Better data making solutions more scalable

Kurt Cripps, partner and head of global parametric speciality, Augment Risk

Cripps focuses on delivering client tailored parametric reinsurance products designed to bring a surgical approach to managing CAT exposures. 

Previously he was head of innovation and solutions at Aon. Before taking on this role, he was global head of weather insurance at Aon, where he innovated products tailored to address the unique risks associated with weather fluctuations.  

What first drew you to the parametric insurance sector?

It was apparent in 2011 that climate change was a megatrend that would dramatically impact buying patterns. Many traditional policies end up in litigation, so the transparency and simplicity (conceptually) and speed of payment made it a cleaner product to purchase. The rise in data and analytics has enabled far superior modelling to mitigate basis risk and garner the associated benefits to buying index-based solutions.

It was abundantly clear to me that weather derivatives worked seamlessly for the energy market and this would evolve into other sectors of insurance, it being the obvious one, given the amount of natural peril risk it addresses.

(Parametrics’) transparency and simplicity (conceptually) and speed of payment made it a cleaner product to purchase.

What key challenges do you face when educating clients about parametric insurance?

The top five are:

• Resistance to change or trying something new
• Ascertaining how it fits into a buying structure
• Budgets not being allocated to parametric solutions.
• Education of the intermediaries
• Lack of consistency of product offering from carriers (modelled vs actual)

The steps to addressing them are:

• Independent data and the inclusion of a settlement agent
• Robust modelling (empirical/stochastic) that is reflective of underlying index
• Syndication and access to capital
• Highlighting the broader capital benefits of buying (multi class/fungibility) and articulating the product offering clearly and aligning to underlying risk at hand
• Index-based solutions are supplementary and a tool in the toll kit to mitigate risk

What are the most promising use cases for parametric insurance today?

Wind and quake-based solutions for captives, insurers and ILS funds, as well as emerging risks such as SCS and wildfire.

How is climate volatility influencing the design, deployment or pricing of parametric solutions?

Most independent data has a good empirical data set so, combined with stochastic, you can have a robust view of risk, be it mid or long term.

What does the next generation of parametric insurance look like?

Embedded as part of the buying cycle and included in the traditional coverage as it is broader in coverage and can extend to NDBI.

What partnership, tech innovation or ecosystem shift are you most excited about?

The insurance landscape is evolving rapidly as secondary perils such as flood, wildfire and severe convective storm become a larger driver of insured losses. At the same time, the expansion of commercial observation satellites and alternative data sources is transforming the way these risks can be measured, providing more frequent, higher-resolution and independently verified data that supports more robust parametric indices.

Advances in AI and geospatial analytics are extracting increasingly valuable insights from these datasets, improving trigger accuracy and helping to reduce basis risk. Alongside this, the growth of specialist insurtechs is accelerating innovation by developing sophisticated peril-specific indices and data platforms.

The combination of better data, better analytics and stronger technology partnerships is expanding the range of risks that can be covered parametrically and making solutions more scalable for clients.

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