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Prediction markets are moving from experiment to action as banks and FinTechs look for new inputs into forecasting and decision-making.
More than half of banks report monitoring prediction market data, reflecting growing interest in a tool that offers a window into how market participants view future events. As economic conditions, regulation and customer behavior shift more quickly, some financial institutions are beginning to explore whether these markets can provide an additional source of insight.
Few expect prediction market signals to replace economic indicators, financial models or management judgment. Their potential lies in showing how expectations are changing in real time, giving organizations another way to challenge assumptions and assess developments that could affect future performance.
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Read moreA leading group of financial institutions has already moved past watching prediction markets and started acting on them. In an EY-Parthenon survey of 150 senior practitioners across US banks and FinTechs, one in five respondents have used prediction market data to influence at least one business decision, with FinTechs further ahead than banks. For everyone else, the question is shifting from whether these signals are interesting to where they can add value alongside existing forecasting and decision-making tools.
The appeal of prediction markets comes at a time when financial institutions are looking for better ways to interpret a constant flow of new information. The challenge is rarely access to data. It is understanding which developments deserve attention and which can be ignored.
Prediction markets address this challenge from a different angle. By capturing how participants assess the likelihood of future events, they can provide another real-time view of how expectations are shifting. For banks and FinTechs, that can provide an early indication that assumptions, forecasts or risk assessments deserve a second look.
Some organizations are already testing this idea in practice. Interest is beginning to extend beyond monitoring and into real-world use.
Most financial institutions are still evaluating prediction market data rather than relying on it. Nearly half of survey respondents report monitoring or assessing these markets, while a smaller group is testing specific applications. Only a limited number have incorporated the insights into ongoing operations.
The survey points to a subtle difference in how banks and FinTechs approach the signals. More than half of banks report monitoring prediction market data, reflecting growing interest in an information source that offers a window into how market participants view future events. FinTechs, meanwhile, are somewhat further along, with a larger share piloting use cases and deploying applications in selected business areas.
The difference appears to be less about interest than implementation. Banks are often evaluating where prediction market data fits within existing forecasting, risk assessment and governance structures. FinTechs are more likely to test the data in targeted applications and refine its use over time. While adoption remains early across the industry, the survey suggests FinTechs are moving more quickly from evaluation to application.
Banks and FinTechs are focusing their prediction market activity on different parts of the business. Banks are concentrating almost entirely on planning and risk, with the highest levels of activity in profit and loss (P&L), balance sheet and business projections, portfolio management, and stress testing. Commercial use cases barely register. Most bank respondents have not discussed product market fit, fraud and financial-crime monitoring, or product pricing. FinTechs spread their attention more evenly across the portfolio, pairing the same planning use cases with customer-facing and operational applications, including product pricing, fraud and financial-crime monitoring, and product market fit. Hedging remains an emerging area for both banks and FinTechs, with most organizations still evaluating potential use cases rather than actively deploying strategies.
For executives, the takeaway is clear that banks and FinTechs are interested in the same underlying signal but are putting it to work in different ways. Both are starting with targeted use cases rather than broad deployment.
Forecasting and scenario planning stand out as the most promising uses for prediction market data. Survey respondents report paying closest attention to market expectations around Federal Reserve policy, interest rates, inflation and regulatory developments, all areas with the potential to affect financial performance.
Across both groups, institutions are using prediction market signals in much the same way, as an external check on their own conviction. Rather than replacing traditional forecasting tools, the data provides an independent measure of how market participants assess future outcomes. Used alongside existing models, it can help organizations pressure-test forecasts and evaluate a wider range of scenarios.
Used alongside economic indicators, internal forecasts and external research, the signals can help test assumptions and identify developments that might otherwise be overlooked.
The harder question is how and when to use it. As adoption expands, institutions with clear processes for incorporating prediction market insights into forecasting, planning and risk assessment will be better positioned to benefit.
Banks and FinTechs see value in prediction market data, but for different reasons. Nearly two-thirds of bank respondents view it as a useful addition to forecasting and planning, supporting a more measured approach to evaluating future conditions, while FinTechs place a higher premium on speed. More than 60% of FinTech respondents cite the continuous flow of updated information as a key benefit, reflecting the need to respond quickly as market conditions change.
However, both groups are drawn to the same core feature: Prediction markets convert uncertainty into probabilities. This gives organizations another way to assess potential outcomes and test internal assumptions against market expectations.
Governance remains one of the biggest hurdles to broader adoption. Legal and compliance review ranks as the top requirement, cited by 71% of banks and 65% of FinTechs. Banks are focused on auditability, controls and clear escalation procedures, while FinTechs place greater emphasis on data validation and vendor due diligence.
The priorities may differ, but the objective is the same: understanding where the data comes from, how forecasts are generated and how their role in business decisions can be explained. The differentiator may ultimately be less about access to prediction market data and more about whether institutions can build the governance, validation and decision frameworks needed to use it consistently.
Over the next 24 months, 43% of respondents expect to use prediction market data as a supplemental input for forecasting and scenario planning, while another 15% anticipate regular use in selected business functions.
This suggests gradual adoption rather than a major shift. Institutions appear more interested in adding these signals to existing processes rather than building new ones around them.
Probably not. The survey points to a more practical role for prediction markets: helping institutions supplement existing forecasts, not replace them.
Financial institutions are exploring where market-based probabilities can add value, particularly when the outlook is uncertain or changing quickly.
Pedro Tellez, Manager, Strategy and Transactions, Transactions & Corporate Finance (TCF), SaT - TCF - Quantitative Finance and Economics contributed to this article.
Prediction market signals are gaining traction in financial services, as banks and FinTechs look for new ways to improve forecasting, planning and decision-making. An EY-Parthenon survey found that one in five organizations have already used these insights to influence a business decision, with FinTechs leading adoption. While interest is growing, firms currently see prediction markets as a complement to existing forecasts, not as a replacement. The biggest opportunity lies in helping organizations challenge assumptions and navigate uncertainty. As adoption expands, strong governance, transparency and clear business value will determine which institutions move from experimentation to lasting impact.
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