Parametric Insurance FAQs: Common Questions Answered

Parametric insurance pays when a predefined event happens, not when physical damage is assessed. If wind speeds exceed a defined threshold, for example, the policy pays automatically. A traditional property policy would first require damage inspections, repair estimates, and claims review.

This makes parametric faster, more transparent, and more flexible, as the table below summarizes.

Our solutions cover financial losses from natural catastrophes and extreme weather events, including:

• Direct losses: physical damage to your own property and assets.
• Indirect losses following damage to your property: lost revenue and added costs while you recover from direct damage, such as business interruption during rebuilding or penalties for missed deliveries.
• Non-damage business interruption: lost revenue when your property is undamaged but an external event still halts operations, such as a port closure, regional evacuation, or a key supplier being hit.
• Third-party investment losses: losses on investments in companies or assets hit by a covered event, for example a fund exposed to infrastructure damaged by a hurricane.

Parametric insurance does not cover events that cannot be measured by an objective, independent index. It also does not cover losses below the trigger threshold, even if real damage occurred. Standard exclusions still apply (such as war, nuclear, and intentional acts), and the payout is capped at the policy limit regardless of actual loss size. 

Parametric insurance can cover perils that can be measured objectively and independently. Common examples include hurricanes (wind speed), earthquakes (magnitude), excess rainfall or drought (rainfall levels), extreme temperatures, and floods (river height or water depth measured on-site). The key requirement is reliable data to define a trigger. 

Descartes covers a broad range of climate, weather, and emerging risks across more than 60 countries. Available perils and triggers vary by region, but the main perils include:

Parametric insurance is most useful when traditional insurance leaves gaps or cannot respond quickly enough.

Companies often use it when they need:

• Faster liquidity after an event
• Protection for hard-to-insure risks
• Coverage for non-damage business interruption
• Flexible protection across multiple sites or countries
• Additional limits above traditional programs
• Deductible buydown
• Coverage for losses excluded in traditional policies (e.g. outdoor equipment)

For example, a company with suppliers across Asia may use parametric insurance to protect against typhoon-related disruption, even if no direct physical damage occurs at its own facilities.

Parametric insurance is often used alongside traditional insurance rather than replacing it entirely.

Descartes has the largest business development team dedicated to parametric insurance globally, with experience across 60+ countries. That reach allows us to stay close to brokers and respond directly to their clients' needs. Backed by 150+ in-house data scientists, risk modelers, and engineers, our commercial team can offer solutions that are grounded in data and calibrated against real losses, rather than off-the-shelf models. 

Three things set our approach apart:

• Tailored data for each risk. We select the best data source for each client based on their location and peril, rather than using off-the-shelf models.
• Triggers calibrated against real losses. We share historical index values so clients can see how a parametric payout would have matched their past losses, reducing the risk of mismatch between trigger and actual loss.
• Continuously updated indices. As client needs change and technology improves, we update indices and introduce new ones to keep coverage sharp.

How we compare: Smaller parametric providers typically rely on off-the-shelf models and third-party data, with fewer experts dedicated to product design. That means less ability to tailor triggers and a higher risk of payouts that don't match real-world losses.

Yes. Parametric reinsurance pays insurers based on objective measurements (such as wind speed, ground shaking, or rainfall) rather than traditional loss adjustment. What sets Descartes apart in this space is our ability to structure highly flexible, bespoke indices that are modelled to closely reflect cedents' actual portfolio losses, delivering a very high correlation between trigger and real-world outcomes. 

How insurers use it:

• Earnings protection against high-frequency, low-severity events that erode retentions.
• Capital efficiency on remote layers of the reinsurance tower.
• Immediate liquidity for reinstatement premiums and post-event cash flow.
• Filling gaps left by traditional reinsurance.

Descartes offers up to $140M of A+ rated capacity per cover across 30+ countries, for cyclone, flood, earthquake, wildfire, and severe convective storms. Each treaty is bespoke, built around the cedent's portfolio and back-tested against their historical losses. 

Example: For illustration, a Florida homeowner carrier with a $140M XS $75M parametric layer would have triggered an estimated $40M recovery during Hurricane Milton in 2024 based on observed wind speeds. Actual recoveries depend on the specific trigger design. 

