A new report published by the Lloyd’s of London insurance and reinsurance market concludes that by underpinning liability catastrophe models with big data techniques, opportunities could be found for the ILS market to get more deeply into casualty risks.
The topic of how or when the insurance-linked securities (ILS) market and its investors could get more meaningfully into casualty and liability type risks, or if it even should, is a hot one.
With the disruption that the entry of ILS and third-party capital, from institutional and capital market investors, has caused in the property catastrophe market fresh in people’s minds, thoughts are turning to where ILS investors and managers could or should focus next.
There is already a move into specialty lines from some ILS fund managers, who see peak energy, marine and aviation risks as akin to catastrophic events. At the same time there is another side to the market that looks to casualty catastrophe risks and seeks to identify how they could become part of the ILS and reinsurance-linked asset class.
The new report, which was commissioned by Lloyd’s, discusses the use of innovative, technology and science led approaches to analysing, understanding and ultimately managing liability risk accumulations.
Lloyd’s notes; “Accumulations of liability risk have the potential to send shockwaves through the insurance industry, and are one of the most complex exposure management challenges faced by insurers.”
The report features a methodology developed by liability catastrophe modelling firm Praedicat Inc, a firm established by well-known catastrophe risk modeller RMS and the RAND Corporation, in collaboration with Lloyd’s.
It uses big data analytics in an effort to improve understanding of these liability accumulation risks, which if they occurred would be true casualty catastrophe events, and to ultimately seek to find ways to reduce the potential for unexpected insurance and reinsurance market shocks.
The methodology involves mining and analysing scientific research associated with potential liability risks using big data techniques, in order to “estimate the probability of a general consensus being reached that exposure to a substance or product causes a particular form of injury.”
The report also looks at big data innovations and concludes that they have the potential to create more robust liability risk management for insurers and reinsurers. Liability catastrophe modelling using big data could provide enhanced means for re/insurers to manage liability accumulations, while also managing risk appetite and allowing them to underwrite more where it makes sense.
Specifically on the ILS and reinsurance side, the report concludes that by using these big data techniques it may be possible to develop new reinsurance products to address liability accumulations and casualty catastrophe risks, from named peril to clash products.
It could also provide the metrics and modelling necessary to enable ILS investors to really enter the casualty and liability space, or for ILS products such as liability or casualty cat bonds to be structured.
Trevor Maynard, Head of Exposure Management at Lloyd’s, commented on the report; “Rapid advancements in big data have opened up a wealth of new opportunities in the understanding of emerging risks. One area in particular in which this is creating new possibilities is around the management of liability risk.
“The approach explored in this report, developed by Praedicat, Inc., is one example of how new technologies are being used to enhance our understanding in this area. While the most effective risk transfer is expected to continue to rely on a combination of underwriting expertise and detailed analysis, emerging technologies are offering new insights that we hope will drive further innovation in the insurance industry.”
Praedicat CEO, Robert Reville, added; “Actuaries were the original data scientists applying innovative statistical methods to price and spread risks. Today, new technologies and ‘big data’ are bringing a new wave of statistical innovation to liability insurance, promising a more robust market and a greater ability to help their clients manage the most complex risks.”
As risk modelling technology improves and big data techniques, machine learning and other high performance computing technologies are applied to liability and casualty risks, the output of models should become more granular and easy to understand.
For certain risks that may result in an ability to break down exposures into data sets that could be parameterised, or at the least enable an enhanced understanding that increases investor comfort in allocating capital to such risks.
We may be some way off a full entry of ILS into casualty and liability risks, but the work undertaken by Praedicat perhaps shows that capital markets risk transfer of these risks is not outside the realm of possibility.
The full report can be accessed via the Lloyd’s website here.
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