Specialist Firms Supply Liquidity Backbone for US Prediction Markets
TL;DR — White Swan accounts for as much as 40% of activity on some secondary exchanges with a focus on the RFQ, parlay market. The largest UK and European sharp-betting groups have moved rapidly into US prediction markets with more than 100 smaller operations interested in entering. People will lose money faster on exchanges and the model could test the sustainability of the model.
SCCG Take — Prediction platforms rely on institutional risk providers that challenge the P2P narrative. Operators and regulators must track retention metrics closely as growth masks faster churn against sharper counterparties. (29 words)
US sports prediction markets function as sportsbooks without in-house risk teams. Bernard Marantelli, founder of White Swan Data, describes Kalshi as “a sportsbook that’s just not allowed to have an in-house risk team.” Exchanges supply an API so professional firms can quote prices and provide liquidity while retail users see a peer-to-peer proposition.
Scale of Professional Market Making
White Swan accounts for as much as 40% of activity on some secondary exchanges with a focus on RFQ parlay markets. Marantelli cites better margins and defensibility because fewer participants can price correlations effectively. He identifies White Swan and Susquehanna at industrial scale in parlays, with Jump Trading, Mojo and DL Trading in the leading group. Smaller syndicates manage between $5 million and $10 million.
Enda Kendrick, chief executive of Veltium, reports that the largest UK and European sharp-betting groups have moved rapidly into the regulated US market. More than 100 smaller operations ranging from individual traders to teams of around 10 have shown interest. Professional counterparties are required for depth at scale; ordinary customers will not back the Philadelphia Eagles with $10 million or $20 million.
Sustainability Questions in the Exchange Model
Marantelli warns customers may lose money faster on exchanges than on conventional sportsbooks. The flexibility to enter and exit positions can encourage holding losing bets rather than accepting losses, and participants face a sharper audience than at DraftKings. “People will lose money faster on exchanges for lots of reasons,” Marantelli says. “It inherently increases spend, volatility, lots of things. And you’re playing against a sharper audience than you’re playing against at the DraftKings sportsbook.”
Kendrick references the early history of betting exchanges where retail liquidity sustained multiple market makers. As that pool weakens, sharper firms trade against one another and even skilled operators can lose. Kalshi increased its number of clients fivefold during the World Cup. White Swan predicts NFL prediction markets could generate between $5 billion and $7 billion of liability in a single week.
Faster losses risk drying up recruitment and re-recruitment. For now margins should “stay good during the growth period” before competition intensifies, according to reporting by iGaming Business. The structure resembles an outsourced sportsbook trading room more than casual opinion trading.
Reporting: iGaming Business (iGB)
Generated by SCCG’s automated editorial system from published source reporting. SCCG Management holds editorial responsibility.
Retail users face sharper counterparties than traditional sportsbooks, accelerating churn and threatening long-term sustainability of the exchange model.
We have worked with market-making firms and sharp betting groups across three continents for decades. As prediction platforms scale in the US, the dependence on professional liquidity — not true peer-to-peer action — changes the risk profile for operators, investors, and regulators. Retention metrics will tell the real story, and we are tracking them closely.
- White Swan accounts for up to 40% of volume on some exchanges; firms like Susquehanna and Jump Trading dominate parlay pricing
- More than 100 European sharp-betting groups have entered or are eyeing the US prediction market
- Retail users lose money faster against institutional counterparties than traditional sportsbook models, risking weaker retention
SCCG angle: SCCG has direct relationships with institutional market makers and sharp groups in the US and Europe. We help prediction platforms identify and vet liquidity partners, structure risk intelligently, and build retention models that account for sharper counterparties — critical as the peer-to-peer narrative meets industrial-scale reality.
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