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For underwriting year 2026, Descartes Underwriting provides parametric insurance capacity of up to €80M–€140M per policy, with higher capacity available in certain cases. The exact amount depends on the peril, geography, and structure. 

Separately, Descartes Insurance provides indemnity-based capacity in the EEA, typically up to €10M–€25M per policy depending on the product line.

All figures represent maximum annual capacity per policy and may vary depending on deal-specific factors such as portfolio accumulation, risk characteristics, timing, and exclusions. For precise capacity, please contact our commercial team.

Different industries use parametric insurance based on the perils that threaten them most and the financial impact of those perils:

• Agriculture and viticulture: drought, excess rainfall, frost, hail, and crop yield shortfalls. Used to stabilize revenue across volatile seasons. See examples here
• Energy (renewable and conventional): lack of wind for wind farms, lack of water for hydropower plants, extreme temperatures for grid load, and hurricane damage to coastal facilities. Often used for revenue protection rather than asset protection.
• Real estate and hospitality: hurricanes, earthquakes, wildfire, and flood. Common use cases include deductible buy-down and NDBI cover for hotels affected by nearby events.
• Transport and shipping: river level extremes affecting inland shipping, port closures from cyclones.
• Manufacturing and industrials: supply chain disruption from natural catastrophes affecting key suppliers or logistics nodes.
• Public sector: disaster response funding for governments and public entities, typically structured to release immediate liquidity after qualifying events.

The most common climate-risk use cases are protection against tropical cyclones, flood, drought, excess rainfall, wildfire, and extreme temperatures. Each is structured around a measurable index, such as wind speed at a defined location, cumulative rainfall, satellite-measured burned area, or temperature thresholds over a defined period.

Beyond peril-specific cover, parametric is frequently used to address structural gaps that traditional programs do not close:

• Sublimit top up: traditional policies often cap coverage for key perils below the client’s actual exposure. A parametric layer on top of sub-limits can restore full economic protection
• High-deductible management: parametric can buy down retained exposure. Instead of absorbing large first losses, clients gain predictable, trigger-based liquidity.
• NDBI: parametric can cover revenue loss without physical damage, a major blind spot in traditional programs.
• Liquidity needs: parametric delivers near-immediate liquidity, enabling cash-sensitive organizations to absorb first expenses and sustain operations before the indemnity settlement is reached
• Exclusion wrap: when a traditional policy excludes a specific peril or a defined geographic zone, parametric can be structured to respond to this exclusion

In today's market, a practical approach is to reinvest premium savings from traditional programs into one or more of these parametric structures, strengthening overall coverage without increasing total budget.

Yes. Parametric programs are well suited to multi-site and multi-country portfolios because the trigger is location-specific rather than tied to a single insured asset. A single policy can cover dozens of sites across multiple countries, with each site assigned its own trigger and payout structure.

This is particularly useful for global corporates with exposure across regions that have different perils, since one parametric program can replace several local placements.

No. Parametric insurance can cover any financial loss linked to a measurable event, not just physical damage. Common non-property uses include non-damage business interruption (revenue lost during a port closure or regional evacuation), supply chain disruption, additional operating costs after a catastrophe, and third-party investment losses on assets affected by a covered event. 

Yes. NDBI is one of the most common uses of parametric insurance because traditional policies often exclude it or cap it at low limits.

NDBI cover responds when an external event (a hurricane, port closure, evacuation order, or supplier outage) causes revenue loss even though the insured's own property is undamaged.

Example: a coastal hotel that loses bookings for several weeks after a nearby hurricane, despite suffering no direct damage to its own buildings, would receive a parametric payout if the storm met the policy's wind-speed and proximity trigger.

More examples: NDBI for Australian Tourism Operators

Parametric insurance supports business continuity in two ways: by providing rapid liquidity after a disruptive event, and by covering supply chain exposures that traditional policies often exclude.

For supply chains specifically, a parametric policy can be triggered by an event affecting a key supplier, logistics hub, or transit route, rather than by damage to the insured's own assets. Typical structures cover port closures from tropical cyclones, flood-related infrastructure outages, and earthquake damage in supplier regions.

Parametric insurance provides liquidity because payment is triggered by the measured event, not by a loss adjustment process. Once the trigger is met and verified by the independent calculation agent (typically within days of the event), payment is released, generally within 2 to 3 weeks.

