Julia R. Cartwright is a senior research fellow in law and economics at the American Institute for Economic Research.
Enron, long remembered as one of the largest corporate fraud scandals in American history, now looks small by comparison. Recent revelations of billions of dollars in fraudulent spending uncovered in Minnesota are a local glimpse of a far greater problem: federal fraud totaling hundreds of billions every year.
Enron was exposed not by an inspector general, a task force or a compliance office but by markets. Investors such as Jim Chanos saw that Enron’s numbers didn’t reflect economic reality and made hundreds of millions betting against the firm. These bets point to a deeper truth: In the private sector, fraud distorts prices, and markets respond by rewarding those who uncover it early. Using that same price-based logic, prediction markets can be harnessed to surface private information and stop fraud at scale.
Prediction markets, which surged into public view during the 2024 U.S. election cycle, are trading platforms that use prices to forecast events. These markets aggregate private information into a single price by allowing participants to trade contracts tied to expected outcomes. On platforms such as Polymarket and Kalshi, prices between $0 and $1 reflect the market’s implied probability: 75 cents implies a 75 percent chance, updating in real time as new information arrives and converting local, tacit knowledge into a public forecast. When the event resolves, the winning contract pays out.
These markets recently made headlines for apparent insider trading, most notably a large payout shortly before Venezuelan President Nicolás Maduro’s capture in Caracas. But trading on private information is not a bug of prediction markets; it’s the feature. Markets work precisely because they make it profitable to reveal private information through trade.
Government contracting has no equivalent mechanism: There is no continuous signal indicating whether a $300 million contract is clean or rotten. Oversight is episodic and backward-looking, and by the time fraud is formally detected, the money is gone. As a result, detecting government fraud is far more cumbersome and far less rewarding than uncovering fraud in private markets.
The United States maintains a sophisticated yet ineffective enforcement apparatus: Inspectors general audit, the Justice Department prosecutes, and the False Claims Act deputizes insiders to bring lawsuits, offering whistleblowers 15 to 30 percent of recovered funds. Yet even by generous estimates, this system uncovers only about 3 percent of total fraud, in large part because investigations are slow, costly and uncertain. The rest slips into the noise, siphoning taxpayer dollars and eroding public trust.
The problem is not a lack of legislation; it’s a lack of prices.
Applying prediction-market logic to public fraud requires three steps. First, all government contracts, grants and major spending programs above a statutory threshold should be publicly disclosed in a database with contract identification numbers linked to contract awards, payments and any contract modifications. Second, Congress should pass a statute that prediction markets may list contracts tied to whether fraud, overbilling or material misrepresentation is later proven in those public contracts, without treating such trading as gambling or unlawful speculation. Third, Congress should require a small portion of each contract to be set aside as a fraud bounty, which acts as a reward for catching fraud within the prediction market.
With disclosure and legal clarity in place, prediction markets compete for the right to host the government contract bet on their platform. They award the fraud bounty and design the most informative private contracts. Markets then list instruments that pay out if, for example, an inspector general or the Justice Department later confirms fraud above a defined threshold by a specified date and pay zero otherwise. Outsiders and insiders place bets based on their insights, producing continuous, public price signals that aggregate dispersed private knowledge in ways audits and compliance checks cannot.
Those prices function as a bellwether for the existing enforcement system, especially the False Claims Act. The act operates as a bounty mechanism, but insiders often act with limited visibility into whether others share their concerns. Persistently high fraud probabilities implied by the market reduce that uncertainty, signaling that suspicions are widely held and that the expected value of a fraud investigation may justify the personal and professional risk of filing a suit. Markets do not replace whistleblowers; they guide and embolden them.
At the same time, investigators, congressional overseers and the press use these prices to triage attention and identify contracts slipping past traditional oversight. The innovation is continuous price discovery, not gambling. Each contract generates a live, quantitative signal of suspected misconduct that updates as information emerges. When fraud probabilities cross a high threshold, contracts are flagged for heightened scrutiny or require justification for inaction, shifting oversight from episodic and reactive to continuous and anticipatory.
Fraud persists in government not because people don’t know where it is but because knowing is not enough. Markets succeed where bureaucracy fails because they reward being right early and punish being wrong loudly. If we are serious about stopping fraud at scale, we should stop pretending audits and whistleblowers alone can do what prices were invented to do.
Fraud hates sunlight. It hates prices even more.
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