Now, this phenomenon has a name—or at least a category: information laundering. To understand why it is so destructive, one must first grasp the essence of predicting market prices, because the mechanisms that enable these markets to function are precisely the same ones that make them vulnerable to exploitation.

Strip away the cryptographic veneer, and PM contracts are remarkably simple. Each share yields $1 if the prediction is correct, nothing otherwise. Since each binary question has only two outcomes, a "yes" share plus a "no" share always equals $1; thus, a "yes" share priced at $0.36 implies the market assigns a 36% probability to the prediction being true.

Crucially, Polymarket does not set these prices. They emerge from a continuous order book (CLOB). Prices are determined by supply and demand between traders, with displayed prices located at the midpoint of the bid-ask spread. This is perhaps their elegance. In this model, prices are not opinions from a betting house but collective expectations derived from all participants in the order book. When new information emerges—such as a strong jobs report or CPI data below expectations—traders reprice, and prices adjust accordingly. In effect, the market becomes a continuously updated probability estimate, one that institutions are willing to pay for. Bloomberg, Reuters, and hedge funds now purchase real-time access to Polymarket’s data feeds, treating them as faster indicators of market sentiment than traditional polls.

Yet the trap lies here: a system designed to convert information into prices cannot distinguish between public information and stolen intelligence. The order book does not ask where your edge comes from—it only records that you bought.

This is where the term “laundering” fits perfectly. In traditional money laundering, dirty cash flows in one end and clean, untraceable cash flows out the other. In information laundering, confidential intelligence flows in one end, and market prices emerge from the other—without any trace.

For example, suppose someone knows a strike will occur in 48 hours, while the current market price stands at 15%. Their buying pressure consumes all sell orders in the order book, pushing the midpoint up—say, to 35%. To others, this appears merely a routine repricing, as if some trader suddenly gained exceptional geopolitical insight. The secret is skillfully packaged into a clear signal. When the strike occurs, the YES contract surges to $1. A position bought near $0.15 yields roughly a 6.7x return. The Maduro case months earlier demonstrated such scale clearly: prosecutors alleged the army sergeant turned approximately $34,000 in bets into about $400,000.

The laundering metaphor also applies to obscuring truth. Bubblemaps found that an Iranian criminal syndicate incurred losses amounting to only a few hundred dollars—so minimal that the company believes they were deliberately incurred to mislead investigators. A 98% win rate looks extraordinary; paired with a few negligible, intentional losses, it appears nearly indistinguishable from that of a highly skilled trader.

Yet the most ironic twist is that these markets are more transparent than traditional exchanges. Even if account holders remain anonymous, every transaction is recorded on a public ledger. It is precisely this openness that enables analysts to use tools like Bubblemaps to reconstruct conspiracies—such as one involving nine wallets—by analyzing temporal correlations and trading volume, including trades recorded days before a market-moving event on February 28.

But the same transparency introduces a secondary risk that alarms regulators. If external analysts can detect a coordinated group heavily betting on an attack, so too can hostile actors. Adversarial observers can identify anomalous trades and use them to craft war plans and forecast market movements. Certain spikes in war markets serve as low-cost, deniable intelligence sources for anyone monitoring the chain. The launderer cleans their information, but as a side effect, they broadcast the original secret in abstract form to the world.

Why doesn’t existing law simply cover this scenario? Because traditional insider trading rules are built around stocks, material non-public information tied to companies, earnings, mergers, executive disclosures—not the timing of military operations. There is no “issuer,” and legally speaking, no insider within a corporation.

Geographic jurisdiction compounds the issue. U.S. federal law prohibits prediction markets from offering wagers on war or assassinations—but Maduro’s bets were placed on Polymarket’s offshore site, outside these restrictions. Moreover, the barrier to entry is laughably low: a mere $2 per month for a VPN easily circumvents U.S. bans. Even a KYC-verified account can be purchased outright. Nevertheless, Washington has finally taken notice. On May 22, the House Oversight Committee launched a formal investigation into prediction markets, demanding records on how identities are verified, geographic restrictions enforced, and suspicious transactions linked to Venezuela and Iran handled. Proposed legislation—the Death Bet Act and the Financial Prediction Market Public Integrity Act—aims to ban war-related bets and prohibit officials from trading on non-public information.

The harsh reality is that information laundering is not a man-made flaw in prediction markets, but rather a side effect of their core operational mechanism. A market capable of perfectly converting knowledge into price inherently rewards those who possess the best information—including those who should not have it. Without weakening the very mechanisms that make these markets more accurate than opinion polls, the loophole cannot be fully closed.

As the industry looks ahead, even just 1–2% adoption among derivatives traders using these tools could push annual trading volume to $50 billion. The question is no longer whether prediction markets are effective—but whether they are too effective. The real concern is whether society can tolerate a machine that transforms society’s most closely guarded secrets into publicly traded digital assets, paying substantial rewards to those who hold them.

Author: Polyfactual

Translated by: Hu Tao, ChainCatcher