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Parametric Insurance and Climate Change

 

Parametric insurance: advantages and disadvantages

As can be inferred from the above table, the main advantages of parametric insurance are:

The main disadvantages/challenges of parametric insurance are:

Case studies

It is worth stating that parametric insurance is generally not designed to replace traditional indemnity insurance products. Instead, parametric insurance is typically used to either:

  1. Complement existing traditional insurance policies – parametric insurance may be used to speed up the payment of recoveries, or to fill the protection gaps and/or exclusions of the existing indemnity policy
  2. Provide insurance where there is a lack of capacity or appetite from traditional insurance markets, especially for risks that are underinsured or uninsured

Below I consider two case studies, one from each of the broad categories described above.

  1. Parametric insurance for a holiday resort in the US

Parametric insurance is well-suited to cover the more intangible, pure financial losses that impact businesses without necessarily causing physical damage to their assets. One such example would be to protect against ‘loss of attraction’ of a holiday resort due to a hurricane approaching whereby, even if the hurricane does not end up damaging the resort, the insured can be compensated for its lower business volumes over that period.

Here the parameter used could be the actual wind speed and hurricane category reached within a specified area or, under a slightly more protective/nuanced loss of attraction agreement, the parameter could instead be based off the number and/or nature of official hurricane warnings covering the area in question, so the payout would be triggered regardless of whether the pre-warned hurricane category actually then materialised or not. The company is protecting itself against people being put off from booking and attending its resort, with indemnity insurance still primarily relied upon for physical damage and traditional business interruption claims.

2. Parametric insurance for crop farmers in India

Farming in India is higher risk than in other countries because it has low irrigation coverage and hence crop yields are highly sensitive to both the amount and timing of local rainfall. Some Indian states offer state-subsidised traditional crop insurance but many of these schemes have failed to make claim payments in a timely manner and, as such, the majority of agricultural land in India remains uninsured[iii]. With a lack of effective traditional insurance and the farmers’ livelihoods highly dependent on the level of rainfall, parametric insurance is well-suited to help close this protection gap.

The parametric insurance can be structured so that the payout – typically an agreed sum per acre – is triggered if the amount of rainfall either exceeds a defined threshold (i.e. a flood) or falls below a defined threshold (i.e. a drought) over a certain period of time. The farmers are looking for protection against extreme experience, whether that be higher or lower than an acceptable middle range. Providing farmers with this layer of security in adverse years should enable them to invest (rather than save) the money earned in benign years to improve their farming practices and hence increase their crop yields and personal income. It is also valuable from the State’s perspective, as it reduces the risk that farmers become internally displaced or dependent on food aid.

However, this form of parametric insurance is not without its challenges. It relies on the availability of reasonably priced and reliable sensors to form a sufficiently dense array of rainfall measurements, backed up by regional monitoring stations to protect against these sensors losing connectivity or becoming unreliable (either naturally or through interference). Furthermore its usefulness will be limited in areas where groundwater/irrigation are the key factors in determining crop yields, rather than rainfall. Measuring groundwater availability is far more complex and groundwater levels are much more exposed to human (i.e. subjective) decisions regarding how/where to allocate water resources – e.g. through controls on dams and irrigation systems.

Parametric insurance and climate change

Weather events are well-suited to parametric insurance because the parameters involved (e.g. wind speed, rainfall) fulfil all of the key properties described in the earlier section – there is a strong link between extreme weather and financial losses, the parameters are relatively easy to measure, there is an abundance of historical data that can be used for modelling, the weather cannot be influenced etc. The key challenge for (parametric) insurance providers within the natural catastrophe space is ensuring that appropriate adjustments are made to models that are based on past data, as these may otherwise underestimate the frequency and/or severity of future extreme weather events.

Although (as discussed above) parametric insurance is by no means confined to natural catastrophes these remain its most common application and, due to the link between climate change and extreme weather, this seems likely to continue. A recent study[iv] expects the global market for parametric insurance to grow by 10% per year over the next decade to around $30 billion of annual premium, driven by the natural catastrophe sector, and there have been numerous start-ups that have raised significant venture capital in recent years[v].

