Predicting the Insurability of Prediction Markets

Could a market that facilitates millions in wagers on how many times Vice President JD Vance claps during a State of the Union address, or whether Elon Musk will finally be unmasked as Satoshi Nakamoto—the elusive, pseudonymous creator of bitcoin—actually provide a serious hedge for global insurers?

Although the insurance industry has always been in the business of calculating the odds, it has long been uneasy with the optics of betting. But as prediction markets like Polymarket and Kalshi seize global headlines with such high-volume wagers, a provocative question is stirring: If these platforms can price the hyper-specific behavior of a politician or the identity of a crypto-founder, could they also provide a new layer of capacity for catastrophic threats and more mundane risks?

The answer appears to be yes.

The transition from a speculative wager to a legitimate layer of insurance or reinsurance protection for large businesses and insurance carriers has entered its preliminary phases, with parametric event contracts like hurricane and seasonal storm prediction contracts available on Polymarket, Kalshi and Interactive Brokers. Winning trades are triggered by objective data like a hurricane’s wind speed and/or its geographic location.

While this event-contract approach offers speed and transparency, its core challenge lies in reconciling volatile, crowd-sourced sentiment with the disciplined, data-driven rigor of the actuarial profession. Nevertheless, sources interviewed for this article suggest value in heeding the wisdom of the crowd, with several observers maintaining that the financial stakes involved in prediction markets force a level of objective forecasting that traditional underwriters fail to replicate.

“I think a market is possible, but we’re still in the early days,” said Sridhar Manyem, senior director of industry research and analytics at rating agency AM Best. “Increasing capacity is a good thing, so long as there is certainty the product will deliver.”

In prediction markets, the “house sets the odds” model common in gambling is replaced by a peer-to-peer market in which participants trade binary contracts on whether an event occurs. Each contract represents a single outcome, Yes or No, where the trading price reflects the real-time probability of the event. For example, if a contract for a specific rhetorical trope from President Trump—such as his frequent use of the word “hottest” in a campaign rally—trades at $0.65, the market is signaling a 65% chance of occurrence. Similar to binary options, the contracts pay out a fixed $1.00 if the event happens and drop to zero if it does not, providing a 35-cent profit per share for successful Yes traders.

By using predefined data thresholds to automate payouts, the platforms convert complex risks like earthquakes or tornadoes into simplified, binary formats. Instead of navigating multi-layered insurance terms and conditions, traders use straightforward contracts that settle based on objective third-party data from agencies like NOAA and USGS. This eliminates the need for traditional claims adjustment, allowing for settlement to occur within hours of the data being published.

Trading on the likelihood of future events, speculators effectively create a real-time price for uncertainty. For organizations facing specific exposures—such as a utility company vulnerable to storm damage—the platforms are touted as a practical risk transfer tool. By purchasing Yes contracts on a potential disaster, the utility can create a financial hedge that pays out if the event occurs, helping to further offset its actual losses.

“Prediction markets for natural disaster risk function as a live, flexible complement to traditional insurance by pricing contracts based on the real-time probability of an event of a given magnitude,” said Patrick Brown, climate scientist and head of climate analytics at Interactive Brokers. The trading platform’s subsidiary, ForecastEx, is a U.S.-regulated exchange that began offering CFTC-approved hurricane prediction contracts in 2025.

“By aggregating diverse, decentralized information from participants incentivized to be accurate, the platforms create a continuous forecast of high-stakes events such as natural disasters or geopolitical shifts,” Brown said, providing the example of hurricane-prone Miami-Dade County in Florida. “If historical data suggests a 10% annual chance of a Category 3 hurricane, a Yes contract would initially trade at about 10 cents to pay out $1.00 upon occurrence. Because the markets pool societal information and align financial incentives, the price shifts as participants ‘speak with their dollars’ to reflect the most accurate probability.”

Put another way, if heightened demand for disaster hedging drives the price to 15 cents against the 10% baseline, the corresponding No contracts become mathematically undervalued. This creates a profitable entry point for speculators seeking to capture the variance between historical data and current market sentiment—a price discrepancy that exists because the hedger is prioritizing safety over pure odds.

“As the Yes bid rises above 10%, the No bid moves in the opposite direction. You’re even willing to pay more as a hedger than what you think the actual price is because you are paying to transfer the risk,” said Brown. “Under liquid market conditions, participants can instantly adjust or close their positions based on new information, such as a shift in a weather model, in a way that static insurance policies do not allow.”

