Prediction markets are having their “crypto 2017” moment. Capital is flowing in, retail participation is exploding, and new venues are launching faster than market structure, regulation, and integrity frameworks can mature.
The early trajectory of crypto also provides a powerful roadmap for prediction markets today. In that formative era, the rapid pace of product innovation often outpaced the deployment of institutional-grade safeguards. In the crypto old-days, this "Wild West" approach inevitably triggered aggressive regulatory scrutiny, including consent orders, civil litigation, and the temporary suspension of activities. Ultimately, beyond serving as a catalyst toward the robust compliance standards we see today in digital asset markets, this evolution serves as a vital lesson for any emerging asset class: sustainable growth depends on internalizing these safeguards from day one. By prioritizing market integrity early, operators don’t just avoid friction; they build the defensible infrastructure necessary to move from a niche novelty to a global financial standard.
Leading the charge into this new era of maturity is Kalshi, which recently announced a landmark partnership with Solidus Labs to implement institutional-grade surveillance across its 4,000+ markets. As a CFTC-regulated entity, Kalshi is setting a new "gold standard" - proving that for the world's most sophisticated venues, implementing high standards for transparency is a foundational requirement to protect consumers and ensure these markets reach their full potential as a defensible asset class.
Why Prediction Markets Demand More Than Legacy Surveillance
Prediction markets sit at the intersection of finance, real-world events, and human behavior. In this high-stakes environment, market integrity is not a supporting function, it is the product. When that integrity is compromised, the ripple effects can undermine the very trust that allows these markets to scale: consumers face direct financial loss, institutions lose trust, and regulators lose patience.
The Six Structural Gaps in Legacy Surveillance:
While prediction markets may look like traditional exchanges, supervising them as such is a fundamental oversimplification. The distinction lies in the relationship between the trader and the asset: unlike equity markets, where a retail trader is primarily an external speculator reacting to corporate performance rather than influencing it, prediction market outcomes can be driven by the very individuals participating in the market. Because these outcomes are tied to human action and real-world results, protecting the integrity of the discovery process requires a surveillance model that understands behavioral intent, not just price movement.
Here are the six structural blind spots of legacy surveillance tools:
1. Insider trading, on steroids
In the 2026 prediction markets, the concept of an 'insider' has evolved beyond the TradFi baseline. In traditional markets, an insider is defined by their access to material non-public information (MNPI) - whether as a corporate executive, a consultant, or a “tippee” misappropriating the information. But in prediction markets, the risk moves from access to agency. An insider isn't just someone who knows the result; they may also be an athlete, an official, or a contractor who can determine it.
Recent activity on platforms like Polymarket has brought this "Outcome Agency Risk" into focus, leading to increased scrutiny and calls for clearer oversight:
- Geopolitical Operations: In early January 2026, an anonymous account reportedly opened a $30,000 position predicting that Venezuelan President Nicolás Maduro would be removed from office. Within hours, Maduro was captured by U.S. forces, resulting in a $436,000 in profit. This timing fueled significant public debate and led to the introduction of the Public Integrity in Financial Prediction Markets Act of 2026.
- Corporate Product Launches: In late 2025, several accounts demonstrated uncanny foresight by opening high-conviction positions on OpenAI and Google product releases just days before public announcements. One notable account, "AlphaRaccoon," reportedly correctly predicted 22 out of 23 categories for Google’s Year in Search, generating widespread suspicion on social media regarding potential access to internal lists.
- Sports Integrity: Unlike sportsbooks, which act as a risk buffer, prediction markets turn direct stakeholders - such as athletes, coaches, and medical staff - into "market participants", broadening the scope of potential insiders. Recent concerns raised by lawmakers highlight that a player’s unknown health status or a referee’s assignment can materially shift market odds, creating a "compliance blind spot" that traditional securities laws were not designed to cover.
Detection in prediction markets depends on behavioral surveillance: identifying patterns of timing, correlation, and event-specific positioning that do not align with public information.
This is one of the clearest lessons from crypto: you cannot rely on knowing who someone is - you must understand how they behave.
2. Retail-first markets require more than siloed trade surveillance
Prediction markets are retail-first platforms, often embedded next to stocks and crypto in consumer apps.
