The Role of Insurance and Reinsurance in Disaster Risk Management and Risk Mitigation

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1 The Role of Insurance and Reinsurance in Disaster Risk Management and Risk Mitigation David Simmons Managing Director: Capital, Science and Policy Practice

2 Introduction Catastrophe re/insurance is an established, proven product For example 2011, an annus horribilis for Asia/Pacific Catastrophe events Christchurch Earthquake(s): Economic Loss $ 18bn, Insured Loss $14bn Bangkok/Thai Flood: Economic Loss $ 30bn, Insured Loss $12bn Japanese Earthquake/Tsunami: Economic Loss $210bn, Insured Loss $35bn The global reinsurance market for catastrophe risk is strong Huge inflows of capital from pension funds (over $50bn) seeking return Established reinsurers are seeking new sources of income from markets they have little current exposure Catastrophe Modelling techniques are well established and widely understood But the benefits are more than just financial restoration Recent schemes focus upon speedy cash injection to facilitate immediate post-disaster response Caribbean Catastrophe Risk Insurance Facility and African Risk Capacity The value is far more; greater risk awareness, better risk management, planned disaster response Incentivisation to improve risk management by pricing mechanisms Improved risk understanding from the modelling/product design process Leading to better risk management and post loss contingency plans 2

3 National Catastrophe Schemes Existing Schemes Most existing schemes protect domestic property Catastrophic risks can be too large for local insurers to bear A national scheme provides basic property insurance coverage to the population Typically coverage is for residential property only Commercial property generally is covered in the open insurance market Governmental property is mostly self insured With a few exceptions the schemes are indemnity based But with limited coverage And with features to speed loss settlement Cover may be compulsory or optional Optional schemes risk adverse selection and low take up Compulsory schemes may be publically unpopular Governments may guarantee the fund In some schemes the government is explicitly reinsurer of last resort In other cases benefits reduce if the scheme exhausts its fund after a major loss 3

4 National Catastrophe Schemes Key Scheme Features characteristics Coverage in schemes is typically limited With insured able to top-up cover from local insurers Rates may be flat, appealing to concepts of national solidarity, or risk adjusted Risk adjusted best influences behaviour and is required for international schemes A few schemes give immediate post-loss funds to governments to mitigate losses / speed recovery eg Fonden in Mexico, CCRIF in the Caribbean, PCRIP in the Pacific and ARC in Africa A few schemes operate on a regional rather than national level CCRIF, PCRIP, ARC and Europa Re One scheme explicitly provides cover to local government and infrastructure exposures Fonden Almost all schemes depend upon global reinsurance markets Exceptions: CCR in France, CCS in Spain and JER in Japan All schemes buying reinsurance use reinsurance brokers bar one, PCRIP Reinsurance brokers help design, model and place the optimal reinsurance at best possible terms 4

5 National Catastrophe Schemes Regional, Structure and National, Design Sub-National Most existing schemes are national Largely covering domestic property Few, if any, covering state assets Fonden is an example of a sub-national scheme Fonden provides cover to federal agencies and state governments in Mexico Other such schemes have been proposed (eg China), there are no technical barriers at all Regional Schemes are growing CCRIF was the first in 2007 Followed by Europa Re in 2010, PCRIP in 2013, ARC in 2014, CCRIF Central America 2015 Regional schemes are harder to put in place Must be fair to all parties, no hidden cross-subsidisation Need a strong sponsor(s); eg the World Bank and CARICOM for CCRIF The modelling underpinning a regional or national scheme can support local initiatives CCRIF, directly or indirectly, has spawned a number of micro insurance initiatives Similarly modelling undertaken for, say, a cities/municipalities scheme can be the basis for a later national and/or series of local schemes or vice versa 5

6 Structure and Design Insurance Is the scheme and self-insured Reinsurance or protected? Some schemes do not buy reinsurance eg CCR in France and JER in Japan The state acting as reinsurer of last resort and/or providing loan facilities to the scheme But the vast majority of schemes however protect themselves with reinsurance Arranged by a reinsurance broker to ensure best structure at best price Reinsurers are showing great flexibility, eg offering multi-year deals to lock in price and capacity postloss There is increasing interest in use of the capital markets For example the World Bank arranged Catastrophe bond for CCRIF announced on 27 th June (replacing an early catastrophe swap arrangement) Capital market prices are becoming increasingly competitive, although for the recent African Risk Capacity placement, they could not quite compete on price 6

7 National Catastrophe Schemes A Structure potential and scheme Design: structure Example Policyholder of parametric insurance policies Management Board Representatives of Local Government, National Government, Local Insurance, Experts Risk Monitoring and Evaluation City/Municipalit y City/Municipalit y City/Municipalit y Trigger + State of emergency declaration Insurance payout Scheme built around a Catastrophe Fund Reinsurance premium Trigger payouts Reinsurance Capacity Premium City/Municipalit y Local and National Government Premium External Donors 7