This compares with traditional indemnity insurance, where loss assessment, negotiation, and settlement typically take 12 to 18 months for major events. The speed gap matters most for clients with cash-sensitive operations, such as those that need to fund emergency response, retain staff, or restart production before traditional indemnity is paid.

Three main factors drive the price of a parametric policy:

1. Probability of the event: Pricing is built from historical data and physical modeling of how often the trigger has been (or would have been) met at the insured location.
2. Payout structure: Higher limits, lower attachment points, or finer payout gradients increase premium. A binary policy that pays the full limit at a single threshold is priced differently from a tiered policy with multiple payout levels.
3. Seasonal forecasts: Forward-looking climate signals such as ENSO can influence the likelihood of certain perils in a given season, and are factored into pricing when relevant.

No, parametric insurance is not inherently more expensive than traditional insurance. Pricing depends on the probability of the trigger event, the coverage limit, and the structure of the policy.

Where parametric appears more expensive on a like-for-like premium basis, the difference usually reflects broader or faster coverage:

• It covers exposures traditional policies often exclude: Traditional payouts are frequently capped by sublimits or deductibles for specific risks, and may exclude certain financial losses (e.g., indirect financial losses from business disruption or physical assets like outdoor equipment). With parametric insurance, the capacity is not capped by deductibles and payouts can be used for any type of financial loss. Non-damage business interruption, for instance, is very challenging to find in the traditional market at a reasonable price. .
• It pays in weeks rather than months: A cheaper policy that takes 12 to 18 months to settle may cost far more in lost revenue or emergency borrowing than a parametric policy that pays immediately.
• It provides certainty over the payout amount from day one: Predefined triggers remove any ambiguity over whether a loss qualifies, so clients know exactly what they will receive if a covered event occurs.
• Simplicity reduces hidden costs: Traditional policies can involve lengthy documentation, negotiations over whether a loss qualifies, and disputes over the application of deductibles and sublimits. Parametric eliminates all of that. 

Parametric structures can also be built around the client's budget, with underwriters designing a payout structure that matches a defined spend. The right comparison is therefore not premium alone but total cost of risk, including speed of payout, scope of cover, and coverage certainty.

It mainly depends on the amount of work required to structure and price the deal. In simple terms, what we usually say for parametric solutions is a minimum premium of around $50k.

For single-location Cat-in-a-Circle structures, the minimum can go down to about $10k.

For more complex deals, such as location-specific flood covers, we generally prefer to start from around $100k, reflecting the higher modeling complexity, data requirements, and structuring effort.

Basis risk is the risk that an insurance payout does not perfectly match the insured’s actual loss. In parametric insurance, this occurs when the payout – based on an index such as rainfall, wind speed, or earthquake magnitude – does not fully align with the client’s real financial impact.

No. Basis risk is not unique to parametric insurance, although it is more explicit and visible in that context.

In traditional insurance, similar gaps between expected and actual payout already exist, but they are framed differently, such as: underinsurance (limits below actual loss), policy exclusions, ambiguous wording, or differences in claims adjustment interpretation. In practice, the industry already lives with basis risk; it is simply described as uninsured loss, out-of-pocket cost, or claims dispute outcomes.

The key difference is transparency:

• Traditional insurance → mismatch discovered after the loss event
• Parametric insurance → mismatch defined upfront as basis risk and assessed before inception

Parametric insurance makes a long-standing reality of insurance, namely the gap between expected and actual payout, explicit, measurable, and pre-agreed. 

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Modern parametric solutions such as Descartes’ reduce basis risk through better alignment between index and real-world loss:

• Close collaboration between clients, brokers, and carriers to refine index design
• Careful selection of data sources depending on peril and location
• Use of high-resolution and scientifically robust datasets
• Strong correlation testing between index and historical losses
• Continuous back-testing and calibration of triggers
• Use of updated technologies and improved datasets over time

The key principle is simple: the better the index reflects the client’s exposure, the lower the basis risk.

Descartes designs each index in four steps:

1. Understanding the client’s needs: We work with the broker to identify the most adapted solution for the client, based on their exposure, risk appetite, and coverage objectives.
2. Data source selection. The team selects the best available data source for the client's location and peril, drawing from satellite imagery, ground sensors, public meteorological networks, and proprietary models.
3. Calibration against historical losses. Where the client can share past loss data, the team back-tests the index to show how parametric payouts would have aligned with actual losses in prior years.
4. Ongoing index updates. Indices are reviewed regularly so the policy reflects new data sources, evolving client exposures, and improvements in measurement technology.

