Since accurately predicting Donald Trump’s 2024 presidential victory, prediction markets have surged into the mainstream spotlight. However, their rapid growth has also drawn intense scrutiny over allegations of insider trading, ethical concerns, and potential systemic risks. In a recent interview with Bitcoin.com News, Amit Mahensaria, CEO of peer-to-peer sports prediction exchange Pred, outlined a vision for building what he calls “integrity infrastructure” that balances industry self-regulation with necessary government oversight.
Self-Regulation as a ‘Must-Have’
Mahensaria argues that any platform serious about long-term survival must proactively establish ethical safeguards, regardless of regulatory pressure. “Any platform serious about longevity should be building integrity infrastructure regardless of whether a regulator is watching,” he said. This includes implementing surveillance systems, clear settlement rules, manipulation detection, and transparent reporting. Self-regulation, in his view, allows the industry to demonstrate responsibility while maintaining the flexibility needed for innovation to flourish.
The Limits of Self-Regulation: A Historical Pattern
Still, Mahensaria acknowledges that self-regulation alone is not sufficient. He points to historical precedents in finance, aviation, and pharmaceuticals, where industries left entirely to their own devices only discovered ethical principles after major scandals. “History shows that industries left entirely to self-regulate tend to discover their principles right around the time a scandal forces the conversation,” he said. Instead of total self-regulation or heavy-handed government mandates, he advocates for “proportionate regulation” that sets baseline standards without undermining the structural advantages of prediction markets. Regulators, he suggests, should focus on settlement integrity, counterparty transparency, and anti-manipulation measures.
AI and Blockchain: Building Better Oversight
The recent partnership between Polymarket, Palantir Technologies, and TWG AI to build an AI-driven monitoring platform highlights the industry’s turn toward technology to detect insider trading. Mahensaria believes AI is genuinely useful for pattern recognition across large datasets, identifying trading behavior that deviates from expected models in ways that correlate with insider knowledge or coordinated manipulation. However, he warns that AI can produce false positives, penalizing skilled traders who contribute to market efficiency. “AI flags should never trigger automatic penalties; instead, they must undergo human review and contextual analysis,” he insisted. He further notes that traditional sports trading platforms have long punished winners through account restrictions, which is the opposite of what prediction markets should encourage. The best defense against insider trading, he argues, is smart market design—refusing to list markets that are highly susceptible to manipulation in the first place.
Mahensaria also highlighted the unique advantages of blockchain technology: “On-chain prediction markets generate a transparent, immutable record of every trade, which gives AI surveillance systems a richer dataset. 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.”
Ethical Boundaries and the Role of Regulators
When asked about the ethical boundaries of prediction markets, Mahensaria cautioned against markets based on assassinations, wars, or political crises. “Markets on assassinations, wars, or political crises raise real ethical concerns that the industry shouldn’t dismiss as squeamishness,” he said. He suggested that platforms must exercise judgment and explain it publicly, while regulators should set boundaries around clearly harmful categories. This dual approach—platform discretion plus regulatory guardrails—can help ensure prediction markets serve as useful information aggregation tools without creating perverse incentives.
In conclusion, Mahensaria’s vision offers a roadmap for prediction markets to navigate the current scrutiny: build integrity infrastructure proactively, combine AI surveillance with human oversight, leverage blockchain transparency, and embrace proportionate regulation. The survival of the industry, he believes, depends on proving that it can be both innovative and responsible.