers Parametric insurance is based on the use of a parameter to create the most relevant cover that is correlated to the client’s damages or financial losses 3 |
- 4. Example of weather sensitivity per sector AGRICULTURE Decrease in yields or lower quality crop production due to drought, frost, or excessrainfall FOOD & DRINK Lower sales of fresh drinks during a cold, rainy summer CONSTRUCTION Work interruption when temperatures are too cold DISTRIBUTION Decrease in seasonalclothing sales (winter coats, bathing suits), due to adverse weather RENEWABLE ENERGY Decrease in renewable energy production due to adverse weather TOURISME & LOISIRS Baisse de fréquentation et de consommation de boissons dans les parcs TRANSPORTS Baisse de fréquentation et de consommation de boissons dans les parcs GAS & ELECTRICITY Low energy demand when temperatures are mild during the winter TOURISM & LEISURE Decrease in visitors and beverage consumptions in theme parks due to cool and rainy temperatures TRANSPORTATION Increased costs for airline companies in case of cold temperatures and snow, e.g.: aircraft de-icing 4 |
- 5. Key Facts Guiding Future Industry Towards Parametric Insurance
- 6. Construction Examples of revenue losses in the sector 2013 Cold and snow at the end of the winter/ beginning of the spring Downto -5% sales in Belgium for theyear 2013 Cold wave in the United States until March 6% decline inconcrete sales during Q1 2014 Severe winter in the US From 13% to 15% drop of sales volumes for aggregates and concrete respectively compared to 2013 Q1 2014 Cold and snow in Western Europe at the yearbeginning added to a difficult economic situation Revenue declined by 11% Leaders in Europe 2013 Poor weather conditions in the Asia Pacific region in Q1 of 2013 4 % decrease incement sales 7% decrease in aggregate sales 4% decrease inconcrete sales South American Leader Early winter start with very cold temperaturethroughout the season in Northern and Western Europe, as well as in a few Easterncountries From 28% to 46% fall of cement sales inmarkets like UK, Germany, Benelux and Poland during Q1 2010 2011 Bad weather conditions in Mexico in September -2% sales duringQ3 2013 Unfavorable weather conditions in the US at the beginning of the year 5% sales declineduring Q1 mostly because of the weather Demand for construction materials is closely related to weather events in all kinds of geographies All information extracted from annual reports
- 7. Food & Beverage Examples of revenue losses in the sector Severe adverse weather in the US and particularly wet weather in the UK -1,2% sales in theUS UK sales decreased 2014 Severe winter in Heineken’s key markets throughout the first semester Profit down to 17% inthe first half of the year 2013 Severe adverse weather in the US Sales of the beverages unit dropped by 4% in the US during Q2 2013 Harsh winter in Europe during the first semester Suntory’s first half financial report for the year 2013 : “In Europe, as the economicenvironment remains severe and the weather unfavorable, the Orangina Schweppes Group engaged in marketing activities to help grow and strengthen its major brands” 2013In Italy, the soft drinkssegment sales declined by 12.9%. Specifically, the Lemonsoda brand was negatively affected by adverse weather conditions during the seasonal sales peak. Adverse weather during seasonal peak Decrease of 10.8% in soft drink sales comparedwith 2012. The spirits sector recorded a slowdown in 2013 in some important emergingmarketsand remained weak in Europe,which was also negatively affected by adverse weather Demand for fresh beverages is closely related to weather events, especially cold and rain 2013 All information extracted from annual reports Confidential
