Kalshi Rolls Out New Safeguards After Insider Trading Concerns Hit Prediction Markets

Prediction market operator Kalshi is rolling out new compliance measures aimed at addressing mounting concerns over insider trading, the exchange said Tuesday, the latest defense in a sector battling mounting insider trading concerns.

The new requirement applies to markets deemed at higher risk for insider trading or market manipulation, Kalshi said in a blog post.

The disclosure rule kicks in only for markets the exchange flags as carrying an elevated risk of insider trading or manipulation, such as contracts pegged to corporate performance, national security, and major geopolitical flashpoints such as the Iran war.

Traders who hit that threshold should fill out an online form with their employment details. Kalshi said it won’t check the information unless an investigation is already underway, though it may bar some users from individual contracts depending on where they work.

Risk scoring

Alongside the disclosure requirement, Kalshi announced a “risk scoring framework” designed to identify markets with elevated insider trading risk.

When a market is proposed for listing, it runs through a system weighing six factors, including corporate KPI or events risk, outcome concentration risk, market importance, regulatory risk, non-traditional insider risk, and national security risk.

Less important markets carrying high insider or manipulation risk may be rejected from listing entirely.

The framework also assesses whether markets pose potential national security concerns.

“By running an assessment on the national security risk a market might present before we list it, we can better prevent dangerous events from having a negative effect on our markets—or vice versa,” Robert DeNault, head of enforcement at Kalshi, wrote in the statement.

Other measures include expanded whistleblower tools that let users report suspicious activity directly to the company’s surveillance team, which monitors public order books 24/7.

The independent Surveillance Audit Committee, appointed to oversee the integrity and enforcement program, will continue delivering quarterly reports.

Kalshi confirmed Tuesday that it has opened more than 150 investigations this year, blocked more than 100 potential insider trades using new screening tools, referred more than 20 cases to law enforcement, and taken five disciplinary actions.

The platform fined and suspended three political candidates this year for trading on their own elections, a conduct it described as “political insider trading.” Last week, Rep. Bryan Steil (R-WI) announced plans to add language to the House congressional stock ban bill that would expand it to cover prediction markets.

Amid this, buoyed by more than $1 billion in perpetual futures volume within a week of launch, according to data shared with CNBC, Kalshi has wasted little time widening its reach, filing to self-certify contracts tied to 12 major altcoins, including Ethereum, XRP, Solana, and Dogecoin.

A useful filter

The employer disclosure metric is “a useful filter, not a solution,” Marcin Kazmierczak, co-founder and COO of modular oracle Redstone, told Decrypt.

The approach will catch obvious cases, like an employee trading their own company’s earnings contract, but it carries structural limits, Kazmierczak said, noting it is self-reported and that Kalshi only verifies once an investigation is triggered, giving “the honest disclose and the bad actors an incentive not to.”

Material non-public information “rarely travels through a clean employment line,” Kazmierczak said, adding that it moves through contractors, suppliers, advisors, friends, and family, “none of whom show up on an employer field.”

Disclosure works best “as an input into the risk-scoring and surveillance layer,” he said, rather than “as a gate on its own.”

The bigger risk is overreach, Kazmierczak said, noting prediction markets are only accurate because informed participants trade on what they know.

Kazmierczak said there is a “meaningful difference between someone trading on legitimate expertise and domain knowledge and someone trading on material non-public information,” warning that overly broad employer checks could end up restricting legitimate participants alongside true insiders.

He noted how users should have clear answers on how employment data is stored, accessed, and shared with regulators.

“None of this is unusual,” Kazmierczak said, noting that regulated brokerages face similar obligations. But he said it is “new territory for a prediction market” and that users should expect the same standards applied to financial venues handling identity and conflict data.