By Joe Schifano, Global Head of Regulatory Affairs, Eventus.
Prediction markets are quickly becoming the next frontier of finance – a new class of markets where people trade on what they believe will happen next. From election results to interest rate fluctuations, these platforms turn collective judgment into tradable data.
But as prediction markets move from experimental crypto venues to regulated exchanges in the U.S., one question looms larger than any other: how will they earn trust and maintain an orderly market?
Integrity is the currency that underpins every financial market. For prediction markets, it’s existential. When the outcome of a trade depends on a real-world event – a referee’s call, a government announcement or breaking news – traditional notions of market abuse, insider trading and fair access take on new meaning. The surveillance and compliance systems designed for futures, equities and crypto must now adapt to event-based trading, where information moves at the speed of social media and influence is more diffuse.
Here are ten surveillance and compliance challenges that will define the next chapter of market integrity in prediction markets.
1. Catching the Insider “Before and After the Whistle”
When insiders or their associates trade on privileged information, the misconduct often surfaces only after the damage is done. In prediction markets – where outcomes depend on human actions like coaching decisions, officiating calls or injury reports – these cases are especially complex. Surveillance systems must reconstruct timelines, identify unusual position-taking and match it to moments when non-public information became known.
But prevention is as important as detection. Pre-trade controls – such as restricted lists, participant screening and automated position limits – will be critical to deter prohibited trading before it happens. These measures create a first line of defense, while post-trade analytics and forensic reviews provide the second.
Recent betting scandals in the NBA and MLB are a reminder that integrity breaches leave data trails. The combination of proactive controls and post-event forensics – using transaction patterns, device identifiers and relationship mapping – will help surveillance teams connect the dots between the “what” (the trade) and the “why” (the information).
2. Who’s Allowed to Play?
Prediction markets blur the line between participant and insider. If a trader can influence an outcome – an athlete, referee, election worker or even data vendor – they undermine the perception of fairness. In traditional sports betting, leagues enforce “prohibited person” lists; prediction markets need similar frameworks backed by dynamic identity checks.
Compliance teams are exploring how to integrate restricted lists from leagues, teams and regulators to ensure that those closest to an outcome aren’t the ones profiting from it. The goal isn’t just to prevent wrongdoing – it’s to preserve trust in the market itself.
3. The Information Edge
In most markets, insider trading laws prohibit using non-public information for profit. But in prediction markets, information is the commodity itself. What counts as “non-public” when traders are betting on events like policy decisions, company announcements or election outcomes?
This legal and ethical gray area challenges both operators and regulators. The U.S. Commodity Futures Trading Commission (CFTC) and National Futures Association (NFA) may need to define new standards of fair access and timing – while surveillance systems must flag trading patterns that suggest foreknowledge without stifling legitimate speculation. The question isn’t just about what traders know, but when and how they act on it.
4. Retail Meets Regulation
Prediction markets are no longer niche. They’re appearing next to stocks and crypto on mainstream apps, reaching millions of new users. That democratization is exciting, but it introduces a new kind of challenge for platform operators: ensuring that retail traders understand event contracts while maintaining robust compliance oversight.
Surveillance and compliance teams must balance education, suitability and behavioral monitoring – protecting novice participants without making the experience overly restrictive. It’s a delicate balance between access and accountability.
5. Surveillance in the Age of Viral News
In the modern information ecosystem, a market can move faster than a newsroom. A rumor on social media, a leaked memo or even a misinterpreted statistic can swing odds before the facts are verified.
Surveillance systems must now ingest and analyze external data – from betting line movements to live news feeds – to distinguish organic sentiment from coordinated misinformation. In thinly traded markets, a handful of actors amplifying a false story can distort prices dramatically. The challenge is syncing market monitoring with real-world information flows in real time.
6. Data, Data, Data
Market integrity is only as strong as the information that supports it. Traditional trade surveillance has relied on a few well-structured data streams — orders, executions and participant IDs. But prediction markets demand far more granular and diverse inputs.
To identify manipulation or insider activity, surveillance teams must aggregate data from disparate sources: trading and order books, funding and wallet activity, know your customer (KYC) and onboarding systems, even third-party feeds such as league rosters, news timestamps and oracle results. Much of this information is scattered across different systems and ownership boundaries – exchanges, intermediaries and data vendors.
The challenge is not only collecting the data, but making it interoperable. Prediction markets might need standardized data schemas and shared identifiers so that behavioral signals – timing, affiliations and correlated trading – can be stitched together across environments. Surveillance teams that can unify these fragmented data points will be the ones able to see the full story behind every trade.
7. Cross-Industry Collaboration
Integrity failures rarely happen in isolation. A suspicious trade on a prediction market might correlate with a bet placed in a sportsbook or a data leak at a partner organization. It may be difficult for a single entity to see the full picture.
Collaboration among exchanges, intermediaries, watchdogs and regulators will be essential. The most effective surveillance models will involve information-gathering frameworks that connect data across sectors. Market integrity, in this sense, will become a team sport.
8. Oracles and Outcomes
Every prediction market depends on a trusted answer to a common question: what happened? If the mechanism that determines outcomes – the “oracle” – fails, everything else collapses. Whether through technical error, governance failure or malicious interference, a corrupted data source can turn a fair market into a broken one.
Modern compliance thinking extends surveillance beyond trading activity to the data that drives settlement. Transparent rules for event definition, dispute resolution and auditability are now essential features of prediction market design.
9. Responsible Innovation
Innovation without accountability is short-lived. Prediction markets seeking regulatory recognition must prove that their surveillance systems work, not just that they exist. Measurable coverage, documented investigations and clear escalation paths will separate compliant operators from the rest.
Far from slowing growth, this approach builds credibility. Firms that treat compliance as a design feature – not a cost center – will be best positioned to attract institutional partnerships and regulatory goodwill.
10. Augmented Insight: AI as the Analyst’s Partner
Artificial intelligence isn’t replacing compliance officers – it’s equipping them. The complexity of prediction markets means surveillance teams must process enormous volumes of data from trading systems, communications, social feeds, blockchain analytics and third-party sources. Machine learning and large language models (LLMs) can help transform this flood of raw data into usable intelligence.
Rather than predicting misconduct autonomously, AI tools can parse unstructured information, surface connections across disparate data sets and provide analysts with the context they need to make informed judgments. Imagine a surveillance alert that comes enriched with relevant historical trades, related counterparties, news events and network relationships – all assembled automatically by an AI assistant.
In this way, AI doesn’t replace human judgment; it amplifies it. By improving data discovery and synthesis, these tools enable compliance officers to focus on the “why” behind behavior instead of the “where” in the data. The future of market integrity may not be fully autonomous — but it will be AI-assisted, faster and far more informed.
Conclusion: The Road to Trust
Prediction markets sit at the crossroads of finance, data and human behavior. Their success won’t just depend on innovation – it will depend on credibility. Surveillance is the invisible architecture that underwrites trust among users and partners alike.
In a world where everyone wants to predict the future, integrity will be judged by how venues handle the present. Prediction markets will thrive not by avoiding scrutiny, but by proving that transparency and trust can coexist with innovation.
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