Use of Catastrophe Modelling Data to Help Earthquake Risk Assessment for Developers and Policy Makers, Alexandros GEORGIADIS
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6th International Disaster and Risk Conference IDRC 2016 Integrative Risk Management - Towards Resilient Cities. 28 August - 01 September 2016 in Davos, Switzerland
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Use of Catastrophe Modelling Data to Help Earthquake Risk Assessment for Developers and Policy Makers, Alexandros GEORGIADIS
- 1. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Catastrophe modelling and disaster risk assessment for developers and policy makers Alexandros Georgiadis, Aon Benfield Impact Forecasting, UK Chris Ewing, Aon Benfield Impact Forecasting, UK Stuart Fraser, GFDRR Innovation Lab, US
- 2. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Agenda • Natural Catastrophe Risk • Catastrophe Modelling in insurance • Catastrophe Modelling beyond insurance
- 3. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Agenda • Natural Catastrophe Risk • Catastrophe Modelling in insurance • Catastrophe Modelling beyond insurance
- 4. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Catastrophe Modelling • Tools that analyse the financial implications of natural catastrophes. US HU US EQ EU WS JP TY JP EQEU EQ EU FL US FL Source: Earth Institute of Columbia University
- 5. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Catastrophe Modelling • Tools that analyse the financial implications of natural catastrophes. Source: Earth Institute of Columbia University
- 6. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Agenda • Natural Catastrophe Risk • Catastrophe Modelling in insurance • Catastrophe Modelling beyond insurance
- 7. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Anatomy of a Catastrophe Model LOSS Insured loss calculation Damage Calculation VULNERABILITY HAZARD Event Generation Intensity Calculation EXPOSURE Asset Characterisation Policy Conditions Source: PERILS
- 8. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Cat Model in insurance • Tools used to calculate: – the impact (monetary loss) – the probability (frequency) for a set level of loss to occur. • View of risk • Benefits – Portfolio optimization. – Risk appetite and capital management. – Reinsurance purchase decisions. Quantify risk in old & new territories Effective accumulation control Reinsurance cover design New insurance products Selective underwriting Understand your exposure
- 9. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org The (re)insurance market Properties Insurance Reinsurance • Risk appetite and capital management • Reinsurance purchase decisions • Portfolio optimization • Portfolio optimization Catastrophe Modelling • Premium calculation Catastrophe Modelling
- 10. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Agenda • Natural Catastrophe Risk • Catastrophe Modelling in insurance • Catastrophe Modelling beyond insurance
- 11. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Challenge • Many developing countries – Limited hazard/exposure/vulnerability data and model coverage to quantify risk and make risk-informed decisions. – Lack established risk financing mechanisms – funding post-disaster activities may be unplanned and not optimal • Post-disaster costs impacting the national budget may cause: – Liquidity gaps. – Long-term government debt and increased poverty for families.
- 12. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Catastrophe Modelling beyond insurance • Expand model coverage and data availability • Impacts beyond monetary loss – i.e., government assets, infrastructure, poverty, GDP growth, population affected... • Integrated risk management using catastrophe models: Open Data Limited money / resource to fund data and modelling Quantifying Nat Cat Risk What-if scenarios & Cost/Benefit analysis Project Planning Resilience studies poverty - disaster synergy Mitigation & preparedness Risk assessment Identify risk hotspots Focus efforts to reduce risks
- 13. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Case Study: Using EQ hazard data for disaster risk reduction • GFDRR integrated Impact Forecasting EQ hazard data within their Think Hazard! global multi- hazard screening tool. • ThinkHazard.org simplifies hazard data into high, medium or low classes (based on frequency of damage occurring in that location). • Recommends first-step actions on how to reduce risk and provides links to relevant data / reports. • Helps DRR professionals to plan projects, and communities to prepare. PGA
- 14. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Contacts Alexandros Georgiadis Impact Forecasting London +44 (0)207 086 3297 alexandros.georgiadis@aonbenfield.com Chris Ewing Impact Forecasting London +44 (0)207 522 8305 chris.ewing@aonbenfield.com Stuart Fraser GFDRR Innovation Lab +44 (0)7455 048 044 sfraser@worldbank.org
- 15. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Natural catastrophe losses (1980-2015) • United States – Economic Loss: $1.65 trillion 34% – Insured Loss: $740 billion 63% • Americas – Economic Loss: $405 billion 8% – Insured Loss: $50 billion 4% • EMEA – Economic Loss: $835 billion 17% – Insured Loss: $195 billion 16% • APAC – Economic Loss: $1.96 trillion 41% – Insured Loss: $196 billion 17% Source: Aon Benfield Catastrophe Insight
Hinweis der Redaktion
- Subject of this presentation is the link between NAT CAT Modelling, risk analysis and it’s applications We will start (i) with CAT Modelling fundamentals, then (ii) look into the Insurance market and the role of CAT Modelling in Europe, US, etc, then we will look into potential uses beyond the established Insurance market.
- Portrait of a dangerous planet: The risks are colour-coded, blue for flood/storm surge, orange for drought, green & Red for EQ!
- Lack of use of catastrophe models and catastrophe risk data to help information disaster risk reduction professionals
- Catastrophe modelling is otherwise known as risk modelling outside of the (re)insurance market. Uses of risk models are multiple: The structure of a cat model can be used to assess risk outside of the insurance market. The components are the same, but perhaps don’t use the financial module (i.e., policy terms) for many analyses. International organisations such as world bank / gfdrr use risk models to identify population affected / government buildings damaged, in the same way as it can be used to assess the number of buildings affected/damaged in an insurance company’s portfolio. Model for sovereign (government) level loans and transactions such as parametric insurance triggers – see CCRIF, for example. 1: Provision of model data openly is key to improving availability - not enough money/resource in development sector to pay for data to be used once and not be made available to multiple stakeholders. Need to drive efficiency in the effort by not duplicating data, and by sharing data and collaborating to improve data / model coverage 3: Structural vulnerability curves can be swapped for curves to relate intensity with death / injury to estimate population affected. 3: understand better how poverty is linked to disasters and what the impact of disaster might be on poverty, by determining the effect of events on peoples livelihoods (needs detailed understanding of household economic situations – work in progress) 4: Understand country’s risk hot spots, and impacts of urbanization / population growth by including scenarios of urban growth and population change. Also explore risk into the future, by changing the hazard parameters / model to include the effects of warmer climate on wind, rainfall, and sea levels. This is used in cost/benefit analysis of disaster risk reduction measures, to identify whether (e.g.) new flood protection, new land-use zoning, etc, are worth the investment in terms of the amount of money saved if a disaster occurred with them in place.
- One example of collaboration between modeling in the insurance sector and development / international organizations. There is a lot of expertise and data in the private sector, which can benefit international development, if it was utilised in the right areas. See RMS work on Risky Business http://www.rms.com/blog/2014/06/24/rms-and-risky-business-modeling-climate-change-risk/ AIR work on PCRAFI – risk on Pacific Islands. Plenty of examples, where private sector, or academic institutions are contracted to run risk model (essentially, cat models), to identify and better understand risk for disaster risk management, of which reinsurance is one component.