Throughout the process, the structure is fine-tuned through back-and-forths with the broker and client to ensure it exactly matches their needs. Index design is supported by 150+ in-house climate scientists, risk modelers, data scientists, and software engineers, the largest such team dedicated to parametric (re)insurance.

Basis risk can be quantified before binding through historical back-testing. This involves applying the proposed index to the past 20 to 30 years of recorded events at the insured location and compare the parametric payouts to the client's actual losses (or to industry-loss proxies if client loss data is not available).

Yes. Most parametric programs are designed to sit alongside a traditional property program rather than replace it. The two products work well together because they cover different layers of the same risk: traditional indemnity provides comprehensive loss settlement over time, while parametric provides rapid, predefined liquidity for specific events.

Common combined structures include parametric layers on top of traditional sublimits, deductible buy-downs, and NDBI cover added to a property program that excludes it.

Five hybrid structures are most common:

1. Exclusion wrap: A parametric policy fills a peril or zone excluded from the traditional program (for example, a flood exclusion in a high-flood-risk area).
2. Sublimit top-up: Where the traditional policy caps cover for a specific peril (such as a $50M cyclone sublimit on a $500M property tower), a parametric layer provides additional protection on top (such as a $100m cyclone cover on top of the $50m sublimit).
3. Deductible buy-down: For clients carrying large deductibles on natural catastrophe perils, parametric provides predictable, trigger-based liquidity to absorb first losses.
4. NDBI overlay: A parametric layer covers business interruption from external events, which is typically excluded from a traditional property program.
5. Liquidity layer: Parametric runs alongside the traditional program purely to accelerate cash flow in the weeks following an event, before traditional indemnity is settled.

Parametric solutions are well suited to clients with large self-insured retentions because they provide predictable, fast-paying liquidity inside the retention layer. Rather than absorbing the full first-loss exposure on the balance sheet, the client can buy a parametric cover that pays a defined amount when a triggering event occurs, regardless of the eventual indemnity settlement above the retention.

This is especially useful for clients with large retentions on natural catastrophe perils, where a single event can create significant cash strain even though it stays below the traditional policy attachment.

Parametric insurance can be ceded into a captive in the same way as traditional cover, or written directly between the parametric carrier and the corporate. The most common captive integrations are:

• The captive retains a parametric layer for known, modeled exposures, and reinsures the rest into the commercial market. Parametric layers are highly flexible and enable a fully personalized risk transfer strategy.
• The captive uses parametric as reinsurance cover to recapitalize quickly after a major event, ensuring it can continue to fund insured losses.

Specific structures depend on the captive's domicile and regulatory regime.

Descartes acts as both a primary parametric insurance and a parametric reinsurance provider. On the reinsurance side, common structures include:

• Aggregate parametric XS treaties that respond when cumulative event-based payouts exceed a threshold.
• Per-occurrence parametric XS treaties that recover on individual major events meeting a defined trigger.
• Quota share arrangements on parametric portfolios.

Treaty capacity is up to $140M per cover, A+ rated, across 30+ countries, for cyclone, flood, earthquake, wildfire, and severe convective storms.

Descartes can get involved at any layer of an XS reinsurance program.

Multi-peril parametric programs combine triggers for two or more perils under a single policy, typically with a shared limit. Common combinations include cyclone plus earthquake for coastal portfolios, drought plus excess rainfall for agriculture, and flood plus wildfire for property in mixed-hazard zones. 

Placing a global multi-country program typically follows these steps:

1. Exposure mapping. The broker and client compile location data (addresses or GPS coordinates), insured values, and perils of concern for each site.
2. Peril and trigger design. Descartes designs a per-location trigger and payout structure, calibrated to local hazard data, leveraging the broker’s expertise.
3. Structuring. Limits, retentions, and aggregate caps are agreed across the portfolio.
4. Regulatory and tax review. The placement structure is reviewed for each country to ensure compliance.
5. Binding. The program is bound under a single policy or coordinated set of policies.

