Kalshi will begin requiring traders to disclose their employers before they can participate in certain high‑risk markets, part of a broader “market integrity” package the prediction market operator said took effect on Tuesday, June 9, 2026. The move is designed to curb insider trading and manipulation as interest in crypto‑linked derivatives and event markets accelerates.

Technology Overview

The new disclosure step is targeted rather than universal. Kalshi said the requirement applies only to markets it designates as having elevated insider‑trading or manipulation risk—examples include contracts tied to corporate performance, national security developments, and major geopolitical flashpoints such as the Iran war. For those markets, traders will be prompted to complete an online form with employment information. Kalshi noted it does not proactively verify the data unless an investigation is underway, but the exchange may still restrict access to specific contracts based on a user’s role or employer.

Alongside the disclosure rule, the company unveiled a risk‑scoring framework intended to evaluate the insider‑trading profile of a market before it lists. The framework weighs six factors: corporate KPI or events risk, outcome concentration risk, market importance, regulatory risk, non‑traditional insider risk, and national security risk. Markets considered less important but carrying high manipulation or insider risk may be rejected entirely as a result of this pre‑listing assessment.

Kalshi said the framework explicitly considers potential national security implications. By running these assessments prior to listing, the company aims to prevent sensitive developments from distorting markets—or the markets themselves from creating perverse incentives around sensitive events—before orders ever reach the book.

How It Works

The employer‑disclosure form slots into Kalshi’s existing surveillance stack. The exchange already collects identification information from all traders and says it operates continuous monitoring of public order books around the clock. The new step functions as an additional data point for case triage and investigation, while also creating a pathway to preemptively limit trading access for individuals whose professional roles could pose conflicts on specific markets.

Kalshi complemented the disclosure requirement with expanded whistleblower tools. Users can now report suspicious behavior directly to the company’s surveillance team, which is tasked with 24/7 monitoring and investigation. An independent Surveillance Audit Committee, appointed to oversee integrity and enforcement procedures, will continue to issue quarterly reports on the program’s performance, according to the company.

The exchange said it has escalated its enforcement posture throughout the year. Kalshi reported opening more than 150 investigations so far, blocking over 100 potential insider trades using new screening systems, referring more than 20 matters to law enforcement, and taking five disciplinary actions. Earlier in the year, the platform fined and suspended three political candidates for trading on markets tied to their own elections—a practice Kalshi characterized as “political insider trading.”

Industry Impact

The measures arrive amid heightened scrutiny of prediction markets, which in recent months have faced a succession of insider‑trading incidents, congressional attention, and criminal cases. The debate is increasingly relevant to crypto‑native audiences: Kalshi has leaned into crypto‑linked derivatives, reporting more than $1 billion in perpetual futures volume within a week of launch and moving to self‑certify contracts tied to a dozen major altcoins, including Ethereum, XRP, Solana, and Dogecoin. As liquidity deepens and product scope widens, the pressure to demonstrate robust surveillance, conflict controls, and transparent enforcement has intensified.

The company’s risk‑scoring approach creates a technology‑driven filter that attempts to prioritize markets where informed trading enhances price discovery while downgrading or excluding contracts whose structure concentrates outcomes or invites exploitation by non‑public information. In practice, this kind of scoring system can guide both listing decisions and subsequent surveillance thresholds—elevating alert sensitivity where the pre‑listing assessment identified heightened risk and reducing it where markets are less likely to attract insider flows.

Expert View: Limits and Trade‑Offs

Marcin Kazmierczak, co‑founder and COO of modular oracle Redstone, called employer disclosure “a useful filter, not a solution.” He argued the rule is well‑positioned to catch straightforward conflicts—such as an employee trading a contract tied to their own company’s earnings—but pointed out structural limits: the information is self‑reported and, by Kalshi’s own description, only verified once an investigation is triggered. That dynamic creates an incentive for honest users to comply while offering little deterrence for those inclined to hide affiliations.

Kazmierczak also cautioned that material non‑public information rarely flows neatly along formal employment lines. Insights can move through contractors, suppliers, advisors, friends, and family—relationships that would not appear in a simple employer field. For that reason, he suggested the disclosure step is most effective as an input to the platform’s risk‑scoring and surveillance layers rather than as a hard gate that attempts to prevent all potential conflicts at the door.

He warned of the risk of overreach. Prediction markets derive accuracy from informed participation, and there is a meaningful difference, he said, between trading on legitimate expertise and domain knowledge and trading on material non‑public information. If employer‑based checks are applied too broadly, they could deter or exclude legitimate participants alongside true insiders. He added that users should have unambiguous answers about how employment data is stored, who can access it, and under what circumstances it is shared with regulators.

While none of these obligations are unusual in the context of regulated brokerages, Kazmierczak characterized them as relatively new ground for a prediction market. Given that, he said users should expect the same data‑handling and conflict‑management standards that financial venues apply to identity and sensitive information.

Future Implications

Kalshi’s updated program sketches a layered defense built around pre‑listing risk categorization, targeted employer disclosure, real‑time surveillance, and independent audits. In theory, that architecture aims to screen out trades based on material non‑public information without stifling the knowledgeable participation that makes event markets and crypto‑linked derivatives useful. The company’s willingness to reject high‑risk, low‑importance contracts at the listing stage suggests a conservative approach to where the platform seeks to concentrate liquidity.

At the same time, the policy environment continues to evolve. Rep. Bryan Steil said he plans to add language to the House congressional stock ban bill to extend its scope to prediction markets, underscoring that compliance expectations may tighten further. With an expanding product set that includes perpetual futures and proposed listings tied to leading altcoins, Kalshi’s surveillance posture—and the practical execution of its risk‑scoring framework—will likely remain central to how the platform balances openness with integrity in the months ahead.