Presentation on emerging risk during FERMA Forum 2015

1. Emerging Risks
LIVING AND
WORKING IN A
RISKIER WORLD
PROFESSION – INNOVATION – DIVERSITY

2. Anticipation
Services
Anticipation
Foresight
Forecast
Data based (trends) – good for short (1-2 y) or
very long (100 y) temporal windows – blind to
novelties and surprises – past oriented
Analytic exploration of possible futures
(scenarios) – includes both weak signals and
black swans – future oriented
Complexity-based – reframes mental models –
visionary – action-oriented – present oriented

3. Anticipation & Complexity
A wider-than-usual conceptual and informative framework is needed
The capacity to use complexity for decision-making has been vanishingly poor
More important than the capacity to understand individual global risks is the
capacity to understand the linkages across them
Complex problems cannot be solved once and for ever
Psychological data are an important source of information
Although data analysis is indispensable it is definitely insufficient for facing future
challenges
Analytic exploration of possible futures is too poor
What is needed is a framework anchored in both forecast and foresight, able to
manage social and psychological complexity – the anticipation approach
Anticipation Services

4. Emerging Risks
LIVING AND
WORKING IN A
RISKIER WORLD
PROFESSION – INNOVATION – DIVERSITY

5. FERMA EMERGING RISKS WORKSHOP
6 October 2015
1. Pandora’s Box: Lloyd’s City Risk Index 2015-2025
2. Cyber Threat: Exploring an Emerging Risk
Prof Daniel Ralph
Cambridge Centre for Risk Studies

6. FERMA EMERGING RISKS WORKSHOP
6 October 2015
Part 1. Lloyd’s City Risk Index 2015-2025

7. What is Lloyd’s City Risk Index?
 300 Cities
 18 Threats
 First ever attempt to price a ten year
•7
“All Threats” (excluding liabilities)
Insurance Premium for Cities

8. GDP@Risk metrics for a city – a risk profile

9. City totals: Top 20 cities
•9

10. Risk as a % of GDP: Top 20 cities
•10
0% 1% 2% 3% 4% 5% 6%
Manila
Rosario
Taipei
Xiamen
Kabul
Port_au_Prince
Kathmandu
Santo Domingo
Ningbo
Hangzhou
Dongguan, Guangdong
Quito
Tehran
Managua
Guatemala City
Calcutta
Damascus
Hanoi
Sana'a
Beirut
Tangshan
Kunming
Busan
Yerevan
% GDP at Risk

11. Comparison with History
•11
Historical Data Direct Costs of Natural Catastrophe
• Observed Past 100 years
• Worldwide
• Repair costs to physical infrastructure
EM-DAT Database of CRED
3,806 events since 1900
0 400 800 1,200
Earthquake
Volcano
Storm
Flood
Drought
Extreme temperature
Billions
Last 10 Years
Last 20 Years
Last 50 Years
Last 100
0 100 200 300 400 500 600 700
1.1 Earthquake
1.2 Volcano
1.3 Wind Storm
1.5 Flood
1.8 Drought
1.10 Freeze + Heatwave
Expected Loss of Economic Output
• Modelled over next 10 years
• 300 cities
• Economic output
US$ Billions US$ Billions
Tropical Windstorms
Temperate
Windstorms
Freeze
Heat

12. Doing more and better: “Project Pandora”
 More … annual update
 Cities
 Threat types
 Detail
 Calibration, Comparison and BackTesting
 Crowdsourcing
 Interactions and connections
 Cascades
 Use cases:
 Emerging Risk Indices
 Underwriting
 Corporate Risk Management
•12

13. FERMA EMERGING RISKS WORKSHOP
6 October 2015
Part 2. Cyber Threat: Exploring an Emerging Risk
Prof Daniel Ralph
Cambridge Centre for Risk Studies

14. Cyber Threat research at
Cambridge Centre for Risk Studies
•14
1. “Sybil Logic Bomb” scenario: 2013-2014
2. Lloyd’s Business Blackout scenario, “Erebos”,
US cyber disaster, 2014- July 2015
3. UK cyber-based power blackout, 2015-Jan 2016
4. Modelling of direct Insurance Losses to Cyber
Events, 2015-2016-…
… the chronology of an investigation of the
unknown

