Since surging into the mainstream following their near-pinpoint accuracy in forecasting Donald Trump’s 2024 U.S. presidential victory, prediction markets have faced intense scrutiny. Despite their growing utility as powerful forecasting tools, these platforms remain dogged by systemic allegations ranging from the facilitation of insider trading to the creation of perverse incentives and significant moral hazards.

While global regulators and governments have pivoted toward enforcement—arresting operators and banning specific high-stakes contracts—the chorus for a more robust, standardized regulatory response is growing. Just recently, alarmed U.S. lawmakers introduced legislation that bars contracts involving death and war.

A fierce debate is emerging over the timing and scope of such oversight. Much like the early days of other transformative technologies, proponents argue that heavy-handed regulation at this nascent stage would likely stifle innovation before it can fully mature. These supporters contend that prediction markets provide unique, real-world value by aggregating disparate information into actionable data.

Instead of rigid government mandates, they advocate for a self-regulatory framework. This approach, they argue, would allow the industry to establish ethical norms and mitigate risks while maintaining the flexibility necessary for the technology to evolve in tandem with everyday life.

Amit Mahensaria, the CEO of the P2P sports prediction exchange Pred, agrees that self-regulation is a “must-have.”

“Any platform serious about longevity should be building integrity infrastructure regardless of whether a regulator is watching,” Mahensaria said. Such self-regulation involves implementing surveillance systems, clear settlement rules, manipulation detection, and transparent reporting.

However, Mahensaria concurs with critics that self-regulation has its limits. While incentive structures are apparent in the short term, history suggests industry players often take serious action against malpractice only after encountering significant crises.

“History shows that industries left entirely to self-regulate tend to discover their principles right around the time a scandal forces the conversation. Financial markets, aviation, pharmaceuticals: the pattern is consistent,” Mahensaria told Bitcoin.com News.

Instead of total self-regulation, the Pred co-founder advocates for “proportionate regulation” that sets baseline standards without stifling the structural advantages prediction markets offer over traditional alternatives. In his view, regulators should focus on settlement integrity, counterparty transparency, and anti-manipulation.

While blockchain-based platforms are broadly scrutinized, those focused on verifiable outcomes with natural timelines have faced less backlash. Mahensaria noted that platforms like Pred have a structural integrity advantage over markets based on political events or geopolitical conflicts, where outcomes can be subjective, manipulable, or ethically fraught.

“Markets on assassinations, wars, or political crises raise real ethical concerns that the industry shouldn’t dismiss as squeamishness,” Mahensaria said. “The question isn’t just whether such markets can be settled accurately. It’s whether they create perverse incentives and whether the information they aggregate is worth the moral cost of the mechanism.”

When asked who should be mandated with curating bets before listing, Mahensaria suggested a combination of platform discretion and regulatory frameworks. He argued that platforms must exercise judgment and explain it publicly, while regulators should set boundaries around clearly harmful categories.

Meanwhile, some proponents are turning to artificial intelligence to detect insider trading, a vision epitomized by the recent partnership between Polymarket, Palantir, and TWG AI. Mahensaria believes the industry is currently lagging behind traditional financial markets in this deployment.

“AI is genuinely useful here. The core application is pattern recognition across large datasets: identifying trading behavior that deviates from expected models in ways that correlate with insider knowledge or coordinated manipulation,” Mahensaria explained.

However, the use of AI introduces a new tension: the risk of false positives penalizing skilled traders. Mahensaria insists that surveillance must protect the market without punishing the “sharp analysis” that makes prediction markets work. He argues that AI flags should never trigger automatic penalties; instead, they must undergo human review and contextual analysis.

“The traditional sports trading industry has spent decades punishing winners through account restrictions and reduced limits. That’s the opposite of what prediction markets should be,” Mahensaria noted. He suggested that the best defense against insider trading is not aggressive surveillance, but rather smart market design—refusing to list markets that are highly susceptible to manipulation in the first place.

Mahensaria also highlighted that the blockchain layer provides a significant advantage for these integrity efforts.

“On-chain prediction markets generate a transparent, immutable record of every trade, which gives AI surveillance systems a richer dataset,” he said. “The combination of on-chain transparency and AI-driven analysis creates a genuinely better integrity infrastructure than what exists in most traditional sports trading environments today.”

• What has sparked scrutiny of prediction markets recently? Increased mainstream attention followed their accurate forecasting of Donald Trump’s 2024 presidential victory.
• What are the main concerns regarding prediction markets? Concerns include insider trading allegations and the potential for creating ethical dilemmas and perverse incentives.
• What regulatory approaches are being proposed? U.S. lawmakers are advocating for legislation to restrict certain high-stakes contracts like those involving death and war.
• How do industry leaders propose to address these challenges? Amit Mahensaria suggests adopting self-regulation alongside proportionate government oversight to establish ethical standards without stifling innovation.

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