Mark Lynch - Importance of Big Data and Analytics for the Insurance Market
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Transcript of Mark Lynch - Importance of Big Data and Analytics for the Insurance Market
Importance of Big Data and Analytics for the Insurance MarketMark LynchImpact 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 ModellingHistorical 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 ModellingImpact 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
10Aon Benfield | Impact ForecastingProprietary and Confidential
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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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 ViolenceVariations 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 ModellingHazard 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 ModellingProbabilistic component
Section 3: Challenges for the Industry
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ChallengesData 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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ChallengesMarket 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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ChallengesPoor 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 ExpansionOverall 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 LynchImpact Forecasting+ 44(0)20 7522 [email protected]
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