Prediction markets are no longer operating at the edge of the financial system. They are beginning to plug into the same distribution infrastructure that scaled options, CFDs, and crypto derivatives: broker apps, media terminals, social platforms, and API-driven trading stacks.
The core change is not just growth in users. It is a structural transition from standalone venues into mainstream trading distribution.
Whether that transition succeeds depends on many variables: regulation, liquidity, resolution credibility, integrity controls, and distribution economics.
1. What “Mainstream Distribution” Actually Means
For prediction markets, mainstream distribution is reached when:
- Contracts are accessible through established broker or fintech interfaces
• Prices are carried as data feeds across financial media
• Institutional liquidity providers quote consistently
• Settlement standards withstand public scrutiny
• Regulatory positioning is clear enough for brand-sensitive partners
At that stage, the venue becomes infrastructure. The distribution partner owns the customer.
This mirrors the evolution of listed options in the 1990s and retail FX in the 2000s: product standardization first, distribution expansion second.
For instance, Interactive Brokers is approaching prediction markets as an extension of its broader multi-asset model rather than a standalone product. Steve Sanders, EVP of Marketing and Product Development at Interactive Brokers, told FinanceFeeds that the firm’s focus is on integrating forecast contracts into a regulated, unified trading environment.
IBKR enables clients to trade prediction contracts through ForecastEx, its affiliate regulated by the Commodity Futures Trading Commission (CFTC), which Sanders describes as a way to ensure “compliance and transparency in prediction market transactions.”
The key difference, in his view, is how prediction markets are positioned within the wider platform. Rather than isolating them, IBKR offers access alongside traditional instruments. Clients can trade “global stocks, options, futures, currencies, bonds, funds, crypto, and prediction markets” through a single account, removing the need to manage multiple systems.
Sanders says this setup allows clients to act more quickly when events unfold. By combining prediction markets with other asset classes, investors can “react swiftly to market developments and global trends” while managing exposure across a single portfolio.
From his perspective, the role of prediction markets is not speculative novelty but practical portfolio use. They allow clients to “diversify their strategies, manage uncertainty, and act on insights across a range of scenarios.” The focus is on contracts tied to real-world events that already drive market behaviour.
That focus shapes the type of products IBKR lists. Prediction markets on the platform currently center on “macroeconomic indicators, climate events, and political outcomes,” aimed primarily at experienced and institutional participants. These contracts are designed to help clients hedge or express views on “key economic indicators, elections (where eligible), and climate events.”
Sanders notes that event-driven contracts can also provide a read on market sentiment. By tracking outcomes such as central bank decisions or major economic releases, prediction markets offer “timely insights into market sentiment for factors that could impact portfolio performance.” This, he says, allows clients to position themselves in advance and manage event risk more directly.
Access to these contracts is built into IBKR’s existing ecosystem, including ForecastTrader, Trader Workstation, IBKR Desktop, IBKR Mobile, GlobalTrader, and the Client Portal, reinforcing the firm’s view that prediction markets are another layer of the same trading workflow rather than a separate category.
2. Liquidity and Microstructure Challenges
Distribution expands access faster than it builds depth. The structural challenges include:
Adverse Selection
Event markets attract traders who believe they possess superior timing or information. That increases toxicity for liquidity providers.
Professional market making becomes mandatory.
Spread Compression
As venues compete for distribution deals, spreads narrow. If liquidity does not scale proportionally, execution quality deteriorates.
Inventory Risk
Unlike continuous markets, event contracts resolve discontinuously. Market makers cannot delta hedge a binary outcome easily. Inventory management is structurally different from options or futures.
Without institutional-grade liquidity provision, distribution partnerships stall.
Prediction markets often raise questions about pricing, risk, and regulation, but Jon Light, Senior Director of Product Management at Devexperts, draws a clear line between what technology providers do and what exchanges or brokers control.Devexperts’ role, he told FinanceFeeds, sits firmly at the infrastructure layer. Its matching engine, DXmatch, operates as a central limit order book. “DXmatch functions as a central limit order book matching engine,” Light explains, with support for “a wide range of order types” and flexible execution rules that can be configured depending on how a market is designed.
From his perspective, performance is a baseline requirement rather than a differentiator. The system is built for “low-latency execution,” with optimized network paths and continuous monitoring to ensure “predictable response times even under high trading volumes.”
Where Devexperts places more emphasis is on access and flexibility. DXmatch provides “direct API access,” including streaming market data, order submission, and execution updates. This allows brokers and exchanges to plug the engine into front-end platforms or algorithmic systems and manage positions in real time.