Typical timeline from data submission to binding is 4 to 8 weeks, depending on portfolio complexity.

A hard market and a soft market describe two phases of the insurance cycle.

• A hard market is characterized by high rates, rising premiums, restricted capacity, and tighter coverage terms (lower limits, higher deductibles, more exclusions). Carriers protect profitability by being more selective.
• A soft market is characterized by low rates, decreasing premiums, broader capacity, more flexible contracts, and wider coverage availability. Carriers compete on price and terms.

Insurance markets cycle between these phases over multi-year periods, driven by claims experience, reinsurance pricing, capital availability, and broader economic conditions.

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Insurance markets move through hard and soft cycles that change pricing and capacity in the traditional market, but they do not change the structural features of traditional cover. Deductibles, sublimits, exclusions, and settlement timelines persist across the cycle.

Current cycle, as of 2026: The market is in a soft phase, but this cycle differs from previous soft markets such as 2016 and 2017. Premiums have decreased, but coverage terms (deductibles, sublimits, exclusions) have remained largely unchanged. Buyers are paying less, but they are not necessarily getting broader cover.

This is why parametric insurance remains relevant regardless of where the cycle sits: it addresses structural gaps in traditional cover that the cycle does not close.

Hear more from our Head Americas

Yes. Parametric insurance remains relevant in a soft market because the gaps it addresses (high deductibles, sublimits, exclusions, NDBI, and post-event liquidity) persist regardless of the price cycle.

In a soft market, parametric is most often used to reinvest premium savings from traditional programs into structural coverage improvements. Rather than absorbing the savings as a cost reduction, clients can use them to:

• Buy down deductibles on key perils
• Restore sublimits that cap the traditional program
• Add NDBI cover that traditional policies typically exclude
• Secure rapid liquidity after major events

The result is a stronger overall program at a similar total budget.

Hear more from our Head Americas

Not in most cases. Cheaper traditional premiums do not address the structural limits of traditional cover, such as long settlement timelines, sublimits, deductibles, exclusions, and limited NDBI cover. These gaps persist regardless of the price cycle.

A more effective use of premium savings is to redirect them into parametric structures that strengthen the overall program: buying down deductibles, restoring sublimits, or adding NDBI cover. The total budget remains broadly similar, but coverage quality improves and liquidity exposure decreases.

Maintaining a parametric layer through the cycle also supports coverage continuity: trigger design and historical calibration accumulate value over time, and rebuilding the same structure later can be slower and more constrained.

Trigger design follows three principles: physical relevance, data quality, and loss correlation.

1. Physical relevance. The trigger metric must reflect what actually drives loss at the insured location. For coastal property, this is typically peak wind speed; for inland flood, river height or cumulative rainfall; for crop, soil moisture or area-based yield.
2. Data quality. The data source must be reliable, independent, and available within days of the event. Where multiple sources exist, the most relevant at the insured location is selected.
3. Loss correlation. Triggers are calibrated against historical losses (where available) and modeled event sets, with the payout curve adjusted so parametric payouts align as closely as possible with expected losses.

Vulnerability is also factored in: identical wind speeds cause different damage at a wood-framed beachfront property versus a reinforced concrete condominium, so payout structures reflect the physical characteristics of the insured assets.

Descartes leverages a diverse ecosystem of data to meet client needs. We utilize sources such as high-resolution satellite imagery, IoT sensors, ground-based radar, public weather stations, or state-of-the-art physical models. By diversifying our sources, we ensure that the data driving a payout is both precise and relevant for our clients.

The primary distinction is how the event is measured. Sensor-based triggers rely on physical hardware—such as weather stations, river gauges, or IoT devices—to record an event at a specific, localized point. These are highly transparent and ideal for assets located directly at or very near a sensor.

Model-based triggers use "reanalysis data," which blends multiple sources such as satellite images, sensor network observations and physical models to estimate conditions across a broad geographic grid. While sensor-based systems are simpler, model-based systems offer superior resilience and coverage for large-scale disasters where physical sensors may be sparse, missing, or destroyed during the event.