8 Structure and Design Or Parametric Something vs Indemnity in Between Complexity Indemnity Scheme pays on actual loss No basis risk But high cost of loss adjustment Loss adjustment also results in payment delays Parametric An event occurs, payment is made Simple, easy to understand Event definition made by a verifiable independent agency (eg USGS) But high basis risk: smaller events may cause a large loss, a large event conversely may cause few losses Basis Risk 8

9 Structure and Design Or Parametric Something vs Indemnity in Between Complexity Indemnity Scheme pays on actual loss No basis risk But high cost of loss adjustment Loss adjustment also results in payment delays Modelled Loss Basis Model pays based on estimated loss from a catastrophe model Basis risk should be low but still real Requires time and expense to build the catastrophe model Catastrophe models are good for homogenous exposures (ie domestic property), less good for complex risks Parametric Index Essentially a simplified version of a modelled loss Formulae estimate hazard at certain reference points (eg wind speed, ground shaking, rainfall) Additional formula estimate the loss resulting from this hazard Lower basis risk than pure parametric, but higher than Modelled Loss Parametric An event occurs, payment is made Simple, easy to understand Event definition made by a verifiable independent agency (eg USGS) But high basis risk: smaller events may cause a large loss, a large event conversely may cause few losses Basis Risk 9

10 Structure and Design Parametric vs Indemnity Parametric or Indemnity (or both?) It is important to decide what is most important Speed : both speed of payment or speed of set-up Basis risk: ie accuracy of loss payment If speed of payment is important, a form of parametric cover makes sense Both CCRIF and ARC promise payment within days of an event occurring Simplified policy terms can speed up indemnity payments but the chance of delay and dispute remains If avoiding basis risk is important, indemnity or near indemnity is required Individuals cannot be exposed to basis risk, at micro level risk too high and tolerance low But a fund may consider accepting some low level basis risk as a trade against speed of settlement It is possible to migrate CCRIF started with a parametric index, allowing the scheme to be up and in place quickly After 3 years, and after more research/model building, they switched to a Modelled Loss basis It is also possible to have both Fonden scheme has a parametric catastrophe bond element to provide immediate post-disaster funding And also an indemnity based insurance to cover local governmental assets 10

11 Schools Water infrastructure Hospitals Road and Bridges Structure and Design a Example hybrid scheme of a hybrid scheme: Fonden Special Vehicle (Fonden) Deductible Deductible Deductible Deductible Trigger product Indemnification insurance Fast Payout Cost of reconstruction and repair Capable of near immediate implementation Mid-/long-term development perspective 11

12 Structure and Design What Coverage is covered? Most national schemes cover private residential property only Relatively homogenous Well modelled by most catastrophe models Relatively simple to rate Most also cover only a proportion of the value of the property Limits scale of scheme Provides some flexibility around loss settlement, to speed and simplify process Provides room for local insurers to provide top-up covers Other risks provide modelling challenges Law of large numbers doesn t apply Varied building types, building standards, occupation, stock and machinery values, business interruption Governmental assets often a particular issue; often there is no directory of risks, especially infrastructure Some schemes provide immediate disaster response Very difficult/impossible to assess accurately loss of governmental income/additional costs post disaster But relatively simple to define what level of response would be useful post an event Schemes such as CCRIF only provide a fraction of total response requirement : it s all about speed 12

13 Structure and Design Compulsory or or Voluntary Optional Many schemes are compulsory Where there is a requirement to minimise call on central governmental funds Where the scheme also wishes to encourage appropriate risk management behaviour Where there is an established insurance market this is easier For example where there is already high insurance penetration for fire, catastrophe can piggy-back It is tougher where there is little insurance currently bought Take-up is not solely driven by wealth For example, take up for the Californian Earthquake Authority scheme is low in some wealthy areas But compulsion is clearly more difficult where the population has little, if any, surplus income Compulsion can be enforced in many ways eg required to get a mortgage/property loan, required to purchase power from utility company But any compulsion is politically unpopular Without compulsion schemes can be unviable Adverse selection: only those particularly at risk buy the cover Unless premiums are risk rated, the scheme s finances may not add up 13