At the same time, Light is explicit about what Devexperts does not do. The firm does not set liquidity incentives, pricing logic, or risk rules. “These programs are typically determined by the exchange or broker operating the market,” he says when asked about rebates. The same applies to areas such as adverse selection, surveillance, and insider detection. DXmatch exposes the data, but “firms can use this data to implement their own monitoring, analytics, and risk management strategies.”
That division of responsibility extends across the entire prediction market lifecycle. Contract design, pricing, and resolution are not handled by the platform. “Contract resolution is determined by the exchange,” Light says, while pricing decisions — including how to handle ambiguous outcomes — are also left to the operator.
Even questions around liquidity are framed differently at the prediction markets level. From a technology standpoint, scaling is not the constraint. “DXmatch is designed to scale with the broker’s order flow,” Light notes, adding that liquidity bottlenecks are “generally a commercial or market-design issue.”
Regulation follows the same logic. Devexperts does not classify products or impose restrictions on contract categories. “Regulatory classification is determined by the broker or exchange,” Light says, and any limits on topics such as elections or geopolitics are set at that level, not by the infrastructure provider.
Taken together, his view reframes how prediction markets should be understood. The platform provides execution, data access, and integration points. Everything else — pricing, liquidity design, compliance, and market rules — sits with the operator.
3. Integrity and Abuse Risk
Mainstream access increases manipulation risk.
Key threats:
- Coordinated trading to influence narrative perception
• Trading based on material non-public information
• Low-liquidity outcome manipulation
• Cross-platform arbitrage exploitation
To operate inside mainstream rails, prediction venues must implement surveillance comparable to established exchanges. If integrity perception deteriorates, distribution partners disengage quickly.
That said, prediction markets often raise concerns about insider activity, but Justin Lin, Head of Growth at Polysights, argues that the issue is less about visibility and more about interpretation.
“Potential insider information is usually signaled by large block trades,” Lin told FinanceFeeds, pointing to sudden buys that “clear out the order books on platforms.” These moves become more suspicious when they come from “fresh wallets” — new accounts with little or no history — that appear just before a market resolves or ahead of major events.
He notes that funding patterns can make these trades easier to trace than many expect. “If someone sends a large deposit from a regulated exchange like Coinbase straight into a new wallet to make a bet, they’ve essentially linked their real-world identity to that trade.”
Distinguishing informed trading from speculation, he adds, comes down to behaviour. “Informed trading usually comes from ‘sharps’ who are very profitable traders with high win rates,” or from accounts that place “massive concentration on one single market.” By contrast, casual users tend to spread exposure across topics and often show “a perpetually negative P&L,” suggesting they are guessing rather than acting on an edge.
To surface these patterns, Polysights combines data with manual review. Lin describes its Insider Finder as a system that tracks “fresh wallets making large buys in specific markets” and assigns a “Radar Score” based on factors such as entry size, trade concentration, and transaction delta. “This allows us to move past simple guessing,” he says, and identify when a trader may be acting on asymmetric information.
Once flagged, activity is traced further on-chain. Lin explains that teams will “dive deeper into Polygon transaction hashes and Arkham visualizers” to follow the flow of funds and determine whether the source resembles typical speculative activity or something more informed.
He also points out that prediction markets differ from traditional finance in one key respect: transparency. “On Polymarket everything is on chain,” Lin says. While this can create the impression that insider activity is widespread, the trade-off is visibility. “You can see everything and prepare for it with enough research.”
The real limitation, in his view, is not access to data but the ability to use it. “The main gap is detailed wallet analysis,” Lin says, noting that most users lack the tools or experience to interpret on-chain activity. Platforms like Polysights are focused on lowering that barrier by making wallet tracking and analysis easier.
Anonymity adds complexity but does not undermine the model. “Anonymity definitely makes it a lot harder to find the exact person who placed the trade,” Lin says, “but it doesn’t really matter.” What matters instead is how information moves through prices. “Whether the trader is an insider or not, their bet moves the price to the correct spot faster.”
That view reflects a different approach to surveillance. Lin says prediction markets should adopt safeguards similar to traditional exchanges — particularly against manipulation such as wash trading or coordinated activity — but the goal is not identical. These markets are designed to “aggregate and reward accurate information about real-world events,” not just prevent misuse of corporate data.
He adds that many platforms are still missing advanced monitoring tools. While data is public, “raw data alone is difficult for retail users to interpret.” This leaves a gap in areas such as trader behaviour analysis, liquidity tracking, and pattern detection.
Best practices, Lin says, are still emerging. “These practices are still in their infancy,” which creates space for new tooling.
