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


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


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


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)


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


Impact Forecasting model suite


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

http://nomad3.ncep.noaa.gov/ncep_data/



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)


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


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


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)


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


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


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 underestimate 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]

mailto:[email protected]




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