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Risk in Context

Parametric Solutions for Insurance Programme Design

Posted by Steve Harry Tuesday, 18 September 2018

Insurance contracts can sometimes be slow to pay out on complex claims, and their precise coverage can be opaque. Could the use of an unambiguous, objective policy trigger and a pre-determined scale of payment solve these issues? Welcome to the world of parametrics.

What are Parametric Insurances?

Parametric insurances (parametrics) are insurance contracts where the policy trigger, limits, or the pay-out (or a combination of these), adjust based on the movements of an index (parameter), rather than the occurrence of an event or a specific loss of value.

Using an index removes uncertainty over coverage and the quantum of pay-out, which can speed up a claim and provide immediate liquidity when a company needs it most.

However, parametrics are not new, and corporates have previously found them to be expensive and difficult to structure. So why consider one now?

Advances in data and analytics now mean that all parties to a transaction – companies, brokers and insurers – are better equipped to identify, match, and price risks for which an index is a good proxy.

The emergence of new risks such as cyber and non-damage business interruption, which are hard to classify in terms of traditional insurance, also creates an opportunity for an innovative approach to policy construction.

Features of Parametrics

There are certain keys to the effectiveness of a parametric:

  • Enough data to create a reliable and credible index on which to trigger or pay the contract.

  • Sufficient correlation of the index to the risk in question.

  • The ability and willingness of an insurer to price the risk.

These involve knowledge and understanding from all involved. Risk and insurance managers need to understand the exposures facing their business, their financial impact, and where they may already have good internal data sources for use in indexing.

Brokers can use their skill in structuring and analytics to look for appropriate risks, seek out correlations with data, and to generate competition among insurers.

A growing number of insurers have the appetite and capability to model parametric programmes and the types of risk they can consider is also broadening.

Pricing of parametrics is usually transparent and scientific – although perhaps this contributes to the perception that these are expensive solutions. A risk with a modelled occurrence probability of 2% will generate a premium rate well above this number – transparent, but a high rate on line when compared to many insurable risks.

Parametrics in Programme Design?

Parametrics are currently most common in the areas of natural catastrophe and weather risk. Here there is usually a reliable data source and a straightforward correlation with loss.

Data and analytics will have a vital role to play in identifying and quantifying these correlations for everyday classes of insurance, if the insurance market is to adopt parametric-based insurance structures.

A full parametric insurance contract may be a radical step, but there are ways to use the technology without necessarily restructuring a complete programme:

  • Applying a parametric “double-trigger” – for example a retention level that adjusts based on the movement of an index - increasing in good times, reducing when the wider business climate is not favourable.

  • Applying parametrics to a single peril within a programme.  The obvious example is a natural catastrophe, but other potential perils include emerging risks such as those mentioned earlier.

The Future

It’s important not to underestimate the difficulties of arranging parametric insurances: The data quality and risk correlation issues are significant hurdles for many insurance buyers.

However, the market for parametrics is becoming more main-stream, and if a full parametric contract is not possible, innovation in small ways – perhaps in the context of retentions – may represent a way forward and a chance to test out the technology.


Related to:  Analytics

Steve Harry

Marsh Analytics


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