- 8. Airlines Examples of revenue losses in the sector The severe weather in Q1 caused a drop inrevenues $55 million reductionin pre-tax revenues 17,000 flightscanceled 2014 Storms in December 45 M$ losses 2013 More North American Players “Historic severe” winter weather contributes to United’s loss inQ1 Loss of $200M in revenues dueto weatheronly 2014 Bad weather during the first quarter 10 M$ losses 2013 In the US, a total of 90 000 flight cancellations for the sector during the 2013-14 winter, representing 4% of scheduled flights 2014 First big storm of 2014 right after New Year’s Day forced it to cancel flights and caused losses in revenues and operating income 1,800 flightscanceled 45 M $ lossesin revenue 30 M $ losses in operating income 2014 Many flight cancellationsin Q1 because ofthe weather 7,500 weather-related flight cancelations Profit reduction of 50 M$ Jet Blue canceled more flights in Q1 2014 than itdid in all of 2013 4,100 flightscancelled 50 M$ losses inrevenue “It’s a negative, there’s no question about it,” comments Herb Kelleher, former CEO of Southwest Airlines, following weather- related losses in Q1 2011, “…and the first quarter of each year is the softest quarter for the airline industry. That means the impact is felt even more.” 2011 2011 Fourth quarter passenger revenuesfalls due to December storms 25 M$ losses 2010 Winter storms forced continental to suspend operations at Newark Liberty twice in February 25M$ losses inpassenger revenue Diversification of major airlines does not compensate for adverse weather conditions in their hub(s) All information extracted from annual reports Confidential
- 9. Energy Recent Example : warm winter in France causes decrease in energy consumption Warm winter in France (2013/14) causes a decrease in energy consumption: In January and February 2013/2014 in France, gas consumption decreased by 22% GDF Suez, the World’s fourth largest group in the energy sector, explains that “tens of degrees per day above seasonal averages has a 13 million to 15 million € impact on its EBITDA” Risk 1: Warm Winter A warm winter causes a decreasein heating energy consumption and thus a decrease in energy companies’revenues AXA CS can cover the energy company’s results against a warm winter Risk 2: Cold summer A cold summer causes a decrease in air conditioning consumption and thus a decrease in energy companies’revenues AXA CS can cover the energy company’s results against a cold summer Confidential
- 10. How parametric insurance works
- 11. How does Parametric Insurance work? ANALYSIS COVER Analysis of the company’s weather sensitivity What consequences do different weather anomalies (heat, rainfall, cold, drought…) on revenues or coststructure? Key steps: Long-term analysis of different indicators Confrontation of these indicators with weatherindexes Cover: design of an insurance adapted to the needs of the related industry: Key definitions: Definition of the indicator and period, e.g. number of days inferior to 15°C during the summer season Choice of triggering frequency, e.g. 20 days of deductible Choice of the amount of compensation, e.g. 1M€ per day beyond 20 days Payment Payment triggered based on certifiedweather data within a few days PAYMENT 11 | Confidential
- 12. Weather stations, satellite imagery, and many others Reliable weather stations can be used for parametric insurance contracts Rainfall Temperature If weather stations are not reliable enough (low density, no historical data..), it is possible to use satellite Between 15 and 30 years of data Resolution up to 250m. Satellites can measure weather parameters … Rainfall (Rain Fall Estimate) … and plenty of other parameters Drought index (evapotranspiration) Vegatation index (NDVI) And many others! What data do we use in our contracts ? 12 | Confidential
- 13. Overview of the developments in parametricinsurance + This trend is expected to persist… New Data Processing Methods AXA Data Innovation Lab: storage capacity, software and techniques, computation capacity More satellites E.g. Sentinel satellites: a multi-satellite project to be launched from 2014 Missions include satellite radar and super-spectral imagingfor land, ocean, and atmosphericmonitoring Timeline: 6 satellites expected to be launched between 2014and 2021 First pilots with weather stations Data that allows for construction ofindices but with a high basis risk First applications aimed at decreasing claims handlingcosts 2000 S Since 2000s, parametric insurance has boomed Mid-2000S SINCE 2010 2020 & BEYOND Use of satellite imagery More granular data and new types of data available through satellite imagery Data science Big Data revolution helps us design the most appropriate insurance products (no or reduced basis risk) “Parameterization” of the industry? We expect that in several sectors, in particular in agriculture, parametricinsurance will cannibalize traditional insuranceproducts Its application will also be widened tomore sectors 13 | Confidential
