In the wake of ferocious wildfires that tore through the Southern California enclaves of Altadena and Pacific Palisades this January, affected home and business owners have mostly followed the standard insurance claims process — first, submitting notices of loss and, in many cases, painstaking inventories of personal property. Claims professionals generally lead with empathy, guiding traumatized policyholders through the steps needed to receive payouts. But, even the most responsive claims team cannot overcome the fundamental challenge: the process is often slow, complex, and opaque — and it can vary widely across insurers.
Recent New York Times coverage of two families affected by the Colorado Marshall Fire illustrates this clearly. After escaping the blaze, “each family soon reached out to their insurers to begin rebuilding their lives. And that’s when their paths diverged — sharply.” One family received prompt assistance and a sense of stability; the other found itself bogged down in paperwork and prolonged uncertainty. Even in well-intentioned systems, these differences can feel arbitrary — adding to the emotional toll of loss. The result, for many, is a sense of frustration and unfairness at a time when they are least equipped to navigate complexity.
Parametric insurance and reinsurance offer a fundamentally different payout mechanism which is a potential salve for the friction in traditional claims handling. Instead of requiring documentation of actual losses, these products pay out based on predefined triggers such as wildfire burn area or proximity, wind speed, earthquake ground shaking, or rainfall accumulation. The key difference from traditional indemnity products is that should these triggers be met —according to agreed-upon data sources — payment is made quickly, sometimes even automatically. They offer clarity and simplicity at a time when they matter most. As Isaac Espinoza, FCAS, CEO of Kettle, a wildfire-focused parametric managing general agent (MGA), explains, “Parametric insurance works because there’s a clear-cut loss definition that’s assigned up front. The rules dictate when a payout happens, which means there are fewer unknowns for everyone involved.”
Slow takeoff, rapid evolution
While there are some earlier examples of parametric-like arrangements, they did not really gain traction until the 1990s. Weather derivatives were emerging in the utilities and energy sectors, blurring the line between financial hedges and insurance risk pooling. Some reinsurers such as Swiss Re began exploring index-based solutions to service these industries, which evolved into the parametric products we know today. These products might offer payouts if winters were unseasonably warm or summers unseasonably cool, affecting energy usage patterns. Often seen as exotic and niche, they were purchased by large utilities clients with sophisticated risk managers seeking faster claims payouts and coverage certainty — particularly to manage the financial volatility tied to fluctuating energy demand. And these contracts were always an alternative risk transfer mechanism that complemented traditional coverage rather than replacing it.
In the late 1990s and early 2000s, agriculture became a potent proving ground for the parametric model, with products in both developed and emerging markets protecting farmers from low crop yields that can result from droughts or floods. Governments and development agencies stood up parametric-based programs aimed at protecting smallholder farmers who might lack access to traditional commercial insurance markets, exemplifying a new and distinctly modern form of social safety net. Incepted in 2003, Mexico’s CADENA program is a notable early example, providing government-subsidized parametric coverage based on rainfall thresholds at weather stations.
The Caribbean Catastrophe Risk Insurance Facility (CCRIF) launched in 2007 as the first multi-country, multi-peril parametric insurance risk pool in the world. Member countries could get rapid payouts when trigger thresholds were crossed for hurricanes, earthquakes, or excess rainfall. By this time, advances in climate modeling and hazard data were improving the viability — and complexity — of a range of innovative parametric structures. CCRIF’s success inspired other parametric risk pools, such as the African Risk Capacity (ARC) established in 2014 to insure African Union member states against drought and other perils. These regional pools underscored how parametric insurance could help close protection gaps in disaster-prone, underinsured regions.
ARC faced a significant setback during a drought in Malawi during the 2015–2016 growing season. With millions at risk of hunger, ARC’s parametric drought insurance failed to pay out. Rather than a direct weather observation like rainfall being used as a trigger, ARC was relying on a modeled simulation which estimated the number of people who would require food assistance, based on satellite-based rainfall estimates, crop growth models, and vulnerability and food security data. On the ground, severe crop failures and food insecurity were playing out, while in the model the estimate of people impacted was far lower — and crucially below the payout trigger. No automatic payment was triggered, but widespread backlash and humanitarian need led to a negotiated payout.
The ARC saga underscores the central challenge in parametric insurance: basis risk — when real-world losses diverge from triggered payouts. It also spotlights two interrelated trends within the space that continue to shape its evolution. First, parametric products have grown increasingly sophisticated alongside advances in modeling, data resolution, and sensor technology. They now often borrow directly from the same catastrophe modeling frameworks used in traditional reinsurance. Second, many triggers have shifted from simple, observable indices (like rainfall at a specific weather station) to model-based simulations that estimate loss impacts across geographies and populations. These developments expand the possibilities for parametrics but also raise new questions about transparency and accuracy.