We maintain data integrity through a rigorous, three-layered process that spans from policy inception to claim settlement:

• Pre-policy quality control: Before a policy is even issued, our dedicated data team oversees all incoming information. They apply a rigorous selection process for new data sources and perform continuous monitoring to ensure that the data is both highly available and accurate.
• Contractual transparency: Every policy explicitly defines the "Primary Data Source" (the exact source used for triggering) and a "Payout Structure." This creates a clear grid showing how specific index levels, such as wind speed or rainfall millimeters, translate into a mathematical payout.
• Independent verification: To remain impartial, an independent third-party agent can audit the data for anomalies, such as sensor failures or outliers, during the coverage period.

To prevent any interruption in coverage, every policy includes a Fallback Protocol. If the primary data source is found to be erroneous or unavailable, the cover automatically switches to a secondary data source already specified in the policy wording. This ensures the claims process remains objective, automated, and uninterrupted even in the event of hardware failure.

It is absolutely possible to design triggers based on a client’s on-site sensors. To do this, our dedicated data team performs an upstream review to ensure the device produces data that is accurate, tamper-resistant, reliable and consistent over time. To maintain impartiality, a certification agent will still validate this on-site data against external data sources such as nearby stations to screen for anomalies, ensuring the final payout is based on a verified truth. 

Our modern catastrophe modeling offers a more precise and forward-looking risk assessment than traditional insurance, which often relies on historical claims data.

• Climate resilience: Unlike actuarial methods looking at historical data only, our models also account for current data and trends. This allows for a more accurate pricing of risks in the context of a rapidly changing climate.
• Scientific rigor: Our approach is built on state-of-the-art environmental science, modeling the physical characteristics of hazards rather than just statistical trends. This proper risk assessment ensures that the premium reflects the actual risk and that the client is not overpaying their cover.
• Anticipating unprecedented events: By simulating catalogs of thousands of potential scenarios, we can model extreme "Black Swan" events. This helps clients understand and cover catastrophic risks that may not have occurred in recorded history but are physically possible today.

Descartes incorporates climate change in two ways:

1. Forward-looking hazard models. Rather than pricing purely from historical event frequency, the modeling team uses climate-conditioned hazard models that adjust event probabilities for current climate states. This is particularly relevant for tropical cyclones, wildfire, and extreme rainfall, where historical frequency is no longer representative.
2. Continuous model review. Hazard models are updated as new climate science is published, ensuring that pricing and trigger design reflect the latest understanding of climate-driven risk.

Payouts are typically issued within days or weeks after a qualifying event. The exact timing depends on how quickly the trigger data is validated by the independent calculation agent, which usually takes a few days, followed by standard settlement processes.

This compares with traditional indemnity, where settlement of major catastrophe claims typically takes 12 to 18 months.

No. Parametric payouts are based on the measured event, not on physical damage. If the trigger (such as wind speed, rainfall, or earthquake magnitude) is met, the policy pays the predefined amount regardless of whether physical damage occurred. The client simply needs to fill a declaration of loss form to declare the financial loss sustained because of the event. This is what enables parametric to cover non-damage business interruption. 

Payouts are designed to align as closely as possible with expected losses through careful trigger design and back-testing. As in traditional insurance policies that include deductibles, limits and exclusions, parametric contracts may not cover the full financial loss sustained by the client. If the clients want to ensure that their full exposure is covered, they can purchase larger limits or work with their broker and parametric insurance provider to fine-tune the index. Many clients also combine parametric with a traditional indemnity layer so that any residual gap is covered by the indemnity policy.

Conversely, if the client sustained lower losses than the pre-agreed amount from the policy’s payout structure, it will be reflected in the declaration of loss form and the parametric payout will correspond to the declared loss amount. However, this case remains rare as parametric policies can encompass all types of financial losses, even indirect ones.   

Independent calculation agents act as the neutral third party that verifies whether a trigger has been met and calculates the resulting payout. Their role removes any ambiguity or conflict of interest from the claims process: the carrier does not decide whether to pay, the data does.

That said, not every policy requires a dedicated independent calculation agent. Many of the indices Descartes uses are based on data published by highly reputable agencies such as NOAA, whose methodologies are well-established, transparent, and widely recognized across the industry. In those cases, the robustness of the underlying data source itself provides the necessary level of objectivity and trust. 

When an independent calculation agent is named in the policy, they follow a predefined fallback protocol if the primary data source fails, and audits the data for anomalies during the coverage period.