14 Structure and Design Price Flat Premium Setting or Risk Rated? The key decision is whether the scheme is risk rated or not Risk Rated: Premium depends on the relative assessment of risk that the policy adds to the scheme Flat Premium: All pay the same premium rate regardless of their risk level Advantages of Risk Rated No cross subsidisation, transparently fair to all policyholders (if the underlying modelling is seen to be fair) Mechanisms can be built in to encourage active risk management, eg lower premiums if roof ties fitted Advantages of Flat Premium Rate Simple Consistent with concept of national solidarity In practice hybrids are often adopted A maximum rate may be applied regardless of risk as politically unacceptable otherwise Encouragement to manage risk can still be built into the rating system Whatever is adopted, care must be taken that the scheme understands its own risk Set overall premium levels as a multiple of modelled annual expected losses Allow for costs of administration, reinsurance and ideally have some left to let scheme funds grow in good years in order to have more money to pay out in bad years. 14

15 Data and Modelling Requirement Basic Modelling Principles Process The modelling process is split as follows: 1. Hazard: The event: hurricane, earthquake, flood, drought etc 2. Attenuation: How this impacts risks at different locations ie maximum wind speed, ground shaking, water coverage, lack of rain 3. Exposure: What is there to be damaged, what are its features (eg how built, how high) 4. Vulnerability: How the property reacts to the wind speed or ground shaking or depth of water, what loss arises 5. Financial: How this translates to a claim via the terms of the insurance policy The data and modelling requirements vary with the type of policy Indemnity : 1 to 5 required Modelled Risk: 1 to 5 required Parametric Index: 1 plus simplified 2 to 5 required Parametric: 1 plus very simplified 2 to 5 required 15

16 Data and Modelling Requirement Is Data Good Requirement Data a Pre-Requisite? It is easy to believe that these schemes cannot go live without excellent data It is undoubtedly true that the better the data, the better the product Good data also makes the product more attractive to reinsurers But it is also true that data is never perfect, to wait for perfection implies a very long wait It is possible to put a scheme in place quickly with imperfect data, refining it as data (and models reflecting and using that data) become available Parametric products can provide real, immediate value without the same data requirements Data for wind and rainfall, both live and historical, are usually available from reputable sources A Parametric Index product based upon these data could be put in place immediately Attractive to reinsurers as transparent and based upon excellent, homogenous hazard data The product can be refined as data becomes available Like CCRIF, move from a Parametric Index to a Modelled Loss basis as modelling becomes available CCRIF has not moved to a full indemnity basis as it wants the fast payment that a Modelled Loss basis brings Similarly ARC will continue to use a Parametric Index basis as fast payment is fundamental to its value 16

17 Data and Modelling Requirement Example: Proposed Philippines PRISM municipality scheme example (PRISM) 1. Return period calculation per LGU* The exceedance of a defined rainfall amount or wind speed triggers a payment The payment is based on a calculated return period 2. Actual event monitoring 3. Trigger payment per LGU* Categorization of intensity Real time monitoring (meteorology) 3 hourly rainfall (daily aggregation) 6 hourly wind speed (daily max) *PRISM concept suggests payout as percentage of LGU (= local government unit) tax income class and based on state of calamity declaration 17

18 Reinsurance/Risk Mitigation General Why these Principles schemes work fior re/insurers Reinsurers (and capital markets) like Transparency Verifiable modelling Consistency of relationship/trust Reinsurers do not like Political risk Poor, incomplete or misleading modelling Uncertainty (around what they are covering) Different reinsurers have different appetites Some like writing risks with a lot of premium but relatively high risk Others like writing risks with less risk but commensurately less premium It is important to know the market and split up the reinsurance placement to appeal to all appetites But all have appetite for non-correlating exposure $1 of exposure in Florida may require 80 of capital to write as their capital is driven by hurricane risk $1 of exposure in SE Asia may require 10 of capital as reinsurers have relatively little existing exposure This should result in low technical pricing for SE Asia catastrophe business assuming risk assessment is trusted 18

19 Reinsurance Risk Appetite Example: How has reinsurance African Risk appetite Capacity changed over the last 8 years? It is also interesting to compare to the experience of trying to place the African Risk Capacity for the first time in 2014 compared to the experience of CCRIF when it launched 7 years earlier African Risk Capacity Interest from 25+ reinsurers and funds-backed markets CCRIF Interest from circa 8 reinsurers 15 quotations received 5 quotations received 12 reinsurers on contract 3 reinsurers on the contract Very keen price Very keen price ARC flew off the shelves despite a contract structure that was tough to understand, unknown modelling and a price as cheap as chips. 19

20 Africa Risk Capacity Introduction to African Risk Capacity (ARC) and ARC Ltd ARC is a sovereign risk pool designed to provide immediate financial response if there is a drought For 2014/15, the pool covered 4 countries, for 2015/18 eight but the aim is to increase this to 20+ within 4 years Cover is triggered by a parametric index developed with the World Food Programme based on staple crop rainfall requirements Rainfall is measured by a network of satellites at a 10km x 10km square resolution ARC Members as at March 2015 (Mali joined in April) 20