That reality fundamentally changes the integrity model:
- Abuse often appears first in funding behavior, not necessarily in trading behavior
- Deposits, withdrawals, and account reuse matter as much as orders
- Risk spans KYC, transaction monitoring, and market abuse simultaneously
Trade surveillance alone is insufficient. Effective oversight requires a unified view of the customer lifecycle - onboarding, funding, trading, and exit. This is the approach crypto venues adopted, and prediction markets are now encountering it at speed.
3. Cross-product exposure breaks instrument-level monitoring
Prediction markets are structurally cross-product. A single event can generate:
- Separate “yes” and “no” token pairs for the same event
- Multiple outcome variants
- Different expiries and resolutions
- Dozens of order books tied to the same real-world result
Exposure to one outcome may be distributed across many instruments. Market abuse, insider trading, or manipulation may only become visible once positions are aggregated at the event level.
Legacy surveillance tools - built around individual instruments - miss this by design. Prediction markets demand event-centric surveillance, not product-centric monitoring.
4. Fragmented data is not a bug, it’s the operating environment
Legacy trade surveillance assumes centralized visibility: one venue, one order book, one consolidated view of activity.
That assumption breaks down immediately in prediction markets.
- Liquidity is fragmented across venues and protocols
- Exposure to a single real-world event can be split across dozens of contracts
- Trading data, wallet activity, oracle inputs, and settlement logic often live in different systems, sometimes owned by different entities
Just as in early crypto, modern surveillance requires reconstructing behavior across incomplete and fragmented data, not monitoring a single market in isolation. Any surveillance solution that assumes full market visibility will fail in practice.
5. Information, and misinformation, is a first-class risk vector
In prediction markets, information is the asset.
Coordinated misinformation campaigns, social media rumors, leaked screenshots, or misinterpreted statistics can move prices faster than official disclosures - especially in thin markets.
Surveillance must therefore extend beyond internal data to include:
- News velocity and timing
- Social amplification patterns
- Correlation between narrative spread and trading behavior
This is not “nice to have.” It is essential for understanding whether price movement reflects genuine belief or coordinated manipulation.
6. Lack of Standardized Symbology Breaks Legacy Surveillance
Prediction markets face a structural challenge most legacy surveillance systems are not designed to handle: there is no standardized symbology.
In traditional markets, instruments have stable identifiers (tickers, ISINs, CUSIPs) that surveillance systems rely on as anchors. Prediction market contracts, by contrast, are dynamically created, defined by free-form text, and often duplicated across venues with subtle wording differences.
This mirrors an early crypto problem: the proliferation of fake or look-alike tokens that exploited systems assuming symbol integrity.
In prediction markets, similar dynamics emerge:
- Near-identical contracts tied to the same event
- Slight wording changes that alter settlement logic
- Multiple markets that appear equivalent but behave differently
Without standardized identifiers, legacy systems struggle to aggregate exposure, link related contracts, or detect manipulation that spans similarly named instruments.
Effective prediction market surveillance requires semantic understanding, not symbol matching - the ability to interpret contract definitions, group related markets by event, and normalize risk across dynamically created instruments.
Crypto learned this lesson early.
Why Solidus Is Built for Prediction Markets
Solidus was founded in the aftermath of the first failed attempts to approve a Bitcoin ETF, a moment when regulators made clear that market integrity, not innovation, was the gating factor for crypto’s legitimacy.
Prediction markets are now facing the same inflection point crypto once did. Growth is undeniable. Innovation is accelerating. But maturity will be defined by credibility, not volume. Solidus is here to support this transition, from experimentation to trust, from novelty to infrastructure, and from rapid growth to defensible legitimacy.
The integrity question that matters
Ultimately, prediction market surveillance must answer a single, difficult question:
Was this behavior reasonable given what was publicly knowable at the time, across all contracts, all accounts, and all information channels?
Solidus was built to answer that question. Our multidimensional detection technology thrives in environments defined by fragmented data, pseudonymous identities, retail-first markets, and information moving faster than price discovery. Those same characteristics now define prediction markets. That is not a coincidence; it is why Solidus fits this moment better than any legacy surveillance approaches retrofitted from equities or futures.
Prediction markets may still be early. But the standards for integrity are being set now. The venues that succeed will not be the ones that bolt surveillance on later, they will be the ones that adopt systems designed for the reality of these markets from day one.
Solidus is not adapting to prediction markets.
It was born and built for them.
See it in action, request a demo.