A Minor Player for Now

While prediction markets are growing rapidly, they remain a fraction of the insurance-linked securities (ILS) market. In February 2026, Polymarket and Kalshi combined for over $18 billion in monthly trading volume, a massive jump from the under $2 billion they saw just six months prior. By contrast, the ILS market—the established vehicle for securitizing major disaster risks for institutional investors—hit a record $61.3 billion in early 2026. (Source: Q42025 Catastrophe Bond Market report, Artemis, bonds outstanding as of 12/31/2025)

This disparity in market size reflects a fundamental difference: Whereas prediction markets track the sheer volume of money changing hands—much like the total bets placed at a racetrack window—the ILS market measures the actual safety net of cash held in trust to pay out claims. In other words, the ILS market continues to offer a far deeper pool of locked, guaranteed capital dedicated to covering major losses.

Nevertheless, the gap is projected to narrow now that Polymarket, Kalshi, Interactive Brokers and other platforms offer live contracts that mirror parametric triggers. Since these contracts settle based on objective data rather than a manual claims adjustment, payments reach the policyholder within hours of a triggering event.

“The speed of prediction markets is something that insurers would find especially compelling,” said Benedict Altier, chief operating officer at Cactus Risk Studio. The firm’s Catamaran platform allows institutional investors to trade parametric Yes and No contracts on hurricane landfall and intensity. “Pricing is real-time and instant, not structured around traditional renewal cycles. That’s a completely different tempo, and it gives underwriters a live read on how risk is shifting rather than waiting months for the next data point.”

Opinions remain mixed on whether insurance carriers can truly replace or augment traditional reinsurance capacity with binary contracts. Altier is among the optimists, noting that the recent explosion of market interest signifies that there is finally enough volume for the industry to take note.

To Altier, the platforms deliver on three fronts where the traditional ILS market has often struggled: transparency, liquidity and continuous price discovery. By functioning on objective data rather than historical models, prediction markets provide an earlier signal of emerging threats before they are reflected in static datasets.

“The speed of prediction markets is something that insurers would find especially compelling.”

Benedict Altier, Cactus Risk Studio [/sidebar]

“Risk modeling companies are slow to update in response to new threats, and insurers are even slower to adopt those versions,” he explained. “Data from prediction markets complements these traditional approaches by giving insurers a live read on how risk is being priced by the broader market. This real-time calibration allows them to refine their internal forecasts against live data.”

Institutional Hurdles

Despite the clear advantages of speed and transparency, the transition from speculative trading to institutional risk management faces significant headwinds. While prediction markets excel at price discovery, they currently lack the formal credit and security that the global insurance ecosystem requires. “Conceptually, what is happening is that the nature of the trades on Polymarket can be fundamentally the same as what reinsurers and insurers do from a parametric perspective, [but] there are a couple of different dimensions to consider,” said Cole Mayer, managing director and head of parametric at insurance broker Aon.

Mayer explained that an insurer or corporate entity choosing to access either traditional reinsurance markets or prediction markets must assess two different scenarios: payment reliability and regulatory capital credit. The first is a matter of capital stability—specifically, the certainty of payment. While agencies like AM Best and S&P provide a high degree of confidence in the solvency of traditional insurers and reinsurers, the creditworthiness of prediction market platforms remains unrated.

However, concerns over capital availability from prediction markets are premised on traditional betting models where a bookmaker’s solvency may be questionable. In contrast, prediction markets require full collateral for every trade. Platforms like Kalshi and ForecastEx hold customer funds in segregated accounts, preventing their use for operational costs. By locking the total potential payout in escrow, the markets effectively eliminate counterparty risk, ensuring that winning participants are paid regardless of the platform’s operational health.

On decentralized platforms like Polymarket, funds are managed by smart contracts to minimize human interference, though this shift toward automation introduces technical risks where code bugs or security breaches could potentially impact the accessibility of winning capital.

Regarding his comment on regulatory capital credit, Mayer explained that regulators allow an insurance carrier to reduce mandatory reserve capital by transferring risk to a certified reinsurer. In other words, by essentially “renting” a reinsurer’s balance sheet, an insurer “can write more business on the front end and receive regulatory capital credit for its reinsurance structures.” This credit “opens up the balance sheet to take on more risk,” he said. “It’s unlikely that regulatory credit would be granted to an insurer using the platforms at their current stage of development.”