One example of a natural catastrophe parametric insurance specialist is FloodFlash, which was founded in the UK and raised $15m earlier this year to fund its international expansion[vi]. The company offers parametric flood insurance that allows prospective policyholders to set a flood depth and payout amount when obtaining their quote. Using sensors, FloodFlash gets an immediate notification when the agreed-upon flood depth has been reached and the payout is then typically sent within 48 hours.

With premium rates hardening in traditional insurance markets, the industry having experienced some bad press over exclusions/policy wordings during the Covid-19 pandemic, more businesses now appreciating the need for effective insurance protection and extreme weather events continuing to intensify, the easy to understand nature, lower cost, flexible pricing structure and rapid payouts of parametric insurance look increasingly attractive – even more so to a generation of digital natives who are comfortable relying on modern technology to both execute the financial contract and monitor the trigger parameters themselves.

Parametric insurance providers targeting parts of the market that are currently uninsured are hoping that these advantages – especially the rapid payout, which is particularly important for smaller businesses – outweigh the downsides of imperfect indemnification. Ultimately, the lower cost of parametric insurance may even make it a direct competitor for indemnity insurance if these traditional products risk becoming unviable in areas that are particularly prone to extreme weather events. In a world where climate concerns remain front and centre, parametric insurance looks set to become an increasingly relevant tool to help mitigate against the effects of a warming and less predictable climate.

Summary

Parametric insurance is an alternative form of insurance that, rather than compensating the insured for actual losses incurred, instead provides a set payout based on the occurrence of a pre-defined trigger event, as measured by some objective parameter. Typically the chosen parameter relates to a feature of a natural catastrophe (hurricane category, rainfall, earthquake magnitude etc) but the insurance can, in theory, be based on any parameter that is objective, reliable and correlated with the actual losses sustained. In time parametric insurance may expand outside of its primary natural catastrophe domain as, with the continued evolution of big data analytics, more parameters can be monitored as suitable proxies for risks whose impacts are harder to quantify.

Parametric insurance is not designed to replace traditional indemnity insurance but to complement it – for example by speeding up payments to insureds – or to offer insurance in parts of the world where there is a lack of capacity and/or appetite from traditional insurance providers. Demand for parametric insurance looks likely to grow significantly in the future as the range and impacts of climate-related extreme weather events become more severe and unpredictable, and the product becomes more mainstream. As such, parametric insurance may become an increasingly viable option for helping businesses and individuals build climate resilience and strengthen their disaster recovery.

 

Sources and Further Reading:

https://corporatesolutions.swissre.com/insights/knowledge/what_is_parametric_insurance.html

https://corporatesolutions.swissre.com/insights/knowledge/10_myths_about_parametric_insurance.html

https://www.marshmclennan.com/insights/publications/2018/dec/parametric-insurance-tool-to-increase-climate-resilience.html

https://farmingfirst.org/2021/12/parametric-insurance-an-important-tool-for-climate-change-adaptation/

https://corporatesolutions.swissre.com/insights/knowledge/what-is-the-most-popular-parametric-solution-cat-in-a-box-explained.html

https://www.insurancethoughtleadership.com/going-digital/3-trends-driving-growth-parametric-insurance

https://www.alliedmarketresearch.com/parametric-insurance-market-A14966

https://floodflash.co/parametric-insurance-trends-2021/

https://earlymetrics.com/parametric-insurance-popularisation/

https://theprint.in/opinion/has-crop-insurance-helped-indian-farmers-many-dont-get-payments-on-time/757815/

[i] For the purposes of this article I am considering parametric insurance in its pure form whereby the contracts are effectively executed as derivatives and there is no requirement for a ‘proof of loss’. For further discussion on the differences between pure parametric covers (executed as derivatives) and hybrid parametric covers (executed as insurance contracts) refer to: https://corporatesolutions.swissre.com/insights/knowledge/10_myths_about_parametric_insurance.html

[ii] Some ‘traditional insurance’ policies provide fixed benefits whereby the payout is pre-determined for certain types of loss (eg a set sum assured for loss of limb or a set sum assured for a Fine Art policy), which makes them similar to parametric insurance to some extent – as pricing for these losses is determined purely by frequency rather than frequency and severity. However the vast majority of traditional insurance is written on an indemnity basis, and where policies do include a fixed benefit feature this is still typically wrapped up with indemnity insurance for other loss types. Therefore within the article the terms ‘traditional insurance’ and ‘indemnity insurance’ are used synonymously.