- 14. Sample cover structure for agriculture: area-yield index and rainfall index
- 15. Claims settlement Calculation of net indemnity following the publication of results¹ Claims settlement Possibility of down payment after semi- definitive results 15 | Confidential Elements to communicate tothe insurer: Planted surfaces Expected yield Insured margin per ton or price per ton Choice of reference yield (e.g.: average of the last 5 years) Desired deductibles and limit Sample “area-yield” cover structure for an agriculturaltrader 1 Figures provided by an official statisticalbody 2015Campaign Crop : Rice Surface (ha) 20 000 Yield forecast (t/ha) 4.0 Insured margin (€/t) 20 Capital insured (€) 16 000 000 Limit (%) 30% Deductible (%) 10% Official yield reference (t/ha) 4.5 June2016 settlement Crop : Rice Official yield 2015 3.9 Yield difference (%) -13.3% Indemnity (€) 528 000
- 16. Sample “rainfall index” cover structure for afarm Sample rice farm location Yunnan Province, China Source: Google Earth 16 | CONFIDENTIA L Sample cover structure Main risk Drought Index Daily rainfall Data provider Certified data provider Location e.g. farm in YunanProvince, China Risk period e.g. per crop season Trigger e.g. rainfall below 700mm during period Exit e.g. 400 mm Payout e.g. percentage of crop valueby missing mm of rain Maximum compensation e.g. 30-50% of cropyield
- 17. Sample cover structures for energy: gas and renewables
- 18. Sample cover structure for a gascompany Clients targeted: Gas providers Oil/Gas transportation companies Tonsofgas 40 20 0 Scenario: Scenario: Scenario: Scenario: Scenario: normal +1°C +2°C +3°C +4°C winter Sales Covered by AXA Losses 60 80 Temperature is a main driver of gas consumption Projection of gas sales / November - March 120 100 Max. compen- sation 18 | ParametricInsurance Confidential Sample cover structure Main risk Hot winter temperatures lead to a decrease ofgas sales Index Average temperature Location City/region Risk period e.g. November – March Trigger e.g. Averagetemperature > +1°C Exit e.g. +3°C Compensation Tobe defined contractually based on the price per kWh negotiated upfront e.g. 1M€ perdegree
- 19. 19 | Sample cover structure for a solar plant With solar radiation measurements, we can model a yearly solar power production index¹, e.g.: GWh/an 1.300 1.200 1.100 1.000 900 800 700 600 2007 2008 2009 2010 2011 2012 2013 2014 2015 Solar energy production (1 MWp) Trigger Limit Sample cover structure Main risk Lack of sun leads to decrease in energy production Index Modeled daily solar energy production Data Provider Certified data provider Location Solar panel farm Risk period e.g. full year Trigger Yearly production decrease below 10% Payout Tobe defined contractually based on the price perkWh negotiated up front Limit To be agreed upon ¹Gridded data from various sources: ERA Interim time series from the ECMWF, MERRA(NASA).
- 20. Sample cover structure for hydro powerplants Precipitation is a driver of electricity production Impact of precipitation on annual electricity production 0 5000 10000 15000 20000 25000700 600 500 400 300 200 100 0 2008 2009 2010 2011 2012 2013 2014 2015 Annual precipitations (mm ofrain) Precipitation trigger (mm) Annual electricity production (GWh) Payout would have triggered in 2012 Sample cover structure Main risk Lack of precipitation leads to decrease in water inflow and thus energy production Index Precipitation Location Area behind the dam Risk period e.g. full year Trigger e.g. Precipitation below300 mm/year Payout Tobe defined contractually based on the price perkWh negotiated up front Limit Tobe agreed 20 | CONFIDENTIA L
- 21. ¹Gridded data from various sources: ERA Interim time series from the ECMWF, MERRA (NASA). Sample cover structure for a windfarm With wind speed measurements and turbine power curve, we can model a yearly wind production index¹, e.g.: 80 Limit 70 90 130 120 110 100 Trigger GWh/year Sample cover structure Main risk Lack of wind leads to decrease in energy production Index Modeled hourly wind energy production Location Wind farm Risk period e.g. full year Trigger Yearly production decrease below 10% Payout Tobe defined contractually based on the price perkWh negotiated up front Limit Tobe agreed 21 | CONFIDENTIA L