A deeper philosophical divide remains over whether the wisdom of the crowd can truly replicate the rigor of actuarial science. Critics argue that market sentiment—no matter how well-incentivized—is a poor substitute for the empirical modeling that underpins the global insurance industry.

Former California Insurance Commissioner Dave Jones, for instance, questions whether prediction markets are “fit for purpose.” He noted that when an insurance company enters into a contract with a reinsurer, it relies on modeling the likelihood of a particular physical event occurring based on millions of simulations and sophisticated computational models.

“That’s very different from prediction markets, which are essentially based on what everyone is betting in a market regarding an event occurring or not occurring,” Jones said. “It’s very different from the kind of empirically based probabilistic modeling that insurers use to determine how to price reinsurance, a cat bond or a parametric policy.”

Economist Robert Hartwig offered a similar perspective, noting that insurers base their purchase decisions on complex negotiations over terms and conditions that prediction markets currently lack.

“The prediction markets are pretty inflexible when it comes to things like retention levels and coverage limits,” said Hartwig, clinical associate professor of finance and director of the Risk and Uncertainty Management Center at the University of South Carolina. “It’s not just the probability of a hurricane making landfall; it’s a negotiation about what the retention is going to be, what the limits are going to be, and what is available for retrocessional reinsurance.”

Opportunities Beckon

The recent influx of professional capital and institutional participation undoubtedly has shifted the perception of prediction markets from speculative curiosities into essential components of global financial and information infrastructure. Altier pointed to professional brokerages like Interactive Brokers launching its regulated prediction products and hedge funds and proprietary trading firms like Jump Trading and Susquehanna serving as market makers to provide the liquidity needed for large-scale hedging by insurance companies. Major institutions like Intercontinental Exchange, a Fortune 500 company that operates 13 regulated exchanges and six clearinghouses worldwide, also have made multibillion dollar strategic investments in the space.

While the platforms continue to build upon this institutional infrastructure, their utility is expanding to the retail level, where a more immediate application for prediction markets is emerging for individual policyholders reeling from a historic surge in property insurance premiums and a retreating private market. According to J. Robert Hunter, a former Texas insurance commissioner and director of insurance for the Consumer Federation of America, the platforms offer a streamlined mechanism for an average homeowner to hedge against basis risk—the financial gap created by ballooning deductibles and excluded losses.

Hunter noted that while traditional homeowners policies are becoming increasingly restrictive, prediction markets provide a transparent, binary alternative for those in high-risk zones. “If you’re a homeowner in Florida, your deductible might be $10,000 or more, and your policy likely excludes certain types of water or wind damage,” Hunter explained. “By purchasing a ‘Yes’ contract on a hurricane, a homeowner can effectively create their own supplemental coverage that pays out instantly, providing the liquidity needed to begin repairs while they wait months for a traditional insurance settlement.”

However, Hunter warns of the potential for predatory pricing in unregulated markets. “The challenge is ensuring that these platforms are seen as a regulated financial tool with clear consumer protections,” he said. “Without oversight, the very people these markets are meant to help could find themselves on the losing side of a very expensive transaction.”

Beyond natural catastrophes, reports suggest that outdoor event organizers and farmers are relying on CFTC-regulated global event contracts to offset losses from adverse weather. Similarly, shipping companies are using them to buffer fuel price spikes caused by geopolitical instability, such as the current conflict in Iran. This form of risk transfer allows for highly customized protection that traditional carriers may find too slow or expensive to underwrite.

Prediction markets are also offering event contracts to travelers through automated, on-chain logic—pre-programmed code that replaces manual claims processing with immediate liquidity. For instance, by separating risk pricing from capital provision, a passenger can hold a contract that automatically triggers a digital wallet payment the moment a flight delay is verified by a third-party data feed.

Other frequent disruptions, ranging from wedding cancellations to project delays or the loss of key personnel, are also being managed through the same real-time market consensus. This suggests a future of hybrid risk management, where dynamic exchanges provide immediate liquidity alongside the broader, long-term security of standard insurance policies.

“The ultimate goal of insurance has always been to spread the burden of risk as broadly as possible,” said Altier. “Diversifying the capital base is essential if we’re going to bridge protection gaps and improve market stability, and prediction markets offer a powerful way to open up risk to a broader pool of participants. The infrastructure to make that happen efficiently and at scale is being built right now. There’s a long road ahead, but if this market matures, it would be genuinely transformative for insurance.”