[iii] https://theprint.in/opinion/has-crop-insurance-helped-indian-farmers-many-dont-get-payments-on-time/757815/

[iv] https://www.alliedmarketresearch.com/parametric-insurance-market-A14966

[v] https://earlymetrics.com/parametric-insurance-popularisation/

[vi] https://floodflash.co/parametric-insurance-trends-2021/

 

Rob Givens

July 2022

 

April 2022 Exam Results

* September 2019 IFoA wide results include only CM, CS, CB subjects, SP2, 5 & 6 and SA2

** April 2020 saw all CM and CS exams cancelled. APR staff normally perform well in these exams, causing a small dip

Looking at our results for individual exams, the APR pass rate exceeded the industry wide pass rate for every subject sat, with APR staff achieving 100% pass rates across 11 different subjects.

 

Highlights

We’d like to congratulate all staff on the results of their hard work. However, particular mention goes to:

Jack Foley

July 2022

 

APR Charity Challenge 2022

 

In the Men’s, it can be no real surprise that Djokovic comes out on top. We expect him on average to at least reach the 4th round of Wimbledon. In fact, using Djokovic’s calculated win probability, we can estimate that he has a 17% chance of winning the whole thing. The 6-time champion has been so dominant over the last few years and he is still the man to beat. Nadal follows closely in second place, indicating that we are still in the era of the tournaments being dominated by the greats.

With both of the authors of this article being based in the Edinburgh APR office, you may be surprised to see that Andy Murray misses out on the top 10. In fact, we actually have him ranked 31st. Statistics aside, we just use this as proof that we have not been swayed by any confirmation bias when creating these models!

After her long winning streak, it seems sensible that Iga Świątek has come out as our favourite to win the title in the Women’s draw. We would expect her to make it to at least the quarter-finals (further than Djokovic, perhaps a bold prediction given her past form on grass). This prediction translates to a 42% chance of her winning the tournament, which emphasises the extent of her domination just now.

The caveat that comes with the Women’s results is an absence of a prediction for Serena Williams due to her lengthy injury period. However using our “expert judgement”, as actuaries are so often asked to do, it seems unlikely she would make our top 10 with a lack of recent match practice behind her.

Lastly, in case you thought we’d forgotten, the Men’s model predicts that Richard Gasquet will win any given match with probability of 48.5% – which almost translates as an expected trip to the 2nd round for the decorated Frenchman. In fact, his estimated number of rounds is 1.94 and he has a probability of winning the tournament of 0.64% – well, you never know.

Now it’s time for us all to sit back, enjoy the Championships and sincerely hope that Djokovic and Świątek don’t make shock first-round exits – our credibility depends on it!

Addendum – written on 19th July, after Wimbledon 2022 had finished

So, how did we do? They say hindsight is a wonderful thing, but they probably haven’t tried predicting a tennis tournament. There were upsets, there were disappointments, there were shocks, and of course there were truckloads of strawberries and cream.

In the Men’s Singles, the drama began to unfold when our Number 5 pick, Berrettini, had to withdraw due to COVID-19 in the first round. At this point, we were starting to get worried. Casper Ruud (Number 4) and Stefanos Tsitsipas (Number 3) bowed out in the second and third rounds respectively, leaving us with a depleted stock from our top 10.

Fortunately, three of them – Cameron Norrie, Nadal and Djokovic all made it to the semi-finals. However, our model failed to predict the injury-forced withdrawal of Nadal at this point which left us with an unexpected Djokovic-Kyrgios final. Kyrgios, who our model predicted 20th most likely to win, had a characteristically explosive tournament, but he was no match for Djokovic. The Serb’s 7th Wimbledon title and 21st Grand Slam never really looked in doubt, and we can say we knew that all along.

