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, as the very mechanisms that enable these markets to function are also what make them vulnerable to exploitation.

Strip away the cryptographic veneer, and PM contracts are remarkably simple. Each share pays $1 if the prediction is correct, nothing otherwise. Since every binary question has only two outcomes, one "yes" share plus one "no" share always equals $1, so a "yes" share priced at $0.36 indicates a 36% probability that the market assigns 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 sitting at the midpoint of the bid-ask spread. This is perhaps their elegance. In this model, prices are not opinions handed down by a betting house but collective expectations distilled 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, for which institutions are willing to pay. Bloomberg, Reuters, and hedge funds now purchase real-time access to Polymarket’s data feeds, treating them as faster, more responsive indicators of market sentiment than traditional polling.

Yet the trap lies in the fact that a system designed to convert information into prices cannot distinguish between public information and stolen information. The order book doesn’t inquire about the source of your edge—it simply records that you bought.

That’s when the term “laundering” becomes apt. In traditional money laundering, dirty cash flows in one end and clean, untraceable cash flows out the other. In information laundering, confidential information flows in one end, and price signals flow out the other—without any trace.

For example, suppose someone knows a strike will occur in 48 hours, while the market currently prices the probability at 15%. Their buying pressure will absorb every sell order in the order book, pushing the midpoint up—for instance, increasing the contract price to 35%. To others, this looks like a routine repricing, as if some trader suddenly gained exceptional geopolitical insight. The secret is cleverly packaged into a clear signal. When the strike actually happens, the YES contract price jumps to $1. A position bought near $0.15 yields a return of roughly 6.7x. The Maduro case months earlier vividly illustrated this scale: prosecutors alleged that an army sergeant turned approximately $34,000 in bets into around $400,000.

The laundering metaphor also applies to obscuring the truth. Bubblemaps discovered that the Iranian criminal syndicate’s losses were minimal—just a few hundred dollars—and concluded these losses were deliberately incurred to mislead investigators. A 98% win rate looks extraordinary, but combined with a few negligible, intentional losses, it appears almost indistinguishable from a top-tier trader.

Yet the most ironic twist is that these markets are actually 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 a network involving nine wallets—by analyzing temporal patterns and trading volume, for instance, transactions recorded days before a key expiration event on February 28.

But the same transparency introduces a secondary risk that regulators deeply worry about. If external analysts can detect coordinated betting on an attack, so can adversarial actors. Hostile observers can identify anomalous trades and use them to formulate war plans and forecast market movements. Unusual spikes in certain war markets serve as low-cost, deniable intelligence sources for anyone monitoring the chain. The launderer cleans their information, while as a side effect, they broadcast the original secret in abstract form to the entire world.

Why can’t existing laws simply cover this scenario? Because traditional insider trading rules are built around stocks, material non-public information related to companies, earnings, mergers, executive disclosures, and similar concepts—not around the timing of military operations. There is no “issuer,” and legally, no corporate insiders.

Geographic jurisdiction compounds the issue. U.S. federal law prohibits prediction markets from offering bets on war or assassinations, but Maduro’s bets were placed on Polymarket’s offshore website, outside those restrictions. Moreover, entry barriers are laughably low—just a monthly fee of around $2 for a VPN suffices to bypass U.S. prohibitions. Even a KYC-verified account can be purchased outright. Still, Washington finally took 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 tied to Venezuela and Iran are handled. Proposed legislation—the Death Bet Act and the Financial Prediction Market Public Integrity Act—aims to ban war betting and prohibit officials from trading on non-public information.

The harsh reality is that information laundering is not an artificial loophole in prediction markets; it is a byproduct of their core operational mechanism. A market capable of perfectly converting knowledge into price inherently rewards those with the best information—including those who should not have it. Without weakening the very mechanisms that make these markets more accurate than polls, the hole cannot be fully sealed.

As the industry looks ahead, even if only 1–2% of derivatives traders adopt these tools, annual trading volume could surge 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 tightly guarded secrets into publicly quoted, tradable digital assets—and pays substantial rewards to those who hold them.

Author: Polyfactual

Translated by: Hu Tao, ChainCatcher