Importance of Big Data and Analytics
for the Insurance Market
Mark Lynch
Impact Forecasting

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Agenda
 Political violence and the insurance industry
 Use of big data and analytics in the insurance market
 Challenges for the industry
 Conclusions

Section 1: Political Violence and the
Insurance Industry

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What constitutes political violence?
 Political violence can encompass a number of things but within catastrophe modelling
this is largely broken down into three key sectors
 Each sector has its own intrinsic difficulties in terms of modelling and analysis
 Can potentially look at each sector as a reflection of domestic support for political
violence
Terrorism and Sabotage Strikes, Riot & Civil Commotion Insurrection and Revolution

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Why does it matter to the Insurance Industry?
 Political violence drives the propensity of losses in a whole variety of Lines of
Business:
Property Business Interruption Life
Motor Workers Comp Contingency
Credit Risk Health Kidnap and Random
 The insurance market’s shift towards emerging markets in recent years has
increased market exposure to political violence
 Long term instability can have an adverse effect on the entire economy,
depressing the economic viability and the insurance market
Greater Penetration in Emerging Markets = Greater Exposure to PV Risk

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Catalyst for Political Violence Modelling
Historical Changes – Terrorism Threat
 Terrorism modelling is a relatively new field
in catastrophe modelling
 It has grown more prominent in the wake of a
number of large market losses stemming
from terrorism
 9/11 compounded this and remains the 5th
largest catastrophe loss ($22bn) and is likely
to grow...
“The huge payouts by insurance companies
contributed to a crisis in the industry, including the
near-collapse of the world's leading insurance market,
Lloyd's of London.”
(BBC, 1993)

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Catalyst for Political Violence Modelling
Impact on Resilience and wider community
 Due to increased uncertainty within the insurance market this has a knock on
effect on resilience as a whole
 Increased perception of the risk leads to an a number of factors that have a
potential effect on recovery:
– Removal of terrorism coverage from policies
– Exclusion of high risk areas
– Exclusion of CBRN coverage
– Increased price of coverage
 With a vacuum in the insurance market for terrorism coverage this dilutes the
capacity of business and

Section 2: Big Data and Analytics

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Impact Forecasting model suite

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Natural Perils: Christian - Met Office data
 1,378 stations
available, high
concentration in UK
and countries of
Western Europe
 Period: 26th Oct
18:00 to 28th Oct
09:00 GMT
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443740
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3723
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3138
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Natural Perils: Christian - Footprint specification
 0.5 * 0.5 degree footprint downloaded from
http://nomad3.ncep.noaa.gov/ncep_data/
 Updated daily, used: 26th,27th and 28th October
 Low resolution compared to the model may underestimate losses
– Increase by 5, 10 and 20% tested

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Unique nature of Political Violence
Variations within each country
 Even within territories the level of
risk can vary greatly
 Understanding this can have a
material impact on insurance
industry
 We see similar issues in
important emerging markets:
– BRIC countries
– MINT Countries
Armed Conflict Location & Event Data
Project (1997-2010)

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Multiple point simulation of the potential target
Example: building: Pentagon
Is point representation of the target good enough?
• The image shows the maximum distance of damage
• This masks a significant variation in losses that could
occur vary depending on the location of the initial
blast
• Without this models are underestimating the potential
loses
–
 Compared to other
“natural” perils,
detailed
geographical
location is critical
for modelling
terrorism risk
 Our models
encapsulate this
and highlight the
variation that can
occur in the losses
 01 Terrorism modelling
Example loss distribution for specific attack
The solution is polygon with multiple attack points
• We use a polygon system and simulate blasts on
over 200 sites for each target
• This helps to display the target uncertainty that is
inherent within each site
• From this we can simulate over 4,000 attacks for
each target within the model
–
Terrorism Modelling
Hazard component

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Truncated illustrative target and attacks type probability matrix
Synthesised IF Database of US Terrorist Attacks
 In order to
contextualise the
losses it is
important to
identify the
likelihood of each
attack
 Based on historical
data, plot analysis
and local expert
input we are able
to project the risk
of terrorist attacks
 01 Terrorism modelling
Percentage of attacks against government buildings in
US
Conventional (explosives,
vehicle-borne devices)
Non-conventional (nuclear) Non-conventional (CBR)
97.0% 1.0% 2.0%
Financial 3.0% 2.9% 0.0% 0.1%
Embassies 5.0% 4.9% 0.1% 0.1%
Government 17.0% 16.5% 0.2% 0.3%
Military 9.0% 8.7% 0.1% 0.2%
Place of worship 1.0% 1.0% 0.0% 0.0%
All other targets 65.0% 63.1% 0.7% 1.3%
Terrorism Modelling
Probabilistic component

Section 3: Challenges for the Industry

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Challenges
Data Quality
 The quality of data that we receive from
clients can vary wildly and is key to
analysis
 Blast analysis is based on extremely
fine details and variance on this effects
 Without this analytics proves to be
highly uncertain
 Data quality can be constrained by
privacy concerns and market problems
(competitiveness, lack of hierarchy)

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Challenges
Market issues hindering a more accurate understanding
 Short term historical memory: threat of political violence risk oscillates
between mass hysteria and calm based on temporal distance from an event
 Cultural issues: some brokers do not see the need for analytics in this
space due to the human element, some deny the existence of risk
 Arm chair expertise: political violence dominates the news thus people
believe they have a comprehensive understanding of the risk including the
biological impact of Cesium-137
 Poor Data: Data poverty that was previously mentioned

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Challenges
Poor central coordination
 Cooperation between the insurance sector and government is still rather
limited and the UK pool that deals with terrorism (Pool Re) plays a limited
role
 Insurance industry requires better empirical data, access to classified
documentation and central coordination
 Government could benefit greatly from knowledge on concentrations of a
lack of insurance, the details of their coverage and potential exposure to a
large scale attack
 Greater cooperation and data sharing would help the industry immensely and
bolster resilience capacity

Section 3: Challenges for the Industry

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Future Trends and Expansion
Overall Trends
 Overall the market is moving over to a more analytical framework to
investigate all catastrophe events
 The development in sophistication for political violence models has been
exponential as it is a nascent area of analytics
 Market forces are pushing the insurance sector towards a more fundamental
understanding of the risk and this can only be a positive thing
 Without a detailed understanding of the risk, insurers are likely to over- or
under- estimate the threat having knock on effects for resilience
 Greater cooperation on data sharing, standardisation and analytics
would allow the insurance industry to play a more fundamental role in
Analytics

Contact
Mark Lynch
Impact Forecasting
+ 44(0)20 7522 8255
mark.lynch@aonbenfield.com

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