Three payout structures are most common:

1. Binary. The policy pays the full limit when the trigger is met, and nothing otherwise. Simple and transparent, used where the trigger is well calibrated to a known loss event.
2. Tiered. The policy pays defined amounts at multiple threshold levels. For example, 25% of limit at one wind speed, 50% at a higher speed, 100% above a top threshold.
3. Linear. The payout scales continuously with the index value between an attachment point and an exhaustion point, producing the finest match to varying event severities.

All three structures are capped at the policy limit.

Parametric wordings are typically shorter than traditional insurance wordings because they replace loss adjustment language with a predefined trigger and payout formula.

A traditional wording spends most of its length defining what counts as a covered loss, how damage is assessed, how depreciation and salvage are handled, and how disputes are resolved. A parametric wording does not need this language because there is no on-site loss adjustment: the policy pays when the named index reaches the named threshold.

In exchange, parametric wordings are more detailed in three areas:

• Data source specification. The exact primary data source is named, with the resolution, refresh frequency, and any required preprocessing.
• Fallback protocol. A secondary data source and the conditions under which it replaces the primary are specified upfront.
• Payout grid or formula. The relationship between the measured index value and the resulting payout is fully defined, usually in a table or mathematical formula.

The net effect is less ambiguity at claims time and more precision agreed upfront.

As in any insurance policy, the entire wording should be read and understood. Four clauses are particularly important in a parametric policy and differ meaningfully from traditional cover:

1. Qualification of the insured event: how the event is defined, measured, and verified.
2. Determination of the payout: the formula or grid that converts the measured event into a payout amount.
3. Sources of data: the primary data source used to measure the event, and any fallback source if the primary is unavailable.
4. Claims notification and handling: how a claim is triggered, verified, and paid.

These four clauses describe how the cover actually works in practice, so they warrant the closest attention.

Exclusions in parametric policies are generally in line with those found in traditional insurance policies. The specific exclusions depend on the cover, the peril, and the jurisdiction, and commonly include events such as war, nuclear or biological events, intentional acts, and sanctions-related restrictions.

As with any policy, exclusions should be reviewed carefully by the insured to identify situations or events that may appear to fall within the scope of the cover but that would not result in indemnification.

Parametric policies also include structural conditions that define the boundary of cover. These are not exclusions in the traditional sense, since they reflect how parametric works rather than carve-outs from broader cover, but they have a similar effect and should be reviewed with the same care:

• Events below the trigger threshold receive no payout, even if the insured suffers real loss
• Events outside the coverage period or named geographic zone are not covered
• Events not detected by the named data source (or its fallback) cannot trigger a payout

Transparency in parametric insurance comes from clearly defining the payout rules in the policy before a loss occurs. The contract specifies:

• The trigger event (for example, wind speed, rainfall level, or earthquake magnitude)
• The data sources used to measure the event
• The payout formula or payout grid
• How trigger levels translate into indemnification

Because these elements are agreed in advance, there is less room for disputes over loss assessment or payout calculations. Unlike traditional insurance, parametric insurance does not rely on lengthy on-site loss adjustment processes or subjective financial damage evaluations.

Claims are typically settled faster because the event report is generated shortly after the event using pre-agreed third-party data sources. This reduces uncertainty and helps insureds understand exactly how payouts are determined.

Transparency can also be improved through back-testing. By applying the payout formula to historical events, insureds can better understand how the policy would have responded in past scenarios. Triggers and payout structures can then be adjusted to better match the insured’s specific risk exposure and financial needs.

To provide a quote, we need:

• Insured location: address, GPS coordinates, or shapefiles
• Perils to be covered
• Risk period: annual, multi-year, or short term
• Requested sum insured
• Loss history: past losses from the relevant perils and associated amounts

Additional information (such as asset construction details for property covers, or yield history for agricultural covers) may be requested depending on the peril.

Typical timelines from first conversation to binding range from as little as one day for simple, standard structures to 8 to 12 weeks for a complex multi-site or multi-peril program. The main drivers of timeline are:

• How quickly the broker and client provide complete exposure data
• The complexity of the trigger design
• Internal client approval processes

For renewals of existing parametric covers, binding can often be completed in less than 2 weeks if structure and exposure are unchanged.