Clarity, complexity, and COVID-19
Throughout the 2010s, parametrics gained traction among large corporate buyers and reinsurers, while improved mobile infrastructure and digital distribution opened doors to individual consumers. Microinsurance programs reached millions of smallholder farmers across Africa, India, and Latin America with rainfall-based or vegetation-based drought protection. At the other end of the spectrum, products like AXA’s Fizzy offered blockchain-enabled flight delay payouts — also an early example of the nascent blockchain hype that would explode after the pandemic — and startups like Jumpstart piloted earthquake coverage triggered by U.S. Geological Survey ground-shaking data. Yet despite growing interest and technical promise, many early ventures struggled to scale. Fizzy was quietly discontinued, and other offerings failed to convert enthusiasm into sustainable market share. The reasons often came down to a mismatch between the simplicity promised and the complexity experienced. Low awareness, distribution frictions, and discomfort with automated payouts — especially when no physical damage documentation was required — slowed adoption. In lower-income markets, public subsidies and NGO-led education helped bridge the trust gap. But in commercial contexts, confusion and skepticism persisted. Parametric insurance is often marketed on clarity and speed; ironically, it is the lack of clarity — for both buyers and capital providers — that has sometimes held it back.
Still, by the end of the decade, existing segments of the parametric market like natural catastrophe were coming into their own, and new products were coming to market. Several firms experimented with non-damage business interruption covers, where payouts could be triggered by events that didn’t physically damage structures, such as citywide transit disruptions, cyber outages, or — crucially presaging COVID-19 — pandemics. While the markets for these early experiments hadn’t matured in time to respond meaningfully to COVID-19, the experience spurred renewed attention to non-damage business interruption coverage. As Raveem Ismail, managing director of Trigger Parametric, observed, “The insurance world has a total obsession with property damage. When I was a terrorism reinsurance underwriter, I kept asking, ‘Why are we so obsessed with property damage?’ With terrorism, those who suffer aren’t necessarily at the epicenter of an attack. The business interruption coverage alone is huge. But you needed a bullet hole through the window for policies to pay out the non-property part of the cover.” That narrow focus became especially problematic during the pandemic, when countless businesses experienced crippling losses without suffering any physical damage. Parametric triggers tied to operational disruption — once seen as speculative and abstruse — now appeared vital, even overdue.
In the years following the pandemic, the global parametric insurance market has seen substantial growth, with annual gross premiums rising from $11.7 billion in 2021 to an estimated $16.2 billion in 2024. While still a pittance compared to the broader reinsurance market, the pace of growth in recent years is nonetheless impressive and is expected to continue, with projections reaching upwards of $50 billion by the mid-2030s. If earlier generations of parametric products revealed the challenges of aligning triggers with real-world losses and communicating coverage clearly, the post-pandemic years have brought sharper tools and more experienced players to the table. Improvements in both data granularity and modeling, ranging from hyper-local weather feeds to high-resolution wildfire and flood models, have helped firms better align trigger conditions with underlying risk. The result is not just a broader range of perils that can be covered, but also a growing confidence among capital providers and brokers that parametric coverage can be both accurate and explainable.
New triggers, new players
This shift is exemplified by a new generation of MGAs applying advanced modeling and data design. Kettle, for example, underwrites wildfire risk using a “Fire-in-Parcel” approach. “We take the polygons of the footprints of wildfires, mapped out in detail, and map those against tax assessor parcel coordinates,” said Espinoza. “We know something triggers when the fire enters that perimeter.” Because wildfires often result in total losses when they affect a property, Espinoza argues, the clarity of fire perimeter data makes basis risk more manageable than in many other perils: “Other perils may need a weather station. With wildfire, we can use perimeter data — it’s much more precise.” The company also structures reinsurance using square grids to help cedants manage regional risk concentrations. “They might find certain areas are overconcentrated,” Espinoza explained. “So they buy wildfire protection which helps soften the blow and protect against extreme exposure concentrations.”
Weather perils — especially temperature, precipitation, and snowfall — have long been the bread and butter of parametric insurance, but recent years have seen a shift toward more refined and structured applications. Arbol, originally focused on agriculture, has grown into one of the most prolific platforms in the space, offering parametric products across sectors from energy to commercial property. COO Alexander Isakov emphasized how the market has evolved: “Four years ago, there was almost no uptake outside of CAT. We had to do a lot of customer education. But now we’re getting a lot of inbound interest — especially from agricultural firms looking to protect things like pecan farms or hay from excess rain.” Arbol has also introduced second-generation parametric designs — multi-trigger structures that combine multiple environmental indicators to more closely match actual exposure, such as interactions between rainfall volume and temperature.
