
Kalshi now requires users to disclose employer and certify no material nonpublic information before trading, creating a paper trail for regulators and shifting prediction-market risk.
Kalshi will now ask you where you work before you can start betting.
The prediction market added several new guardrails to crack down on insider trading and abusive behavior. Kalshi's enforcement and legal counsel, Bobby DeNault, wrote a blog post on the company's website detailing the policy shift. Users must now disclose their employer, their position, and whether they hold material nonpublic information about any contract they would trade.
DeNault described the move as a "first line of defense" against traders who bet with nonpublic information. The firm already banned employees of federal agencies and national labs from trading contracts on their own agencies. The expanded rule widens that net to any employer whose work might overlap with a Kalshi contract – a practical check for anyone working inside a company or regulator covered by the exchange's event contracts.
Prediction markets live or die on their reputation for accuracy. A string of insider bets after the 2024 election cycle raised questions about who trades what and when. Kalshi's fix is direct: before you place a trade, you certify that your employer's inside knowledge is not in play. The system also checks employment data against past trading patterns to flag mismatches.
The policy does not ban trading outright for most people. It creates an attribution chain. If a trader from a relevant company makes an unusually timed bet, Kalshi now has a named employer and a certification on file. That is a meaningful upgrade from the "trust us" model that smaller prediction platforms have leaned on.
Kalshi's contracts cover macroeconomic events, policy outcomes, and corporate actions like mergers or rate decisions. A trader who works at a company targeted by a contract – or at the regulator reviewing it – could have a window into the outcome before the public. The new form forces a per-session acknowledgment: you confirm you hold no MNPI relevant to the contract you are buying. The employer field is cross-checked against internal databases on past activity.
DeNault did not specify penalties for a false certification, the blog post referenced "permanent bans" for violations. Civil liability under securities laws for insider trading on prediction markets remains contested territory. The certification creates a paper trail that regulators could subpoena.
Kalshi is the largest regulated prediction market in the U.S. Its policy changes often set the floor for smaller competitors. The employer check follows a pattern: Kalshi has moved from permissionless betting toward structured entry over the last 18 months. Withdrawal limits, KYC checks, and now employer verification all point to a platform preparing for regulatory scrutiny under a potential CFTC regime that demands better market integrity.
Critics still raise questions about enforcement. A self-reported employer box catches honest users. It may not deter a determined insider. Even so, the psychological barrier – typing your employer and clicking "I certify" before a trade – changes the risk calculation for most users. The cost of lying on a form that ties back to a named individual is higher than the cost of betting on a platform without one.
Kalshi users who work at companies or agencies with exposure to event contracts now face a choice: certify their employment each session or walk away. For the platform, the real test will come when the first enforcement action against a false certification hits. If Kalshi bans a user and publishes the rationale, the rule becomes credible. If the cases sit in a drawer, the backlash from critics who called the policy window dressing will get louder.
Prepared with AlphaScala research tooling and grounded in primary market data: live prices, fundamentals, SEC filings, hedge-fund holdings, and insider activity. Each story is checked against AlphaScala publishing rules before release. Educational coverage, not personalized advice.