It’s good that we can say that, because we certainly can’t say that we had full confidence (or much at all really) that Elena Rybakina would win the Women’s tournament, as she did. Our model had her as 18th most likely to take the title, but bear in mind that Wimbledon’s own seedings placed her in 17th before the tournament, so we weren’t far off the official list. We consoled ourselves with this fact after the heartbreak of seeing Iga Swiatek – our predicted favourite by some distance – end her 37-match winning streak in style with a huge loss to Alizé Cornet in the third round. We’re yet to face up to the “I told you so” that will be coming our way from the reviewer of this article and APR colleague, John Nicholls, so that’s something to look forward to…

Before you decide to chuck our model into whatever the equivalent of a dustbin is for mathematical models, you should know that our number 2 pick, Ons Jabeur, did actually make it to the final. She became the runner-up to Rybakina after a three-set battle that swung both ways. Our reputation just about remains intact. 

Modelling aside, it was yet another glorious instalment in the Wimbledon saga as far as the tennis was concerned. As obsessed as we were with outcomes matching our predictions, we still managed to find some moments of solace to sit back and appreciate the quality of tennis going on. Richard Gasquet even made it to the third round!

But the question on everybody’s lips is: were our models actually any good? It’s difficult to say with certainty – we only ever said who was more likely to win any given match. So we won some and we lost some. Djokovic was victorious and Swiatek bombed out. What is certain is that we will need to ruin tennis once again next year…

Ross Witney-Hunter and Josh Payne

June 2022

[1] Number of matches and wins relate to WTA250 level or above, (i.e. any wins at lower-level tournaments don’t contribute).

 

Wordle – how to maximise your chances of getting it in two

May 2022

[1] Solvency II Review: Consultation – GOV.UK (www.gov.uk)

Cyber Security: SQL Injection

April 2022

[1] This is a fact profound enough to warrant a footnote, even though it is not strictly relevant to the article. Colloquially, Gödel proved that any logical system capable of expressing arithmetic will contain some number of true statements which are unprovable. Whilst this is obviously consequential for mathematics and philosophy, it also less obviously puts constraints on the kinds of computer programs that can run in a finite number of steps.

[2] Machine code is the code which is expressed in terms that your computer can understand, and it’s not nice to read. Just as all legal programs can be translated to machine code, all legal functional programs can be translated to an extended form of the lambda calculus (which in turn can be translated to real machine code to run on your PC). Most languages, even functional ones, do not actually do this (opting to cut out the lambda calculus middleman), but it can be done in principle, and a few do take this approach. Regardless of how the computer “really” treats them, the lambda calculus is the theory that supports the grammar of such languages.

[3] This is a bit of a tautology – “computable” is typically taken to mean anything that a Turing Machine can do with infinite time and resources.

[4] Note that brackets don’t mean anything special, as they do for f(x) in standard function notation. They just indicate the precedence of function application.

[5] A word of advice for those who embark on this journey: although the pure lambda calculus does not provide arithmetic or numbers, it can be helpful to pretend they exist to test your intuition.

[6] Both examples are taken from the Haskell wiki. Rest assured, they have not deliberately made the C code look worse than it needs to be!

[7] This is one of the reasons that C is such a notoriously hard language to use – programmers can wreak all kinds of havoc with this power. But by the same token, expert C programmers are able to squeeze phenomenal performance out of their code.

[8] With a few notable exceptions, such as Java (which enforces OOP) and Haskell (which is necessarily purely functional). Some languages also just lack facilities that would make certain paradigms convenient. For example it is very difficult to do any sophisticated functional work in VBA.

[9] Excel formulas cannot save secret variables in some persistent location, nor can they cause side effects – they can just be evaluated, The Excel formula language is purely functional for this reason.

Actureads Book Club: The Transgender Issue

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Parametric insurance is a form of insurance that, whilst still relatively uncommon, is gaining greater prominence within the general insurance sector. In this article Rob Givens explains how parametric insurance differs from traditional indemnity insurance and, by way of some example case studies, highlights how it could become an increasingly important tool in dealing with the consequences of climate change.

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