21 AFRICA Risk Capacity Africa Risk View sample output 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only. 21

22 Covered Countries Different growing seasons assist diversification Growing Season are spread throughout the year driving another element of diversification even for adjacent countries (eg Senegal and The Gambia) Niger Mali Senegal Burkina Faso Gambia Mauritania Kenya EAR2 Arid Kenya EAR2 Semi-Arid Malawi Zimbabwe Kenya EAR1 Arid Kenya EAR1 Semi-Arid 2015 May Jun Jul Aug Sep Oct Nov Dec 2016 Jan Feb Mar April May Jun 22

23 Africa Risk View Customisation of ARC coverage for each member country Determine Africa RiskView (ARV) parameters The basis of their parametric index, set with ARC staff Calculate Water Requirement Satisfaction Index (WRSI), based on: ARV settings and rainfall measurements from satellites using NOAA (the US Weather Agency) algorithms The staple crop currently grown, recent climatic condition and farming practice Determine a WRSI benchmark Reflect recent and expected conditions Based upon historical as-if losses and local experience Set drought thresholds as a percentage of WRSI benchmark Thresholds represent severe, medium and mild drought Estimate the proportion of the population that would be affected by severe, medium and mild droughts Based upon population surveys Set a response cost per affected person Corresponds to their budgeted contingency plans 10-day rainfall imagery at 10x10 km resolution across Africa. 1 May January 2016 comparison to normal 23

24 As-if Historical Losses: Response Costs Annual Response Costs per Country/Season (USDm) before insurance *2014 assumes no further losses The 2015 national level response cost is calculated by country/season using the vulnerability-area level data from ARV This data includes regression analysis that extends the vulnerability area level data back to 1983 A clear downward trend can be seen reflecting: Wetter conditions in most territories / seasons The unwinding of conservative forward calculation of means and median for benchmark calculation ARC Ltd pay-outs are then calculated by applying the insurance policy information 24

25 Understanding benchmark revaluation: example Niger The historical set is consistent with science and other observation The rising trend in rainfall, reflected in the WRSI benchmark, is clearly seen across Niger in the 1990s The benchmark transitions into a more stable phase since 2000 The distorting impact of the forward mean until 1993 is clearly seen Records from the Niger national meteorological services shows that the drier period extended into the 1970s The assumptions made in the historical dataset are conservative 25

26 African Risk Capacity Risk Management Benefits Countries spend up to a year working with ARC experts to understand the drought risk in their countries: ie what crops are grown, where, what rainfall requirements do they have, what defines crop failure, how many would be affected, how much immediate aid per person is required Countries are also required to draft a Disaster Response Plan Outlining how insurance recovery will be spent to maximise impact; eg buy grain on international markets, move grain from central stores, move water to feed livestock, provide alternative seed-crop to salvage growing season etc etc They must also obtain a Certificated of Good Standing To demonstrate good financial management so money s received will be put to appropriate use Post loss they must further provide a Final Implementation Plan Refining the draft Disaster Response Plan to determine a precise plan of action to maximise the societal benefit of the cash received The process of buying cover is arguably as important as the cover itself 26

27 African Risk Capacity Why it does works ARC work? Governments Access to a $100m+ pot of donor funds Assistance to develop risk understanding and develop disaster risk plans Valuable insurance cover at a low price Donors Low political risk Private/public partnership, ticks all boxes Developing risk awareness, improving disaster response Reinsurers/ Capital Markets Low political risk New risks from under-represented countries, largely uncorrelated with their existing business Transparent contract terms Everybody s objectives are satisfied! 27

28 Conclusion Re/insurance provides real benefits to developing countries Global re/insurance markets will actively support developing country catastrophe schemes We have not just financial capacity, but modelling and structuring expertise to share There are a variety of existing templates to consider But the scheme each city/regional scheme must be designed to suit their specific requirements Certain features attract both reinsurance markets and donor capital Transparency is the key Data and modelling are important but there is no need to wait for perfection Valuable schemes can be put in place immediately as long as the data they do use is well founded The scheme can grow and develop over time Like CCRIF, can move to a more technical trigger as enhanced data and modelling become available Or like Fonden, with both parametric and indemnity elements Schemes can be constructed to encourage transfer or risk management expertise ARC is a great example of the possible Reinsurers have a big appetite for developing market catastrophe risk The price must be technically credible: the higher the uncertainty, the higher the price But SE Asian risk is diversifying, capital charges loads should be low making cover affordable We open to the floor for questions and comments 28

29 The Role of Insurance and Reinsurance in Disaster Risk Management and Risk Mitigation David Simmons Managing Director: Capital, Science and Policy Practice David Simmons: Managing Director, Analytics Phone: Mobile: