This conversation is part of Eventus’ ongoing examination of the biggest surveillance and compliance challenges in prediction markets. In prior installments, we explored core considerations like insider trading risk and participant eligibility. Today, we turn to cross-industry collaboration and the reality that integrity failures in event-driven markets rarely occur in isolation.
A suspicious trade on a prediction market may correlate with activity in a sportsbook, a social media campaign, a data leak or access to nonpublic information. No single platform necessarily sees the full picture. As prediction markets expand into elections, sports and geopolitics, surveillance and accountability cannot operate in silos. Integrity, in this environment, becomes a shared responsibility across exchanges, intermediaries, data providers and regulators.
Self-certification and surveillance design sit at the center of this challenge. They are not merely procedural requirements; they are where contract design, manipulation risk assessment and supervisory architecture first converge. The effectiveness of that framework depends not only on what an individual exchange builds internally, but on how well information, expectations and standards align across the broader ecosystem – and how consistently the CFTC applies its oversight and enforcement posture.
Shortly after this interview took place, the CFTC’s Division of Enforcement issued a prediction markets advisory tied to disciplinary matters involving misuse of nonpublic information and fraud in event contracts. That development underscores the central theme of today’s discussion: the true test of collaboration and integrity is whether surveillance models, participant controls and governance structures function cohesively, and whether they can withstand regulatory and public scrutiny when issues arise.
To explore how compliance and surveillance teams should approach these cross-sector challenges, Eventus Global Head of Regulatory Affairs Joe Schifano sat down with:
- Elizabeth Lan Davis, Partner, Davis Wright Tremaine
- Regina Thoele, Partner, Patomak Global Partners
The conversation covers self-certification blind spots, regulatory expectations from the CFTC and NFA, the onboarding of non-traditional participants and the need to stitch together trade surveillance, identity, AML and geolocation data into a defensible, collaborative market integrity framework.
Self-certification is often discussed as a procedural step – but in prediction markets, it’s also where contract design, integrity assumptions and supervisory expectations first collide. The question is whether a framework built for traditional commodities can reliably scale to fast-moving event contracts.
Joe Schifano: Self-certification is often treated as a procedural step, but it’s also where integrity risk can hide. Does the current self-certification regime adequately address the risks unique to prediction markets, or does it create blind spots that may only surface through enforcement or examination?
Elizabeth Lan Davis: The biggest challenge is that the self-certification framework was built around traditional commodities and TradFi. With prediction markets, contracts are moving beyond “pork bellies and crude oil” into sports, culture and other event-driven categories, and that’s where blind spots emerge. There are nuances specific to those events that may not be fully captured by what’s typically provided in a self-certification submission. You often don’t find out what you missed until after something happens.
Regina Thoele: I’d echo that in a broader sense. Technology and innovation are moving quickly, and it’s hard for regulators to keep pace. Even with the best intentions, rulemaking and oversight take time, and prediction markets are evolving in real time. That can leave uncertainty about where the lines are, especially as new models and new participants enter the space.
As prediction markets expand, many operators and participants are encountering the CFTC/NFA environment for the first time. That makes compliance less about policies on paper and more about whether firms understand what a regulated market expects.
Schifano: A related dynamic here is who’s entering the CFTC-regulated world. We’re seeing many non-traditional, non-financial entrants. What does that mean for compliance readiness?
Thoele: We’re absolutely seeing that. A lot of firms coming into prediction markets haven’t lived in the CFTC/NFA compliance world before. The practical risk is that they underestimate what it really means to have robust compliance infrastructure: policies and procedures, surveillance and the right skill sets to run those programs.
And it’s not just one thing. These are regulated environments with expectations around surveillance, supervision, reporting and more. Part of the work, frankly, is education: helping new entrants understand what regulators will look for and where their blind spots are likely to be.
Davis: Exactly. The considerations run across every area: the policies and procedures they need, the operational obligations they’ll face and whether they have the people in-house who understand things like segregation and AML requirements. That capability gap is very real, and it’s a blind spot if you’re entering this world for the first time.
Even where the high-level self-certification workflow is clear, the practical reality is murkier: what’s public, what’s confidential and what does “not readily susceptible to manipulation” mean when the underlying event is cultural, political or discretionary?
Schifano: On the mechanics of self-certification: contracts seem to materialize rapidly. How does the process work in practice? What does the regulator actually see?
Davis: At a high level, exchanges must certify that they comply with the CFTC’s core principles. One that comes up again and again is the idea that the contract is not readily susceptible to manipulation. That can be harder to interpret in event-driven markets than in traditional commodities.
