Prediction market platforms need to build “integrity infrastructure” before regulators force them to, according to Pred co-founder and CEO Amit Mahensaria. He pointed to surveillance systems, clear settlement rules, manipulation detection, and transparent reporting as the core elements that serious operators should already have in place if they want longevity.
Prediction markets moved deeper into the mainstream after their near-exact calls on Donald Trump’s 2024 U.S. presidential victory, but the added visibility also brought heavier scrutiny. The sector now faces repeated allegations tied to insider trading, distorted incentives, and moral hazard around certain types of contracts. Governments and regulators in some jurisdictions have already shifted toward enforcement, including arrests of operators and bans on specific high-stakes contracts. In the U.S., lawmakers recently introduced legislation that would bar contracts involving death and war.
Self-regulation is necessary, but not enough on its own
Mahensaria called self-regulation a “must-have.” His view is that platforms aiming to last cannot wait for a regulator to start watching before they invest in market integrity. At the same time, he agrees with critics who say self-regulation has limits. Industries often discover their principles only after a scandal forces the issue, he said, pointing to patterns seen in finance, aviation, and pharmaceuticals.
That is why he argues for what he described as “proportionate regulation,” not an absence of rules and not blanket restrictions either. In his framework, regulators should focus on settlement integrity, counterparty transparency, and anti-manipulation standards, setting a baseline without wiping out the structural advantages prediction markets may have over traditional alternatives.
Market selection itself is part of the integrity question
Mahensaria drew a distinction between markets built around verifiable outcomes with natural timelines and markets tied to politics or geopolitical conflict. The former, he said, tend to face less backlash. The latter can involve subjective outcomes, greater room for manipulation, and sharper ethical concerns. He was explicit that assassination, war, and political crisis markets raise real moral questions, and that the industry should not dismiss those concerns.
Asked who should decide what gets listed, Mahensaria supported a mix of platform discretion and regulatory boundaries. Platforms, in his view, need to make judgment calls and explain them publicly, while regulators should define limits around clearly harmful categories. That makes listing standards part of the integrity stack, not just a product decision.
AI can flag suspicious trading, but humans still need to review it
Some firms are now turning to AI to watch for abuse. The report notes that Polymarket has partnered with Palantir Technologies and TWG AI to build a new AI-driven platform. Mahensaria said prediction markets are still behind traditional financial markets in this area, but he sees a clear use case: pattern recognition across large datasets to identify trading behavior that departs from expected models and may correlate with insider knowledge or coordinated manipulation.
He also warned that AI surveillance creates a separate problem. False positives can end up penalizing traders whose edge comes from strong analysis rather than misconduct. For that reason, he said AI flags should never lead straight to automatic penalties; they need human review and contextual assessment. He added that traditional sports trading has a long history of restricting successful traders through account limits, which he said is the opposite of what prediction markets should become.
For Mahensaria, the stronger defense against insider trading may start with market design itself: refusing to list markets that are highly vulnerable to manipulation. He also argued that blockchain adds a meaningful advantage here. On-chain prediction markets create a transparent and immutable record of every trade, giving AI monitoring systems a richer dataset. In his telling, the combination of on-chain transparency and AI analysis can produce a stronger integrity framework than what exists in much of traditional sports trading today.