Yes. Parametric policies can be structured as annual, multi-year, or short-term covers. Long-term agreements are particularly common where clients want price stability and where the trigger design is well calibrated. Because parametric pricing is based on physical risk rather than reinsurance market cycles, multi-year pricing is often more predictable than for traditional cover. 

Yes. Descartes' business development team works directly with brokers, and where appropriate with end clients and lenders, to explain how indices are designed, how payouts are calculated, and how parametric cover integrates with existing programs. The team has experience across 60+ countries, and underwriters can be involved for technical discussions including index design, modeling, and payout structures. 

Four pitfalls come up most often:

1. Treating parametric as a like-for-like replacement for traditional cover. Parametric is most effective when its specific strengths (speed, transparency, NDBI, hard-to-insure perils) are matched to client needs, rather than benchmarked solely on premium.
2. Underestimating the importance of data quality at the insured location. A weak data source produces a weak trigger.
3. Skipping the back-test. Failing to test the proposed index against historical losses leaves both parties exposed to avoidable basis risk.
4. Not aligning internal stakeholders early. Parametric typically requires CFO or treasurer involvement, not just risk management, because the cash-flow benefit is part of the value.

Descartes Insurance and Descartes Underwriting are two entities within the Descartes group, with distinct regulatory positions and product offerings.

• Descartes Underwriting writes parametric (re)insurance globally, with capacity of up to €80M to €140M per policy depending on peril and geography.
• Descartes Insurance is a regulated insurer operating in the EEA, writing indemnity-based products including PDBI, Technical Risks, Hail, Cyber, and CPRI, with capacity of up to €10M to €25M per policy.

Descartes Insurance differentiates itself from traditional insurers through three core pillars:

• Parametric insurance pioneers: Descartes has over 8 years of experience specializing in parametric insurance. This track record gives their underwriters a much deeper understanding of how to structure these products compared to newer competitors.
• Heavy focus on science & data: Their risk models aren't outsourced; they are built in-house by a team of over 150 PhDs, data scientists, and engineers. This allows for highly accurate, data-driven pricing for both climate and traditional risks.
• Highly adaptable products: Instead of rigid, off-the-shelf policies, Descartes designs flexible coverage for complex areas like cyber, technical risks, and credit, making them far more adaptable to specific client needs than legacy insurers.

Descartes Insurance, as a full-stack insurer licensed in the EEA, is authorized to operate and issue policies directly in the following countries and territories:

• France, including DOM-TOM and New Caledonia
• French Polynesia
• Most EEA countries, specifically: Germany, Austria, Belgium, Spain, Netherlands, Luxembourg, and Italy

The company is continuing to grow its footprint, and the expansion of its authorizations to operate in other EEA countries is ongoing. 

Descartes Insurance offers indemnity-based products in the EEA across the following lines: PDBI (Physical Damage Business Interruption), Technical Risks, Indemnity Hail, Cyber, and CPRI (Credit and Political Risk Insurance). 

Descartes’ Cyber product is a parametric cyber insurance solution designed around predefined triggers and fast payouts. It covers key cyber exposures such as business interruption, incident response costs, and optional cyber liability, with indemnification occurring in days rather than months. The product is fully flexible and can be structured as primary cover, excess layer, or integrated into more complex arrangements such as captives or virtual captives. It is designed to provide fast liquidity, reduce claims uncertainty, and adapt coverage to each company’s operational model. 

Descartes Insurance supports captives through structured solutions that can be embedded into captive programs. This allows captives to manage risks with pre-agreed triggers, predictable payouts, and reduced claims volatility. 

The solution can be structured in several ways:

• Risk transfer layer: Parametric coverage acts as excess protection or structured coverage sitting alongside the captive program, improving overall capital efficiency and stability.
• Direct integration into the captive program: Parametric solutions can be embedded directly into the captive structure, helping smooth loss volatility and reduce retained exposure.
• Captive fronting: Descartes Insurance can act as a fronting carrier for captive programs, providing the regulatory and rating framework needed to access parametric capacity.

Across all structures, captives benefit from faster access to liquidity after events, clearer financial planning, and the ability to expand capacity for perils that are otherwise difficult to retain, including cyber risk. 

Descartes provides risk modeling, structure design, fronting, reinsurance, and DIC/DIL solutions for any type of captive program. By combining competitive fronting fees with advanced climate expertise, we design tailored covers that empower captives to maintain greater control over their risk management.