Content
TL;DR
- Solidus Labs has published a new report on Polymarket and systemic risks in onchain prediction markets.
- The report argues that prediction markets need more than standard trade surveillance because real-world outcomes can be influenced by the same people betting on them.
- Key risks include insider trading, outcome manipulation, fragmented liquidity, misinformation and duplicated markets tied to the same event.
- The bigger takeaway is that prediction markets are moving from crypto-native novelty to financial infrastructure — and that means market integrity now matters as much as volume.
Solidus Labs is putting Polymarket under a tougher market-integrity lens.
In a new report on Polymarket and systemic risks in onchain prediction markets, the crypto surveillance firm argues that prediction markets face a different risk profile from traditional exchanges. The reason is simple: these markets trade on real-world outcomes, and some traders may have information — or even influence — over the outcome itself.
Simply put, prediction markets are not just another trading venue. They are markets where information, behavior and real-world events can collide in messy ways.
Polymarket Shows Why Prediction Markets Need A Different Risk Model
The key issue is not only wash trading or suspicious wallets. Those are familiar crypto problems.
Prediction markets add another layer: outcome risk. A trader may not just know something before the rest of the market. In some cases, the trader could be close enough to the event to shape the result, leak information, manipulate a data source or push a narrative that moves odds.
That makes surveillance harder. A stock exchange mostly watches orders, trades, disclosures and known insider relationships. A prediction market also has to understand event wording, settlement rules, oracle inputs, news timing, social media narratives and the relationship between market behavior and real-world action.
That is why Solidus frames the sector as needing event-centric surveillance, not just product-level monitoring.
What Changed
The prediction-market conversation is shifting.
For most of the last cycle, the main story was growth: more users, more markets, more volume and more mainstream attention. Now the harder question is whether that volume is trustworthy.
Polymarket has already faced scrutiny over manipulation and insider-trading concerns. In March, the platform tightened its market-integrity rules, including clearer resolution criteria, stricter market design standards and enhanced monitoring. That move showed the industry already knew the issue was becoming harder to ignore.
Solidus is now pushing the debate further. Its point is not just that Polymarket needs better rules. It is that the entire category needs surveillance built for how prediction markets actually work.
That same tension is visible in wallet-native prediction products, where event trading is moving closer to everyday users through integrations such as Trust Wallet’s Predict.fun rollout. More access can help the category grow, but it also raises the bar for safeguards.
The Hardest Risk Is Insider Access To Real-World Events
Insider risk looks different in prediction markets.
In traditional finance, insider trading usually means someone trades on non-public information. In prediction markets, the problem can be broader. A person may have private information, but they may also have agency over the outcome: an official, employee, contractor, athlete, campaign worker, data provider or someone close to a decision-maker.
That changes the surveillance challenge. Platforms cannot only ask whether a trade was unusual. They need to ask whether the behavior was reasonable given what was publicly knowable at that moment.
Recent headlines show why this matters. Prediction markets have faced fresh concern over suspicious trades around politics, military events, sports, weather and official data sources. The point is not that every suspicious trade proves abuse. The point is that these markets now operate close enough to real-world outcomes that regulators, users and platforms need stronger tools to separate informed forecasting from unfair access or manipulation.
Fragmented Markets Make Abuse Harder To See
Solidus also highlights a structural problem: one event can create many related contracts.
A single election, court ruling, sports result or policy decision may appear across multiple markets, with different wording, deadlines, data sources and settlement rules. Traders can spread exposure across those markets, making risk harder to detect if surveillance tools only look at each contract separately.
That is a major difference from traditional symbols like stocks or futures. Prediction markets often lack stable identifiers, and small wording differences can change the meaning of a contract.
For market operators, that means surveillance needs semantic understanding. It has to know when two markets are basically tied to the same event, when they are subtly different and when a trader may be using that complexity to hide exposure.
This mirrors a problem already seen across crypto markets, where fragmented venues and inconsistent data can make abuse harder to detect. It is the same reason stronger crypto AML and market-integrity controls are becoming part of the infrastructure conversation, not just a compliance checkbox.
Who It Affects Now
The first group affected is prediction-market platforms. If they want institutional users, sports partners, media integrations or regulatory acceptance, they need to show that their markets are not easy to game.
The second group is traders. Prediction markets can feel transparent because odds are public and trades are often onchain. But transparency does not automatically mean fairness. If insiders, coordinated groups or outcome manipulators can move markets, retail users are at a disadvantage.
The third group is regulators. Event contracts sit in an awkward space between trading, gambling, political information and public forecasting. That makes oversight harder, especially when markets operate across jurisdictions and settle through crypto rails.
The fourth group is wallets and consumer apps. As prediction markets move into easier interfaces, distribution platforms may need to think harder about which markets they surface and what risk warnings users see before trading.
Why It Matters
This story matters because prediction markets are trying to become more than internet betting boards.
If they work, they can become powerful tools for pricing public expectations around elections, policy, sports, macro events, crypto prices and corporate outcomes. But that only works if users trust the odds. Once markets look manipulated, the “wisdom of the crowd” story starts to break.
The next thing to watch is how platforms respond. Stronger resolution rules are a start, but the bigger test is whether prediction markets can build surveillance across wallets, funding flows, related contracts, social narratives and event outcomes.
If they can, the sector has a better chance of becoming serious market infrastructure. If they cannot, prediction markets may keep growing in volume while losing trust where it matters most: with users, partners and regulators.