In terms of what’s visible: you can access lists of certifications publicly, but much of the supporting detail may be subject to confidentiality requests. So you often see the general shape of what was filed, but not necessarily the nitty-gritty of how the surveillance and controls are designed. And while there’s a window for the CFTC to engage, including the ability to seek a stay, the reality is that, in these fast-moving markets, that timeline doesn’t always map cleanly onto how quickly contracts list and resolve.
Thoele: One thing I’d add is that self-certification can sound deceptively lightweight to newer entrants – almost like a formality – when in practice it’s where you’re meant to pressure-test the contract design, the manipulation vectors and the supervision plan. The challenge is that a lot of the real work sits behind the filing: the internal rationale, the controls and the operational readiness to monitor and enforce. Because prediction market contracts can list and resolve quickly, the expectation shifts from regulators catching things in advance to being able to defend your framework and decisions – and your ability to detect and respond fast when something looks wrong.
In prediction markets, integrity isn’t just a trade-surveillance problem. It’s a multi-stream supervision challenge that involves connecting trading behavior with identity, AML controls, geolocation and external integrity signals and proving the program can function coherently across vendors and data sources.
Schifano: Let’s talk about the surveillance data stack. In prediction markets, you’re quickly in a world of trade surveillance plus AML monitoring, identity verification, geolocation and external integrity signals. Who ultimately owns integrity, and what will regulators expect when surveillance is distributed across vendors and data streams?
Thoele: The hard part is that you can’t treat those streams in isolation. You have to put it all together and be able to explain what happened, which requires a holistic approach and some centralized ability to connect the dots. The challenge, of course, is scale: it’s a needle-in-a-haystack problem across multiple sources of information.
Davis: And when something does happen – when there’s a major dislocation or controversy – everyone goes back to reconstruct the story. That’s when your controls, your audit trail and your ability to combine data points get tested. It’s easy to connect the dots after the fact, but regulators and stakeholders will still ask what you had in place, what broke and why?
Schifano: That’s an important point. We’ve all seen this dynamic in mature markets – the “reconstruction” exercise after the fact. In prediction markets, that scrutiny will arrive too, and it will focus on whether you can demonstrate the integrity of your supervision across systems and vendors. Looking ahead, you can imagine a future where integrity signals come from external sources like sports integrity monitoring, paired with on-platform trading behavior, then enriched with geolocation or identity context. Does that multi-stream model feel inevitable?
Davis: Yes, but the key is what you do with it. It’s one thing to have access to multiple feeds. It’s another to have a defensible framework for how they inform investigations and decision-making.
Thoele: And from a client-readiness standpoint, it reinforces the theme we’ve been discussing: this space is moving quickly. If your systems and controls don’t mature alongside the products, you’re setting yourself up for pain later.
Shortly after this conversation, the CFTC’s Division of Enforcement issued a prediction markets advisory tied to recent disciplinary matters on a DCM.
Schifano: One more thing: on a very timely note, the CFTC’s Division of Enforcement issued a prediction markets advisory following the public release of two cases involving misuse of nonpublic information and fraud related to event contracts traded on Kalshi. One involved a political candidate trading on his own candidacy; another involved a trader affiliated with a YouTube channel trading with likely access to material non-public information. Does this strengthen the case for KYC and blocking access to certain contracts? What does it suggest about where the CFTC is headed?
Davis: The advisory is notable for two reasons. First, it reinforces that prediction markets are not a special case – the Commission is signaling that it will investigate and prosecute misconduct such as manipulation, fraud and wash trading on prediction markets in the same way it does on traditional exchanges. Second, it highlights how these issues can surface in practice: reporting around the actions suggested the activity was identified through a combination of surveillance and other users observing near-perfect trading performance, which underscores the importance of having monitoring that can detect unusual patterns quickly.
From a controls perspective, these fact patterns do support the case for robust KYC and participant controls, particularly where contracts are more susceptible to information asymmetry or conflicts. But there’s also an education and deterrence dimension here: public notices like this can help clarify what constitutes violative activity in a space where the contract set is broad and evolving. It’s unusual for the CFTC to highlight disciplinary actions taken by a DCM in this way, and I read the advisory as a clear signal to the industry that the Commission is paying close attention and expects strong market integrity programs, not just as a matter of policy, but as a matter of enforcement posture.
Our thanks to Elizabeth and Regina for their time and perspective. For additional commentary and resources from Eventus on market and product evolution, explore our blog or request a demo.