Why Prediction Markets Need Trust and Safety Professionals

2026-05-15 · Source: Tech Policy Press · Field: Finance & Economics — FinTech & Digital Financial Services, Capital Markets & Investment Management, Economic Analysis & Policy · Depth: Novice, medium

Summary

Prediction markets, exemplified by platforms like Kalshi and Polymarket, are rapidly expanding beyond niche fintech into a central feature of the financial ecosystem, with valuations projected to exceed $20 billion. This growth, accelerated by Kalshi's 2024 court victory enabling US election markets and subsequent expansion into sports, introduces new risks beyond traditional financial fraud. Concerns include insider trading, with cases like an OpenAI employee's dismissal and a US soldier charged for profiting from classified intelligence on Polymarket. Additionally, there is evidence of information manipulation, including altered public data and threats to journalists to influence market outcomes. Existing regulatory frameworks, like those from the CFTC for sports betting, are insufficient for the broader scope of these markets, which now encompass geopolitical conflicts and political leader tenures, necessitating a more comprehensive integrity approach.

Key takeaway

For executives overseeing financial technology or information platforms, the rapid expansion of prediction markets necessitates a proactive approach to integrity. Your organization should consider establishing dedicated "prediction market integrity" roles, integrating trust and safety expertise into governance structures. This will help address risks like insider trading and information manipulation that extend beyond traditional financial oversight, protecting both market integrity and organizational reputation.

Key insights

Prediction markets require dedicated trust and safety expertise to mitigate risks beyond traditional financial fraud, especially information manipulation and real-world harm.

Principles

• Market design influences participant incentives and potential for harm.
• Prediction markets can actively shape, not just reflect, reality.

Method

Integrate trust and safety professionals into prediction market governance to design market rules, conduct pre-listing risk assessments, manage insider threats, and inform regulatory frameworks.

In practice

• Implement pre-listing risk assessments for new market contracts.
• Establish internal policies restricting trading based on nonpublic information.
• Adapt incident response frameworks for external threats to journalists.

Topics

• Prediction Markets
• Trust and Safety
• Insider Trading
• Information Manipulation
• Regulatory Frameworks

Best for: Executive, Investor, Entrepreneur, Legal Professional, Policy Maker

Related on AIssential

• Everything is gambling now: the latest news on prediction markets like Polymarket and Kalshi — Prediction markets are rapidly integrating into media and finance, raising significant regulatory and ethical concerns.
• Beyond detection: 5 pillars of proactive corporate fraud prevention — Proactive fraud prevention requires structured governance, specialized teams, and clear risk appetite to combat evolving threats effectively.
• Sethi on “Betting on Geopolitical Violence” — Crypto-based prediction markets enable anonymous speculation on geopolitical violence, attracting insider trading and potential manipulation.
• AI in Finance: Addressing the Failure of Current Models (Uncertainty, Risk, and Trust) — Financial models often fail due to lack of trust during market shifts, necessitating uncertainty-aware approaches.
• Meta Gambles With Its Trust In Prediction Markets — Prediction markets face broad consumer skepticism, intensified by Meta's trust issues and perceived addictive design.
• The Math Behind the Bots Dominating Prediction Markets — Strict mathematical models, not sentiment, drive consistent profitability in prediction markets.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Policy Press.