- 22. Other sample cover structures
- 23. Sample excess wave height cover Sample cover structure Main risk Wave height above (X) m that prevents ships from harboring Index Daily Maximum Wave Height Location Buoy at the Port Shipment days Specific scheduled shipment dates (e.g. June 12th, July14th, August 17th) Critical day Day with wave height above (X) m during the shipment day Deductible n.a. Payout e.g. 20k€ in case theshipment day is a critical day Limit e.g. 60k€ in case of 3 shipment days Premium e.g. (X)€ per shipment day multiplied by number of datesof risk Port of Tema, Accra, Ghana Sample port location 23 |
- 24. 60 80 40 20 0 Scenario: Scenario: Scenario: Scenario: Scenario: normal -1°C -2°C -3°C -4°C winter Sales Covered by AXA Losses Sample cover structure for an air conditioning company – decrease in energy sales in case of a cool summer Sample cover structure Main risk Cool summer temperatures lead to a decrease in electricity sales for air conditioning Index Average temperature Location e.g. Barcelona Risk period e.g. June to August Trigger e.g. Average temperature more than 1°C below normal Exit e.g. 3°C below normal Compensation Tobe defined contractually based on the price perkWh negotiated up front e.g. 1M€ per degree Max. compen- sation Main risk: a cool summer means less revenues for the energy company Temperature is a main driver of electricity consumption for air conditioning Projection of electricity sales / June - August 120 100 24 |
- 25. Sample cover structure forhotels Poor weather in the summer (e.g. July- August) can cause hotel booking cancellations, and thus a decrease in revenues 0 10 20 30 40 50 60 70 °C 27,5 27 26,5 26 25,5 25 24,5 24 23,5 23 Impact of temperature on booking cancellations Payout would have triggered in 2011 and 2014 2010 2011 2012 2013 2014 2015 Average temperature in July/August in Barcelona (°C) Trigger (°C) Cancellation index 25 | CONFIDENTIA L Sample cover structure Main risk Cool temperatures in the summer in Spain cancause hotel booking cancellations Index Average temperature Location e.g. Barcelona Risk period e.g. July-August Trigger e.g. Averagetemperature below 25°C Payout e.g. 500,000€ perdegree Celsius below the trigger Limit e.g. 1,000,000€ Simulation 2014 Index value : 24,4°C Payout : 300,000 €
- 26. 2007 2008 2009 2010 2011 2012 2013 2014 Average temperature in Toronto / Dec-Mar (°C) Cement sales (t) Trigger (°C) Severe winter in the US in2014 From 13% to 15% drop of sales volumes foraggregates and concrete respectively compared to 2013 Q1 Sample cover structure for a cementcompany In this example, if the average temperature over the period decreases by 1°C, sales decrease by 10,000 tons. Payout would have triggered in 2013 and 2014. Frompublic sources - 50 000 100 000 150 000 200 000 t 250 000 Impact of temperature on cement sales in thewinter Temperature is a driver of cementsales in the winter 3 °C 2 1 0 -1 -2 -3 -4 -5 -6 Sample cover structure Main risk Cold winter temperatures lead to a decrease of cement sales Index Temperature Location e.g.Toronto/Ontario Risk period e.g. November - March Trigger e.g. Averagetemperature below -3°C Compensation Tobe defined contractually based on the expected marginper kt of cement e.g. 10M€ perdegree below the trigger Limit e.g. 50M€ 26 | Confidential
- 27. Sample cover structure fortransportation Snowfall events in January-February2015 in Istanbul 7 6 5 4 3 2 1 0 Snowfall(cm) Four major snow storms detectedin Istanbul in Q1 2015. ¹Gridded data from various sources: ERA Interim time series from the ECMWF, MERRA ( Sample cover structure Main risk Snowstorm which stops transportation: airport, ferry boat, bus, tramway, etc. Index Hourly/daily snowfall* Location Airport Risk period October- March Trigger Daily snowfall above 1.5 cm Compensation Tobe defined contractually based on cancellation cost and deicing costs Limit Tobe agreed NASA). From public sources Severe winter in the US in2014 Loss of $200M in revenues due to the weather inQ1 27 | Confidential
- 28. Weather stations in Turkey 30 | Confidential Enough to Built Parametric Insurance Supportedby • Weahter Station • Satellite imagery provided by NASA • Gridded data : Data calculated by meteorological models (typically temperature is estimated by a model if there is no weather station)