15. •15
Sybil Logic Bomb
SITEs and the Cyber Economy
Sybil
Auto
Aerospace
Energy
Consumer
Biotech
Technology

16. Sybil Logic Bomb
Catastronomics: GDP damage
•16
Scenario S1 S2 X1
China-Japan War 17 27 34
9 month conflict 2 year conflict 5 year conflict
Sao-Paulo Virus 7 10 23
43% infection Poor response Poor response +
Vaccine failure
Millennial Uprising 4 * *
Europe & US
Only
Europe, US
+ BRICS
Europe, US,
BRICS + ME
Sybil Logic Bomb 4.5 7.4 15
Standard scenario More damage +
liability
Longer latency
period
2007-2012 Great Financial Crisis 18
Great Financial Crisis at 2014 20
US$ Trillion 5 Year GDP@Risk

17. Erebos Initial Outage - Day 1
•17

18. Erebos End of Day 3
•18

19. Erebos Day 14
•19

20. Erebos Macroeconomic Impacts
 Outage disrupts the economic output of key region of US
 Power loss affects exports, labour supply, consumption,
and consumer confidence: Direct loss $60 Bn
 Shock cascades across the broader economy and effects
continue for nearly three years
– Also affects economies of other countries that trade with US
– Oxford Economics Macroeconomic Model of US Economy
 GDP@Risk: US GDP reduces by: $243 Bn
– Lost output resulting from incapacity of economic activity
– 0.5% of US economy over 3 years
•20

21. Erebos Insurance Loss Estimate
Power Generation Companies $ millions
Property Damage (Generators) 633
Business Interruption (Generator Damage) 3,817
Incident Response Costs 3
Fines - FERC/NERC 4
Other liabilities -
Defendant Companies
Liability 2,253
Companies that Lose Power
Perishable Contents 595
Contingent Business Interruption - Suppliers Extension 6,769
Liability 3,120
Companies Indirectly Affected
Contingent Business Interruption - Critical Vendor 2,928
Liability 749
Homeowners
Household Contents 465
Specialty
Event Cancellation 63
Total $ 21,398
•21
For variant S1
3
1
2
4
$20 Bn
Insured Loss

22. Exploring emerging risk is a journey
 Exploring an emerging risk or an unknown risk
 Scenarios as Stress Tests
 Macroeconomic impacts and consequences
Direct losses
GDP@Risk
 Global  Regional  Sectoral effects
 Insurance loss aggregation modelling
Legal and technical: Peril definition e.g. ‘Cyber’ as
identifiable or proximate cause; Attribution; Wordings
and exclusions
•22
Quantifying “Pandora’s Box” is the
journey we are on

24. Erebos
Wide Range of Insurance Lines Impacted
•26
Class Line of Business Class Line of Business Class Line of Business
Property Aerospace Life & Health
Personal Lines/Homeowner 0 Airline 2 Life Insurance 0
Personal Contents 2 Airport 3 Health Insurance 2
Commercial Combined 5 Aviation Products 1 Income Protection 2
Construction & Engineering 1 General Aviation 1 Death & Disability 0
Commercial Facultative 4 Space 0 Hospital Cover -3
Binding Authorities 0 Energy Pension and Annuities
Casualty Downstream 5 Standard Annuities 0
Workers Compensation 1 Energy Liability 5 Variable Annuities 0
Directors & Officers 3 Onshore Energy & Power 0 Enhanced Annuities 0
Financial Lines 3 Upstream 0 Life Settlements 0
General Liability 4 Specialty War & Political Risk
Healthcare Liability 0 Accident & Health 1 Kidnap & Ransom 0
Professional Lines 1 Aquaculture insurance 0 Political Risk 2
Professional Liability 2 Contingency - film & event 4 Political Violence & Terrorism 1
Auto Equine insurance 2 Product Recall 3
Personal Lines -1 Excess & Surplus 1 Trade Credit 4
Commercial & Fleet -2 Life Insurance 0
Marine & Specie Livestock 1 Major decrease in claims -5
Cargo 0 Agriculture -4
Marine Hull 0 Multi-peril crop 0 -3
Marine Liability 1 Crop hail 0 -2
Specie 1 Livestock 0 -1
Forestry 0 No change in claims 0
Agriculture 2 1
2
3
4
Major increase in claims 5

25. Thanks for your support !