Risk Modelling
Our team of actuaries devise and create unique modelling solutions that can more accurately evaluate, quantify and price terrorism risk.

We harness our Risk Awareness teams’ intelligence and reports, and blend this with a collaboration of government, academia, and global (re)insurer data. This has both accelerated and enhanced our modelling capabilities, which allows us to provide bespoke modelling solutions to solve different aspects of the terrorism risks your business might face in the future.

Our Risk Modelling services provide

The expertise to build and deploy modelling tools and techniques that can be adopted by Members, (re) insurers and the capital markets to inform their view of terrorism risk and how to price it.

Unique insight and technological advances. With the increased confidence in terrorism modelling we help provide the foundation for private market capacity to grow.

Visualisation capability of Members’ risks. Allowing the quick estimation of loss following an event, by supplying ‘shape files’ of cordons within which exposure may be affected.

The significance of our modelling capabilities was demonstrated in 2019, when they were pivotal to the successful placement of an historic £75 million Insurance Linked Security (ILS). Complementing what was already the world’s largest terrorism retrocession placement globally, the ILS was the first bond to exclusively securitise terrorism risk, and just the second to be written out of London following the government’s efforts to drive competition with traditional ILS strongholds such as Bermuda.

Risk Modelling Case Studies

Situation: Multiple incidents in 2017, in particular the London Borough Market attack, highlighted the lack of coverage for businesses who were denied access, unable to operate or closed due to police cordons.

Solution: Having identified this gap in coverage we worked with government to change the scope of cover our Members could offer.  We built and parameterised a stochastic model that demonstrated the range of scenarios that could affect policyholders and what this could mean to individual Member insurers.  The parameterisation of the model involved interviews with counter terrorism specialists drawing on their experience of cordon characteristics.

Benefit & Result: Interested members had a starting point of how Pool Re considered pricing this business using a market representative perspective; and could leverage on this as necessary when considering their own pricing.  Pool Re remain happy to work with members to tailor such output for their individual needs.

Situation: What would happen in the event of a major loss? How could Pool Re respond, how many members could be involved? Would there be sufficient loss adjusters in the market?

Solution: Working with an independent consultancy a realistic major loss scenario was derived. Pool Re built an independent view of the damage and losses arising from that event, estimating which members would suffer claims, how many policies would be affected. This information fed directly into the Claims Team, to help identify internal and external resource requirements.

Benefit & Result: Claims Team able to better understand and quantify demands within the industry in the event of a major loss.

Situation: Everyone is aware of how the accuracy of data behind any model will affect the results from that model. London market data is well known within the industry to lack quality, as members grapple with inflexible legacy systems and the adoption of industry data schemas lacks traction. Better, more accurate data can help drive more accurate modelling and hence lower insurance cost.

Solution: Through the design of a Member Data Tools, first rolled out in 2017 and enhanced annually, the accuracy of Pool Re data has continued to improve from 67% at full postcode level to 92% in 2020. One particular tool aims to highlight errors within data submissions before they are sent to Pool Re, and frees up time spent on trivial data scrubbing errors to allow concentration on the consistency of programme representations and large risks.

Benefit & Result: Pool Re is more able to understand the composition of the risks it and its members are exposed to. This translates into better pricing by Pool Re’s retrocessionaires, due to lower loadings for data uncertainty.

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