The Payout is in the Parameters: A glimpse into the world of Parametric Insurance

In this article, I’m going to provide a high level overview of this product, which is gaining traction in the risk transfer world but is yet to really take off.

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As Co-Founder of Innovatrix Capital, this trend is particularly close to me as our solutions are based on these mechanics.

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Parametric risk-transfer solutions – what are we talking about?

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Let’s start in the insurance world where risk transfer is the name of the game.

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Consumers, customers, clients, policyholders – they’re all the purchaser or the “insured”. They have a risk or “exposure” to risk which they’d prefer not to hold themselves – the reasons for transferring the risk are extensive and relatively self-evident in a lot of cases.

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They seek out a risk bearer or risk carrier (often in the form of an insurer) to assume this risk. Other alternative carriers include captives or potentially investors participating in the capital markets.

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This functions (in theory), as it should be more cost effective for a risk carrier to assume the risk and spread it across their portfolio i.e. benefiting from diversification. The assumption here is that unless there is a major catastrophe, a poorly structured or badly priced risk, there should be sufficient inflow of premium (“float”) to cover losses as we don’t expect all policyholders to make claims at the same time, if at all. That leaves insurers with sufficient premium across their portfolio to cover their claims and wider costs and thereby generating a return for themselves.

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Great, the system works – not perfectly but it’s the system we have!

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Typically, this type of insurance product is what is known as an indemnity based product. The policy lays out the terms on which a customer is protected and any exclusions, deductibles et al. So far, so normal.

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But it can and does get tricky.

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Think back to the height of the Covid-19 Pandemic. Insurers were receiving business interruption claims, although (in many cases), pandemic risks were an exclusion to the policy wording. This caused a legal headache all round and once again, insurers came out not looking particularly great.

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So, what’s the alternative? Where does the Parametric product fit in?

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Parametric insurance can be thought of as a very specific and focused insurance cover. The clue is actually in the name – “parameters”.

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The nature of a claim event is based on the breaching of pre-defined and pre-determined parameters. As such, the structure and payout mechanism is driven by a model or indeed a third-party data source for confirmation of the parameter changes.

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The final point is important as using this mechanism should alleviate most Moral Hazard risk (I,e., where insurance may discourage responsible behaviour,? like someone with dental insurance neglecting oral hygiene)as it doesn’t rely on a customer informing the insurer about the event – in contrast insurers verify a claim event by using the model/third party data source and then informing the client.

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A model defining a claim? Surely models are deficient and can’t accurately showcase the real world well enough to be a useful way to assess a claim? Before we spiral into panic, let’s explore this a little deeper.

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Models are models – they aim to emulate reality. They’re never perfect nor will they ever capture the real world entirely.

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That being said, could there be a benefit to their use, particularly where traditional insurance coverage presents gaps or where insurance premiums based on the “real” world are too expensive.

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Let’s consider an example:

?Maria is a farmer in California's Central Valley with a 20-acre tomato farm in Fresno, California.

For her, Summer:

• Heatwaves are a significant risk, potentially damaging the tomato crop/yield.
• Maria has the option of traditional indemnity insurance but this is expensive due to the difficulty of precisely measuring heatwave impact on tomato yield.

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Maria looks into a Parametric Insurance solution which would work as follows:

Trigger:?The policy uses?satellite-based data?to monitor?growing degree days (GDD), a metric that measures heat exposure for crops.

• GDD Threshold:?The policy defines a specific GDD threshold for the critical fruiting period of tomatoes. For example, if the GDD during that period exceeds 2,500, it signifies a damaging heatwave.
• Payout Structure:?The policy offers a tiered payout based on exceeding the GDD threshold. Here's a possible structure: No GDD threshold breach:?No payout. GDD exceeds 2,500 by 10% (2,750 GDD):?25% of the pre-agreed insured amount (e.g., $5,000 payout on a $20,000 insured amount). GDD exceeds 2,500 by 20% (3,000 GDD):?50% payout ($10,000). Severe heatwave (GDD exceeds 2,500 by more than 20%):?Full payout ($20,000).

Benefits for Maria:

• Affordable coverage:?Parametric insurance often has lower premiums compared to traditional indemnity insurance due to the streamlined claims process and focused event triggers.
• Fast payouts:?Based on objective satellite data, Maria receives payouts quickly to address heatwave impacts, such as purchasing cooling systems or replanting damaged tomato plants.
• Transparency:?Maria understands the payout terms upfront and knows exactly what triggers a payment.

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What is important to Consider:

• Basis Risk:?While GDD is a good indicator, it might not perfectly capture the actual damage to the tomato crop. This is known as basis risk. Maria might experience some yield loss even if the GDD threshold isn't breached.
• Customisation:?Parametric insurance can be customized to Maria's specific needs. She could adjust the GDD thresholds, payout amounts, or even insure against additional risks like unexpected cold snaps.

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Limitations – insurance is a highly regulated market with some important rules in place for the protection of customers and for good reason! One such rule is that products sold must demonstrate “risk-transfer” and that it can be evidenced that there is an “insurable interest”. The customer also can’t be placed in a better position (“betterment”) from insurance products which open the potential for fraud and arbitrage (unrelated of course!).

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What this means is that:

• We can’t create products that are not directly related to the underlying risk event;
• There needs to be a sufficient chance that a claim is possible; and
• A customer purchasing the product won’t be in a better financial position after a claim than otherwise.

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All the above being said, is there a way to deploy these “risk-transfer” solutions in a different way?

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Capital markets and insurers complement one another. The lines have and continue to blur when we consider the plethora of derivative products out there designed for “hedging” – Put options, CDSs et al.

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Add in the Insurance Linked Security market, specifically designed to (where possible) cost effectively supplement the reinsurance capacity in the market to protect against catastrophe events (amongst other perils), there must be a mechanism to see parametric products fitting in here.

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Structured products/derivatives are sold as either over the counter (OTC) or exchange traded (ET). OTC are your bespoke products and ET are standardized with different regulations around both markets.

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A key difference between the derivatives marketplace and insurance is size and scale of available capital.

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According to ISDA OTC Trends 2023, “The global OTC derivatives notional outstanding reached $714.7 trillion at the end of June 2023” and “The gross market value of OTC derivatives reached $19.8 trillion by the end of June 2023”. These numbers are huge when we consider the total size of the global P&C insurance premiums hit $7.1 trillion in 2023 according to Swiss Re sigma 2023.

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There’s probably a good reason for this. Whilst capital markets are regulated and derivatives have seen their fair share of see-sawing in their own regulations, derivative structuring, it is reasonable to say, is arguably less constrained by regulation than insurance, particularly with respect to the underlying risk upon which the “bet” is being made.

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It isn’t a giant leap to assume that the ability to absorb insurance type risks by the capital markets is huge if we can structure the risk-reward mechanics to compete with the incumbent product and risk types.

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What’s the challenge?

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Insurance isn’t a commodity – policyholders vary (especially when we consider personal lines risks) and it becomes increasingly challenging to standardize and structure.

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That being said, this absorption by the Capital markets has worked for mortgages – the same people taking on home loans are the same people buying insurance – it should be possible, right?

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Is there such a thing as a parametric derivative?

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We’ll pick this up in Part II.

Piers Clark Innovatrix Capital Ltd Aelia Capital