Kalshi, the prediction market platform, now requires users to disclose employment details before accessing certain high-risk markets. The screening targets trades where insider information could swing outcomes.
The compliance move flags markets with elevated manipulation risk. Users trading those contracts must reveal their job titles and employers. Kalshi blocks individuals with access to material nonpublic information from placing bets on related outcomes.
Prediction markets operate on information efficiency. A pharmaceutical executive shouldn't trade on FDA approval odds for their company's drug. A government worker shouldn't bet on policy announcements before they drop. Kalshi's vetting attempts to prevent exactly that.
The disclosure requirement reflects growing regulatory scrutiny around prediction markets. The CFTC has warmed to these platforms in recent years, but only with guardrails. Insider trading restrictions are table stakes. Without them, prediction markets lose credibility as price-discovery tools and become vehicles for information leakage.
This isn't Kalshi's first compliance move. The platform has previously restricted users from certain markets based on professional conflicts. The new system automates those checks at scale. Users input job details. Kalshi's algorithm flags conflicts. Access gets denied if necessary.
The stakes matter here. Prediction markets have exploded in size and scope since the CFTC granted conditional approval to Kalshi and Polymarket. Billions now flow through these platforms. As volume increases, so does regulatory heat. Exchanges that fail to police themselves face enforcement action.
Kalshi's move also protects the platform itself. Lax insider trading controls invite CFTC investigation. The regulator has shown willingness to shut down platforms that ignore the rules. Better to screen users upfront than face charges later.
For traders, the requirement stings. Legitimate users with relevant jobs lose access to markets. But that's the tradeoff. Prediction markets work only