Prediction Markets are Getting Bigger. Is It Time to Build Your Own?

Author : tessa hardin | Published On : 20 Aug 2026

What if you could turn uncertainty into useful market intelligence?

Every day, businesses make decisions about things they cannot know for certain.

Will demand increase next quarter?

Will commodity prices rise?

Will a new product succeed?

Will a supply-chain disruption happen?

Will a particular event occur?

Traditionally, businesses have relied on analysts, surveys, historical data, and expert opinions to answer these questions.

But prediction markets offer another approach: let people put their expectations into a market and see what the collective signal says.

And in 2026, this idea is getting much bigger.

Prediction markets are no longer just about politics and sports

If you've followed prediction markets recently, you've probably noticed how quickly the conversation has changed.

Platforms such as Kalshi and Polymarket have expanded the types of events people can trade, while institutional participation is becoming a much bigger part of the story.

In May, Reuters reported that Kalshi's annualized trading volume had climbed to $178 billion, more than tripling in six months, while institutional trading volume had increased by 800% over the same period.

And the latest development is even more interesting.

Cantor Fitzgerald has now launched prediction-market trading for institutional clients through Kalshi, giving roughly 3,000 institutional clients access to event contracts. The move is designed to make larger trades possible and bring prediction markets closer to traditional institutional trading infrastructure.

That changes the conversation.

Prediction markets aren't simply attracting people who want to speculate on the next election or sporting event.

They're increasingly being explored as tools for hedging, forecasting, market intelligence, and managing uncertainty.

But there's a problem businesses can't ignore

The excitement around prediction markets can make building one sound simple.

Create an event.

Add Yes/No outcomes.

Let users trade.

Settle the result.

Done, right?

Not really.

The moment real money and real users enter the picture, the difficult questions begin.

How will you create liquidity?

How will you determine whether an event has actually happened?

Who provides the outcome data?

How will disputes be handled?

How will you prevent market manipulation?

How will you verify users?

What markets are legally permitted in your target jurisdiction?

And perhaps the biggest question:

Why would users choose your prediction market instead of an existing platform?

These are the challenges that turn a prediction-market idea into a serious product-development project.

This is where a Custom Prediction Market Solution becomes interesting

A generic prediction platform may give you a basic marketplace.

But businesses don't necessarily need another generic marketplace.

They may need a platform built around their audience, their data, their market categories, and their business model.

That's where a Custom Prediction Market Solution can offer a different approach.

Imagine a business building prediction markets specifically around:

Commodity prices
Weather events
Product demand
Supply-chain risks
Technology adoption
Corporate performance
Energy markets
Carbon markets
Financial events
Industry-specific forecasts

The goal isn't simply to ask people what they think will happen.

It's to create a structured environment where collective expectations can become measurable signals.

And that's where prediction markets can become particularly useful for businesses.

From “What will happen?” to “What should we prepare for?”

This is probably one of the most important shifts happening in the space.

A prediction market can provide more than an answer.

It can provide a signal.

Suppose a company believes demand for a particular product will increase by 20%.

Instead of relying entirely on an internal forecast, it could create a market where qualified participants express their expectations.

If the market consistently points toward higher demand, that could become another input for business planning.

The same concept could potentially be applied to supply-chain disruptions, commodity movements, weather-related risks, or other measurable events.

Of course, a prediction-market price isn't automatically a guaranteed forecast. Markets can be wrong, manipulated, illiquid, or influenced by incomplete information.

That's why market design matters.

AI could make prediction markets even more interesting

Another major trend is the growing intersection between AI and prediction markets.

AI can potentially help platforms analyze market behavior, identify unusual activity, summarize relevant information, improve discovery, and provide users with additional context.

Imagine opening a market and seeing more than just:

YES — 64%

An AI-powered platform could potentially explain the major factors influencing that probability, summarize relevant developments, highlight changing sentiment, and help users understand why the market is moving.

But there's an important distinction.

AI shouldn't replace the market.

It can enhance the information surrounding the market.

That combination—collective intelligence from participants plus AI-powered analysis—could make future prediction platforms considerably more useful.

Institutions are bringing another challenge: scale

As institutional participation grows, prediction platforms need to think differently about infrastructure.

Larger participants need:

Better liquidity
Reliable execution
Strong risk controls
Institutional-grade reporting
Secure account management
Robust APIs
Market surveillance
High-performance infrastructure
Clear settlement mechanisms

The recent Cantor Fitzgerald development is a good example of this shift. Its institutional offering is specifically designed to facilitate larger event-contract trades, with Susquehanna providing pricing and liquidity support.

In other words, the market is developing from a retail-facing concept toward something that can also support professional financial participants.

But regulation remains a major pain point

There's another side to the growth story.

Prediction markets are expanding quickly, but regulators are still debating how different types of event contracts should be classified and governed.

For example, Kalshi is currently involved in a dispute with Nevada regulators over geofencing requirements, highlighting the continuing tension between federal and state approaches to prediction markets.

There are also growing concerns around market manipulation, insider information, and sensitive event markets.

That means businesses considering prediction-market development cannot treat compliance as something to figure out after the platform is built.

Market selection, user eligibility, geofencing, KYC/AML, surveillance, settlement rules, and regulatory requirements need to be considered from the beginning.

So, what does the future look like?

Prediction markets are evolving quickly.

The market is becoming more institutional.

The categories are expanding.

AI is entering the picture.

And businesses are beginning to see prediction markets as potential tools for forecasting and risk intelligence, not simply speculation.

But the biggest opportunity may not be creating another platform with thousands of random markets.

It could be creating a purpose-built prediction ecosystem around a specific industry, audience, or business problem.

That's the real potential of a Custom Prediction Market Solution.

Instead of asking:

“Can we build a prediction market?”

Businesses should perhaps ask:

“What uncertainty in our industry could become a measurable market signal?”

Because you can't predict everything.

But you can create better ways to listen to what the market thinks is coming next.

What would you build a prediction market around business risks, commodities, customer demand, technology, weather, or something completely different?