Prediction Markets Are Not Truth Machines: AML and Seven Structural Barriers

Prediction markets are rapidly becoming strategic decision-making tools across many fields, from politics to finance. However, behind the promising numbers lie deep flaws that most users fail to recognize. The issues are not only related to familiar management challenges but also stem from structural errors within the market’s operational mechanisms. This article will analyze the barriers that cause prediction markets to frequently deviate from their original mission and explain why current solutions, including AML (Anti-Money Laundering) frameworks, are still insufficient to fully address these problems.

Operating Mechanisms and Misguided Assumptions

Prediction markets operate on a seemingly simple principle: participants trade contracts related to the outcomes of future events. A contract is priced between 0 and 1 dollar, with the market price generally interpreted as the probability of that event occurring. For example, if a contract on “Donald Trump winning” trades at $0.70, the market is effectively signaling a 70% chance of this outcome.

These platforms exist in various forms. Sites like PredictIt specialize in political predictions, allowing users to bet on election results and public policies. Kalshi, regulated by the CFTC (U.S. Commodity Futures Trading Commission), offers markets for economic indicators and geopolitical events. In the Web3 ecosystem, Polymarket and Augur operate on blockchain, using smart contracts to automate transactions and payouts.

The appeal of this model lies in its incentive mechanism: participants must put real money at risk, creating predictions with “skin in the game.” Unlike opinion polls where respondents merely express views without consequences, prediction markets require actual financial risk. The theory is that this mechanism filters out casual predictors, leaving those with more accurate information. Prices are continuously updated as new information emerges, providing a “live” signal about the future.

However, these assumptions only hold under certain ideal conditions. When those conditions are broken, prediction markets become more distorted tools than “truth detectors.”

Seven Structural Flaws Hindering Market Effectiveness

1. The “Fool’s Money” Problem – The Liquidity Trap

Every market needs a balance between professional traders and ordinary investors. If only professionals participate, no one will want to trade. Small investors provide trading volume and liquidity, acting as “catalysts” to attract experts.

The problem is that most prediction platforms struggle to recruit enough of these small investors. Complex registration processes, extensive KYC (Know Your Customer) requirements, and strict AML measures often cause casual users to give up before trading begins. This creates a vicious cycle: insufficient retail liquidity discourages professional participation, leaving the market fragmented and inefficient.

2. Persistent Mispricing and Arbitrage Opportunities

When the total value of “Yes” and “No” tickets in a binary market does not sum to 1 dollar, riskless profit opportunities arise. arbitrage traders can systematically exploit these mispricings.

On Polymarket alone, since 2024, basic arbitrage strategies have accumulated profits exceeding $39.5 million. This alarming figure indicates that the market is not efficient enough to correct its own pricing errors. These opportunities exist not because of a lack of public information but because the market’s trading mechanisms do not allow for rapid adjustments.

3. Robots, Algorithms, and the Dominance of Automated Trading

Automated trading systems are increasingly dominating prediction markets. These algorithms operate at speeds humans cannot match, creating an unfair playing field. Casual users often get “eaten alive” by bots, leading to unfair losses.

This not only reduces market fairness but also directly impacts price signal quality. When trading is dominated by short-term pattern-following algorithms rather than fundamental information, prices tend to reflect bot behavior more than actual probabilities.

4. Self-Fulfilling Feedback Loops and Detachment from Reality

One of the most significant risks in prediction markets is their “self-fulfilling” nature. Traders, instead of integrating external information independently, often look at market prices and assume they reflect the truth. Consequently, they believe the market price is accurate without further verification.

This creates a dangerous logical cycle. Instead of incorporating new information, the market simply “looks at itself.” This loop can persist even when clear external evidence points in the opposite direction. For example, in the 2020 U.S. presidential election, prolonged mispricings reflected this phenomenon, as some participants relied on misinformation, and the market failed to adjust swiftly.

5. Misinformation, Poor Data Quality, and Groupthink

When false information begins to spread in markets—especially those with low trading volume—it can persist and significantly distort prices. A few participants may amplify false information, systematically skewing prices.

The core issue is that when many people believe in false information, markets lack mechanisms to quickly “correct” these misconceptions. Markets can only self-correct if there is enough trading activity based on more accurate data. But if accurate information is not widely disseminated or its holders lack incentives to trade, mispricings can endure.

6. Insider Trading and Asymmetric Information

A major concern in prediction markets is the presence of asymmetric information, unlike traditional markets. For example, athletes might bet on their own injuries, or politicians might trade based on confidential plans. These opportunities create unfair advantages.

While the SEC (U.S. Securities and Exchange Commission) explicitly bans insider trading, the CFTC allows trading based on non-public information in many cases. This legal gap creates loopholes exploited by those with privileged information. Current AML and KYC measures mainly focus on identity verification and anti-money laundering, not on addressing information asymmetry.

7. Low Liquidity: The Market’s Most Vulnerable Point

Prediction markets with low trading volume are more susceptible to manipulation. A single large trade can cause significant price swings. When the number of participants is insufficient to correct these deviations, prices become stuck at inaccurate levels.

This limits the practical application of prediction markets to popular events. Local or niche markets, or those with limited interest, become unreliable. Prediction markets are effective only when they focus on high-profile topics with broad public attention.

Rapid Infrastructure: Technical Solutions to Structural Flaws

Most current prediction markets face a transaction queue bottleneck. Whether betting on elections or sports, all trades are processed sequentially in a single queue. This delay extends the window for arbitrage, allowing bots to systematically exploit mispricings.

Emerging infrastructures like FastSet aim to address this fundamental issue. Instead of sequential processing, FastSet enables parallel settlement of non-conflicting trades. This achieves eventual consistency in under 100 milliseconds—a significant improvement over traditional platforms.

When settlement speeds are fast enough, the arbitrage window closes before large-scale exploitation can occur. Resulting prices will more accurately reflect true probabilities. Casual users will also be less affected by systemic delays.

This is not merely a performance upgrade but a fundamental shift toward fairer and more efficient prediction markets.

The Future of Prediction Markets: Balancing Efficiency and Compliance

Prediction markets are increasingly used by organizations, policymakers, and traders. However, realizing their full potential requires addressing the deep structural flaws discussed here.

Current AML/KYC challenges, while necessary for regulation, contribute to the liquidity trap. There is a need for quick, user-friendly identity verification methods that do not impose excessive barriers on casual participants. Future platforms must confront this balancing act.

Furthermore, tackling issues like information asymmetry, bot dominance, and self-reinforcing feedback loops will require rethinking fundamental structures. Not all problems can be solved through additional regulation alone. Some demand architectural changes, clearer public standards, and incentives for accurate information trading.

If these issues are thoroughly addressed, prediction markets could evolve from a niche curiosity into a truly reliable forecasting platform. But for now, these structural flaws quietly limit their accuracy, scalability, and trustworthiness.