Polymarket and Kalshi, two of the largest real‑money prediction platforms, are quietly overhauling how they police trading behavior as political and regulatory pressure on the industry intensifies. Both companies rolled out new measures on Monday aimed squarely at a problem that has long haunted markets of every kind: insider trading.
The timing is not accidental. Prediction markets have drawn increasing fire from Democratic lawmakers, who argue that some contracts-particularly those involving war, geopolitical conflict, or potential mass casualties-amount to gambling on human suffering. Critics in Washington have also raised concerns that markets on elections and policy decisions could turn sensitive government information into a tradable asset for insiders.
Against that backdrop, Polymarket announced a sweeping update to its integrity rules, spelling out in much sharper detail what counts as illegal or unacceptable trading conduct on the platform. The company clarified that trading based on non‑public, material information-whether obtained directly or through illicit “tips”-is strictly banned on both of its products: its decentralized finance (DeFi) prediction platform and its U.S.‑facing platform, which operates under oversight from the Commodity Futures Trading Commission (CFTC).
The firm framed the move as less of a policy shift and more of a codification of standards it says have been in place for some time. By tightening the language and providing concrete examples of prohibited behavior, Polymarket is trying to remove any ambiguity about what users can and cannot do. The stated goal is to make expectations “abundantly clear” to every participant and to underscore that the company has already invested in compliance systems, rather than scrambling to build them in response to the latest wave of attention.
Kalshi, which runs a fully regulated U.S. prediction exchange that lists markets on topics ranging from inflation reports to sports outcomes, is taking a more technical tack. Instead of leading with new public rule language, the company is emphasizing upgrades to its internal tools for monitoring trading patterns. These enhancements are designed to flag suspicious activity-such as unusual spikes in volume or highly profitable positions opened just before new information becomes public-that might indicate someone is trading on inside information.
Though the two approaches differ, the direction of travel is the same: prediction markets are moving closer to the surveillance and enforcement model long associated with traditional financial exchanges. Where once the space marketed itself primarily as an experiment in decentralized information discovery, it is now increasingly expected to operate under the same expectations of market integrity that govern stocks, commodities, and derivatives.
The political climate is accelerating that transition. Democratic lawmakers have in recent months floated proposals to restrict or outright ban certain classes of prediction markets. Contracts linked to outcomes like wars, terror attacks, or large‑scale disasters have drawn particular ire, with opponents arguing that they normalize or even incentivize catastrophic events. Markets around elections and governance, meanwhile, have sparked debate about whether public servants with advance knowledge of policy decisions might quietly profit on these platforms.
For regulators, the core concern is familiar: when markets exist on events that can be influenced-or anticipated-by people in positions of power, those same people might be tempted to trade on privileged information. Prediction markets, by design, turn opinions about future events into prices. That makes them efficient aggregators of beliefs, but also potentially attractive tools for anyone holding confidential data about upcoming government reports, classified military actions, or sensitive corporate decisions.
Insider trading in this context can take multiple forms. A government official who sees an embargoed economic report ahead of time and takes a large position in a related inflation or unemployment market would be a textbook example. So would an aide who, aware of an impending policy announcement, tips off a friend who then places trades. Even a contractor with access to non‑public scheduling details-such as the timing of a ceasefire negotiation or a central bank speech-could, in theory, use that knowledge to gain an edge in relevant prediction markets.
Polymarket’s updated integrity rules appear designed to capture these edge cases. By explicitly naming “illegal tips” and other forms of second‑hand inside information, the company is signaling that it intends to hold traders responsible not just for how they trade, but for the nature of the information they rely on. That brings its public rulebook more in line with the standards used in securities law, where both tippers and tippees can face liability.
On the enforcement side, both Polymarket and Kalshi are likely to lean heavily on pattern‑recognition tools. Modern surveillance systems can analyze order books, trade timing, and position histories across thousands of markets in real time. By comparing trading behavior around key news events or data releases, exchanges can identify accounts that consistently get in or out just before price‑moving information hits the public domain. Those accounts can then be subject to additional review, questioning, or even suspension.
For users, this evolution cuts both ways. On one hand, stricter rules and more active enforcement can increase trust in the fairness of the markets. Many traders are wary of participating in platforms they suspect are rigged in favor of insiders. Making it clear that privileged information cannot be exploited-at least not without scrutiny-can help prediction markets attract more volume and a broader user base, including institutional players that demand robust compliance.
On the other hand, the tightening environment could narrow the type of information that can be safely used for trading. Prediction markets have long celebrated the idea that “local” or specialized knowledge-from an industry expert, a civil servant, or someone with on‑the‑ground experience-can improve price accuracy. The line between legitimate expertise and illegal insider information is not always obvious. Stricter enforcement may cause some would‑be traders to steer clear of markets where they worry their informational advantage might be misconstrued as illicit.
A key challenge for platforms will be maintaining the benefits of information aggregation while staying well within legal boundaries. One approach is education: clearly explaining to users what constitutes material, non‑public information in their jurisdiction, and encouraging traders to rely on publicly accessible sources, models, and analysis rather than confidential data. Another is careful market design, avoiding contracts that are too tightly linked to single, easily manipulable decisions by identifiable officials.
The regulatory footprint also differs sharply between the two companies. Kalshi operates as a fully regulated exchange in the United States, subject to the CFTC’s established frameworks for event contracts and risk controls. That status requires the company to maintain detailed compliance programs, report suspicious activity, and work closely with regulators when potential violations surface. Its tooling upgrades are thus not just a business decision, but a regulatory expectation.
Polymarket straddles two worlds. Its DeFi platform embodies the ethos of open, permissionless trading, where smart contracts and blockchain rails replace centralized intermediaries. At the same time, its U.S. platform operates under CFTC oversight, forcing it to marry crypto‑native design with the compliance disciplines of traditional finance. Updating its integrity rules across both fronts is part of this balancing act: reassuring regulators and politicians without abandoning the innovation that made the platform popular.
Looking ahead, the industry is likely to see more, not fewer, moves in this direction. As volumes grow and the outcomes listed on prediction markets increasingly intersect with public policy, elections, and national security, scrutiny will intensify. Platforms that want to survive in major jurisdictions will need to show that they are not havens for insider abuse or tools for monetizing confidential state information.
At the same time, the debate over what kinds of markets should exist at all is far from settled. Critics argue that some contracts are inherently unethical, no matter how well‑policed they are. Supporters counter that prediction markets-when designed responsibly and monitored for abuse-can improve forecasting, inform decision‑makers, and provide valuable signals about the likely consequences of policy choices.
In that broader argument, the insider‑trading question sits at the intersection of ethics, law, and market design. If platforms like Polymarket and Kalshi can demonstrate robust defenses against illicit use of information, they gain a stronger case for their legitimacy in the eyes of regulators and the public. Their latest rule changes and tooling upgrades are early tests of whether the prediction‑market model can adapt to a world where both the stakes and the scrutiny are rising.