Demex, by contrast, focuses on modeled aggregates for insurance portfolios exposed to weather risk including severe convective storm (SCS), with plans to expand into winter storm coverage. Their RCR Re product introduces a parametric form of stop-loss reinsurance, designed not to protect solvency after catastrophe, but to smooth earnings volatility at lower layers of the coverage stack, which traditional reinsurers have increasingly shunned as secondary perils grow more unpredictable. “It’s technically a parametric product, but it behaves more like an aggregate indemnity layer,” said EVP Charlie Eadie. “We take observed weather and translate it into modeled loss, then trigger off that.” By calibrating models with insurers’ own claims data, “we ingest geolocated claims and regress against historical weather.” Demex aims to reduce basis risk and align the trigger closely to expected loss. Several clients have already received payouts under Demex policies this year, with quarterly settlements tied to observed weather data and modeled losses. While uptake remains cautious (typical of a risk-averse industry), early recoveries that align with actual indemnity outcomes are beginning to build credibility and interest.
Building trust in a fragmented market
For Ismail, whose firm Trigger Parametric designs parametric contracts for natural or man-made perils, distribution is king. Echoing AXA’s experience with Fizzy, he cautions that “insurance is the ultimate ‘build it and they will not come’ industry. Even if your product is more accurately modeled to reflect the real risk, this will not drive market traction.” He warns that focusing on product design before confirming distribution leads to “a garage full of white elephants nobody will ride.” In his view, the real challenge is not modeling — it’s selling. “Models can be built in two days flat,” he quipped. The harder task is earning trust — both from the buyer and the capital provider — and walking the buyer through the logic of coverage. “A good broker will take the insured on the journey of recognizing their pain, articulating it, and manifesting it in the desire and budget. That’s where the broking comes in and needs to shine.”
That journey unfolds within a distribution ecosystem far more complex than a simple seller-buyer relationship. While parametrics promise clear and rapid payouts, their delivery today often mirrors the traditional complexity they sought to supplant. As Sam Knee-Robinson, a Lloyd’s capital and emerging risk broker at Guy Carpenter, put it: “The value chain is changing.” Parametric products are stepping into spaces where traditional reinsurance has pulled back, especially on aggregate layers. “Parametrics can fill a space where traditional products have fallen away,” he noted. But that opportunity brings its own challenges. Many MGAs “act as quasi-brokers,” originating deals and sometimes bringing capital to the table — but often face what Knee-Robinson calls a portfolio construction problem: the difficulty of assembling a balanced, modelable book that capital providers will trust. Without the scale or track record of incumbents, newer players must work harder to align risk appetite, modeling, and capacity.
Knee-Robinson describes the current moment as a cold start problem: “How do you sell a new product? Someone has to take a leap of faith on both sides of the transaction.” Trust in brokers remains paramount: “People trust a Guy Carpenter or Aon — but it takes longer to trust newer entries.” For buyers, the landscape is equally daunting. “You might talk to four or five MGAs, each with different models and data sources. The onus is on the buyer to sort it out.” Unlike the Insurance-Linked Security (ILS) space, where shared frameworks like AIR and RMS lend consistency, the parametric market lacks modeling standardization. As more MGAs, reinsurers, and data providers enter the space, the simplicity of the trigger is often counterbalanced by complexity in the deal. Distribution remains king — but in this era, brokers must also serve as translators and educators for both sides of a deal.
Despite this complexity, parametrics have proven to be more than a novelty limited to edge use cases. With a dynamic market teeming with agile startups earning partnerships with huge incumbents and Lloyd’s syndicates, observers see a potent combination — incumbents bring capacity, global data, and trust, whereas startups bring novel ideas and digital-first distribution. All along the way, regulators and rating agencies have become more trusting of parametrics, greatly increasing their viability as more buyers integrate them into their capital stack. This convergence of trust, innovation, and capital comes at a moment of mounting pressure on the global risk landscape. Climate change may be the most visible driver, but it is far from the only one. From cyberattacks to grid strain, from pandemics to geopolitical tension, the risks facing businesses and governments have grown more complex, interdependent, and frequent. In this context, parametric insurance is no longer just an exotic alternative — it is becoming a strategic tool for risk managers, CFOs, and policymakers. Whether backing public disaster pools, smoothing quarterly earnings, or protecting against emerging perils, parametrics are increasingly seen as one arrow in the broader quiver of financial resilience.
As the parametric market continues to grow and diversify, the next frontier may lie not just in what types of risks can be covered, but in how seamlessly these products can be integrated into the financial and operational fabric of risk-bearing institutions.
DJ Falkson, FCAS, is an actuarial director at Lemonade. He is a member of the Actuarial Review Working Group and its Writing Subgroup.
