The biotech industry – already plagued by insider trading – might see this problem get even worse due to the infection vector of prediction markets. Already, an outsized portion of insider-trading prosecutions involves the biotech industry. But that doesn’t come as a surprise – after all, whether the FDA approves or disapproves a process or product can be the difference between a company’s share price catapulting cartoonishly and cratering completely, making inside information about the status of FDA review particularly valuable. Many insiders in biotech companies find themselves at the dangerous intersection of unfettered access to material nonpublic information (MNPI), opportunities to profit from that information, and, unlike financial services employees, insufficient education in securities compliance.

In the last year alone, law enforcement agencies brought cases against (1) a biostatistician who analyzed clinical data [https://www.sec.gov/files/litigation/complaints/2026/comp26460.pdf]; (2) a contract documentary producer hired to create promotional materials about a biotech firm [https://www.sec.gov/files/litigation/admin/2026/34-105151.pdf]; and (3) a research scientist who bought short-dated out-of-the-money options after learning of imminent FDA action [https://www.sec.gov/files/litigation/admin/2026/34-104651.pdf]. There were others as well.

In the vast majority of these cases, insider traders were caught because market surveillance systems identified red flags, including anomalous trading patterns (like trades immediately preceding the announcement of market-moving information), large transactions made by those who have never traded equities before, or the purchase of short-dated out-of-the-money options. Because securities regulators have access to a wide range of trade data, it is relatively simple to identify unusual trading patterns and connect names to these trades. Regulators can thus track trades, identify who placed the applicable orders, and determine when and at what prices trades were made. All of this is because securities markets are designed to be relatively transparent.

But all of that might be changing, and for the worse.

On July 16, Kalshi opened a pilot suite of prediction markets where participants can take positions on clinical trial outcomes and FDA approvals. Kalshi has purportedly limited the markets to late-stage clinical trials being run by established biopharma enterprises, listing contracts only after patient enrollment is completed (to limit the risk that recruitment or enrollment decisions can be influenced by predictions), and required employment verification to more effectively police prohibited trading by insiders. However, despite Kalshi’s attempts to protect market integrity, the temptation to obtain an improper edge persists. Kalshi’s own biotech prediction markets announcement highlighted that “[t]he odds that a drug will succeed are among the most valuable numbers in the economy.” [https://news.kalshi.com/p/kalshi-biotech-prediction-markets].

In a world where a White House teleprompter operator misuses their insider position to trade contracts based on the content of President Trump’s speeches, it is unlikely that Kalshi’s precautions, laudable as they are, will eliminate insider trading. Leaving aside the fact that excluding insiders as participants does not prevent friends, family, or other tippees of those insiders from trading, there are several remaining causes for concern.

First, unlike the equities markets—which are policed by the Securities and Exchange Commission (SEC), United States Attorneys’ Offices (USAOs), state securities regulators, and attorneys general—prediction markets are currently regulated by the Commodity Futures Trading Commission (CFTC). By nearly every measure, the CFTC is a weaker regulator than its fellow agencies. Not only does it lack the manpower and enforcement experience of the other agencies, but the rule employed by the CFTC to combat insider trading—CFTC Rule 180.1 (which was adopted in 2011 and explicitly modeled after the SEC’s Rule 10b-5)—only recently reached its adolescence and is significantly less established. The CFTC’s expertise and enforcement capacity were not developed either in connection with issuer-specific MNPI or the biotech industry, important gaps in policing biotech prediction markets. Moreover, the CFTC has been criticized recently on the grounds that its closeness to the prediction markets has impaired its independence.

Second, prediction markets lack the transparent reporting infrastructure of securities markets. For example, prediction markets have no analog to the Consolidated Audit Trail (CAT), which allows regulators to track securities trading. Prediction markets do not give regulators the same ability to reconstruct trading activity, identify beneficial owners of accounts, trace order routing, examine account histories, and connect suspicious trades to people with access to MNPI. Even if the prediction markets themselves collect similar information, the CFTC cannot rely on the corporations operating them to perform its regulatory work. Without that enforcement and surveillance architecture at the governmental level, prediction markets risk becoming attractive venues for insider trading.

Third, Kalshi’s guardrails do not solve the tippee problem. Employment verification may keep some obvious insiders off the platform, but, in order to be effective, insider-trading enforcement must reach everyone who trades on the basis of MNPI. If enforcement is limited to those who obtain it directly from their employer, the workarounds are obvious and tempting.

Finally, given the light-touch regulatory oversight currently afforded to prediction markets, there is little reason to believe they will impose self-governance structures remotely approaching those employed by financial services firms. Traditional financial services firms have developed extensive compliance architectures including, for example, lists of securities that employees are barred from trading, MNPI controls, surveillance mechanisms, compliance training and certification, and monitoring of electronic communications. As former prosecutors, we remain skeptical that prediction markets, absent comparable regulatory requirements (or enforcement activity), will replicate those controls with the same rigor.

In many ways, this is a perfect storm. Insider trading risk – in an area already subject to insider trading – is migrating into a less transparent venue, overseen by a less experienced and underfunded enforcement agency. Biotech is already one of the most fertile areas for insider trading because clinical and regulatory developments produce sudden, binary, and often significant price movements. Opening a less transparent market for such wagers exacerbates the risk of insider trading. Unless and until prediction markets are subject to surveillance, identity-verification, reporting, and enforcement-access obligations similar to those applicable to the securities markets, we can expect the problem of biotech insider trading to metastasize.