The third in a series on parametric insurance. The first post, Fast Money, Slow Trust, examined basis risk, trigger design, and the coordination problem. The second post, The Supply Chain Problem, tested parametric against a real $450 million semiconductor loss. This one looks forward.
The gap between how risks behave and how insurance responds is widening.
That idea has been with me for a while. It was part of why I launched a parametric MGA back in 2020, and it came up again during my interview with InsurTech Scout in January this year. Every conversation I’ve had since, whether debating parametric trigger design with product builders, auditing bank insurance programs, or exchanging ideas with reinsurance and carrier people at conferences, keeps returning to the same observation. The emerging risks, cyber, climate, supply chain, AI liability, don’t look like the risks the insurance industry is able to handle with its current infrastructure and mindset.
The speed problem
Traditional insurance prices risk using historical loss data. Actuaries study what happened over the past five, ten, twenty years and project forward. That worked when the environment changed slowly or stayed the same. A factory’s fire risk in 2025 looks a lot like its fire risk in 2015. The models hold.
But a growing share of the risk environment changes shape faster than the models and the filed rates can follow. A ransomware variant propagates to 10,000 companies in an afternoon. A wildfire season burns through actuarial assumptions that took decades to build. An AI system generates a liability exposure that didn’t exist six months ago. By the time the actuarial tables reflect the new reality and the rates are filed and approved, the risk has already moved on.
The correlation problem
Insurance works because of diversification. One house burns, the rest don’t. One company gets sued, its competitors keep operating. Losses are independent events, and spreading them across a large pool makes each one manageable. The law of large numbers. That’s the foundation the entire model stands on.
The emerging losses that are growing fastest break that foundation. A single software vulnerability hits thousands of companies simultaneously. A climate event disrupts supply chains across continents. A critical supplier fails and every company downstream absorbs the impact at the same time. In 2023, a ransomware attack on one semiconductor supplier, MKS Instruments, generated over $450 million in documented downstream losses. Traditional insurance covered the downstream impact at zero dollars. The policies were never designed for a loss that starts at someone else’s facility and cascades through the supply chain.
These are correlated losses. Everybody is exposed to the same event, and the event hits everybody at once. For insurers and reinsurers, this creates a concentration problem that diversification can’t solve. You can’t spread the risk when the risk hits everyone in the pool simultaneously.
This is already visible in the reinsurance market. Swiss Re reported $142 billion in global insured losses for natural catastrophes in 2024, the second consecutive year above $100 billion. Cyber insurance faces the same dynamic: a systemic event affecting thousands of policyholders at once is the scenario that keeps underwriters up at night. And supply chain risk is correlated by design, because the entire point of a supply chain is that companies depend on each other.
The industry’s response has been to exclude, sublimit, or decline the most correlated exposures. That’s rational from an underwriting perspective. But it means the risks that are growing fastest are also the risks that are becoming least insurable under the traditional model.
The traceability problem
Traditional insurance needs to establish what happened, to whom, and how much it cost. That process, claims adjustment, requires a clear chain of events from cause to loss. The adjuster shows up, inspects the damage, reviews the records, and calculates the payout. It works when the cause is visible and the path from cause to cost is traceable.
But the causes of loss are getting harder to trace. Was the business interruption caused by the cyberattack on your vendor, or by their vendor’s vendor? Did the supply chain disruption start with the semiconductor shortage or the port closure? When an AI system makes a decision that results in a liability claim, who is responsible: the company that deployed it, the company that built the model, or the company that provided the training data?
Every one of those scenarios is playing out right now, and each involves a causal chain that is longer, more layered, and harder to untangle than what the claims process was designed for.
When the causal chain can’t be traced, claims don’t just slow down. Certain risks become functionally uninsurable under any model that requires proof of proximate cause. If you can’t establish what caused the loss, you can’t adjust it. And if you can’t adjust it, you can’t insure it. At least not traditionally.
What this means for traditional insurance
Traditional indemnity insurance isn’t failing. I have said that throughout this series, and I mean it. For the risks the industry was built to handle, it remains a very effective risk transfer mechanism. Complex liability disputes, litigation defense, non-catastrophic property losses, professional errors and omissions: actual-loss adjustment, legal infrastructure, and decades of case law produce outcomes that nothing else can replicate.
The world, however, is outgrowing parts of the model. The structural requirements that make traditional insurance work, historical pricing data, independent risk pools, provable causation, become constraints when applied to risks that move faster than the rate cycle, cluster across thousands of policyholders at once, or involve causal chains too layered to adjudicate. That mismatch is growing, and the industry’s primary response has been to exclude those risks or to price them at levels that effectively exclude them.
Where parametric comes in
Parametric insurance has a specific structural advantage for exactly these gaps.
Speed: parametric pays immediately when the trigger fires. No adjuster, no causation analysis, no year-long settlement process. For losses that cascade quickly and where the insured needs capital during the disruption, that speed has real value.
Correlation: parametric can be backed by capital markets capacity through catastrophe bonds and insurance-linked securities rather than relying solely on traditional reinsurance pools. The reason capital markets have more appetite for correlated insurance risk is structural: pension funds and hedge funds buying cat bonds are diversifying against their existing portfolio of stocks and bonds, so insurance-linked risk, even correlated insurance-linked risk, adds diversification to their book. Traditional reinsurers don’t have that luxury because insurance risk is their entire book.
Traceability: parametric doesn’t need to prove what caused the loss. It pays based on a measurable external event that triggers the contract, sidestepping the causal chain problem entirely. The trade-off is basis risk: the trigger may fire when the insured has no loss, or fail to fire when they do. But for exposures where the alternative is no coverage at all, that trade-off starts to look reasonable.
All of this depends on trigger design. A poorly designed trigger creates more problems than it solves: false payouts, missed events, disputes over data sources. The value of parametric lives or dies in the engineering of the trigger, and the industry is still early in learning how to get that right.
Parametric is not a replacement for traditional insurance. In Fast Money, Slow Trust, I wrote about the coordination problem, the gap between how parametric products are sold and how they interact with existing indemnity programs. In The Supply Chain Problem, I stress-tested parametric against a specific use case and found it viable only under narrow conditions. Those limitations are real. The question for the next decade is which risks belong in which structure, and whether the two can be designed to work together rather than operating in parallel.
What has to change
None of this happens automatically. Parametric has been called the next big thing in insurance for a decade, and it’s still under a percent of global premium. Getting from niche product to actual infrastructure takes a few things, and none of them are close to done.
That’s the work. If it gets done, the gap closes. If it doesn’t, it keeps widening.
Where this lands
Assume the work gets done. Trigger data improves to the point where neither side argues about the source. Parametric and indemnity are bought as one program. Regulators have classified the product. Designers are still iterating, and buyers are pushing back on the gaps instead of walking away at the first one.
Get there and parametric stops being a niche product and becomes the layer that absorbs what traditional insurance was never built to carry. A well-run program a generation out looks different from today’s. Indemnity keeps doing what it does well: property, liability, professional E&O. Parametric covers the exposures the old models can’t reach. Capital markets supply the capacity reinsurance can’t offer for systemic risk. One program, one set of scenarios, placed by someone who understands all the layers.
Miss it, and parametric stays what it’s been for ten years: a good idea with good marketing and not much premium. The gap keeps growing. And the exposures sitting outside the system, the $450 million losses that traditional insurance covers at zero, keep getting bigger and more frequent.
The direction is already set. The only open question is whether the industry can move fast enough to close the gap before it’s too wide to close at all.
