Examining Regulatory Challenges, Market Outlook

February 2026

EXECUTIVE SUMMARY

Prediction markets represent an innovative mechanism for aggregating dispersed information to forecast future events. However, Singapore’s regulatory approach has created a challenging environment for these platforms. In January 2025, Singapore blocked access to Polymarket, the world’s largest prediction market platform, classifying it as illegal gambling under the Gambling Control Act 2022.

This case study examines the current state of prediction markets in Singapore, the regulatory framework that governs them, potential solutions for reconciling innovation with regulatory concerns, and the broader economic implications of these policy decisions.

KEY FINDINGS Singapore banned Polymarket in January 2025 as part of a broader crackdown on unlicensed gambling that has blocked over 3,800 websites since 2015The Gambling Control Act 2022 creates a binary classification system that treats prediction markets identically to traditional gamblingResearch shows prediction markets consistently outperform traditional forecasting methods and opinion polls by aggregating information efficientlyPotential solutions include creating a distinct regulatory framework for information markets, implementing licensing regimes for qualified platforms, and fostering academic partnerships

1. CURRENT STATE: REGULATORY ENVIRONMENT

1.1 The Polymarket Ban

In January 2025, Singapore became the latest jurisdiction to restrict access to Polymarket, joining the United States, France, and Taiwan. Users attempting to access the platform from Singapore now encounter a warning from the Gambling Regulatory Authority (GRA) citing Section 20 of the Gambling Control Act 2022, which imposes penalties of up to S$10,000 in fines and six months imprisonment for using unlicensed gambling operators.

This action represents part of Singapore’s intensified enforcement against unlicensed online gambling. According to the Ministry of Home Affairs, over 3,800 websites have been blocked and approximately S$37 million worth of transactions have been intercepted since the enhanced enforcement regime began in 2015. Enforcement authority transferred from the GRA to the Singapore Police Force on January 1, 2025, signaling a harder stance on gambling regulation.

1.2 Legal Framework: The Gambling Control Act 2022

Singapore’s gambling regulatory framework underwent significant modernization with the passage of the Gambling Control Act 2022 and the Gambling Regulatory Authority of Singapore Act 2022, both enacted on August 1, 2022. These laws consolidated and updated Singapore’s fragmented gambling legislation, replacing the Betting Act, Common Gaming Houses Act, Private Lotteries Act, and Remote Gambling Act.

The GCA significantly expanded the definition of gambling to be technology-neutral and capture emerging products. Under Section 4 of the GCA, “betting” is defined as making or accepting a bet involving payment or staking of money or any thing of value on the outcome of any event or likelihood of anything occurring. This broad definition encompasses prediction markets, even when participants frame their activity as forecasting rather than gambling.

The definition of “thing of value” extends beyond fiat currency to include digital assets, virtual items, in-game credits, NFTs, loyalty points, and data access rights capable of transfer or monetization. This comprehensive scope ensures that blockchain-based prediction markets using cryptocurrency (like Polymarket, which operates on the Polygon blockchain with USDC) fall squarely within the regulatory framework.

1.3 Enforcement and Penalties

Singapore employs a multi-layered enforcement approach against unlicensed gambling:

• Website Blocking: Internet service providers are required to block access to unauthorized gambling sites
• Payment Blocking: Financial institutions must prevent transactions with unlicensed operators, with S$37 million intercepted to date
• Criminal Sanctions: Users face fines up to S$10,000 and imprisonment up to six months under the principle of personality jurisdiction
• Advertising Bans: Promotion of unlicensed gambling is prohibited, with fines up to S$20,000 for individuals

1.4 The Gambling-vs-Forecasting Distinction

A fundamental question remains unresolved in Singapore’s regulatory approach: where is the line between prediction markets and gambling drawn? Authorities appear to view platforms like Polymarket primarily through the lens of gambling rather than as forecasting tools, despite the platform’s positioning as an information aggregation mechanism.

This binary classification stands in contrast to the regulatory evolution occurring in other jurisdictions. For example, the U.S. Commodity Futures Trading Commission (CFTC) announced in January 2026 that it would develop new rules specifically for prediction markets, with Chairman Michael Selig stating it was “time for clear rules and a clear understanding that the CFTC supports lawful innovation in these markets.”

Singapore’s approach does acknowledge that certain financial products regulated by the Monetary Authority of Singapore (MAS) should not be treated as gambling. However, this carve-out appears limited to traditional derivatives and securities, not extending to event-based prediction contracts that might serve similar information aggregation functions.

2. MARKET OUTLOOK AND GLOBAL TRENDS

2.1 Global Growth of Prediction Markets

The prediction market industry has experienced explosive growth globally. Polymarket alone processed over $3.2 billion in wagers on the 2024 U.S. presidential election, with Bloomberg Terminal integrating Polymarket data into its platform for financial professionals. Kalshi, the U.S.-licensed competitor, has similarly seen rapid expansion, particularly after being featured in the popular animated series South Park in September 2024.

The proliferation of prediction markets reflects several converging trends: increased comfort with cryptocurrency-based financial products, growing distrust in traditional expert forecasts, technological improvements in market infrastructure, and a broader cultural shift toward data-driven decision-making. These platforms now offer contracts on diverse topics including political outcomes, economic indicators (GDP, inflation, interest rates), corporate events, sporting outcomes, and even popular culture phenomena.

2.2 Regulatory Fragmentation

The global regulatory landscape for prediction markets remains highly fragmented, creating challenges for platform operators and users alike:

Jurisdiction	Status	Regulatory Body	Approach
United States	Evolving	CFTC	New rulebook in development (2026)
Singapore	Prohibited	GRA / Police	Classified as illegal gambling
South Korea	Prohibited	Various	Treated as illegal private betting
Thailand	Prohibited	Police	Gambling law violations
Taiwan	Prohibited	Various	Active prosecution of users
France	Prohibited	ANJ	Banned under gambling regulations

This fragmentation creates significant barriers to the global expansion of prediction markets. Conservative approaches from Asian countries like Singapore, South Korea, and Thailand contrast sharply with signs of regulatory relaxation in the United States under the current administration.

2.3 Access via Traditional Brokers

Interestingly, while crypto-based prediction markets like Polymarket are blocked, Singaporean residents can still access prediction market products through regulated traditional brokers. Interactive Brokers, for example, was rated the best broker for prediction market trading available to Singapore residents in 2026. This creates a peculiar regulatory arbitrage where the same economic activity is permitted through one channel (regulated securities brokers) but prohibited through another (blockchain-based platforms).

This disparity suggests the regulatory concern may center more on the decentralized, cryptocurrency-based nature of platforms like Polymarket rather than the fundamental concept of event-based contracts. Traditional brokers offer stronger regulatory oversight, established anti-money laundering procedures, and integration with existing financial compliance frameworks—advantages that blockchain-based platforms currently struggle to match from a regulatory perspective.

3. POTENTIAL SOLUTIONS AND PATHWAYS FORWARD

3.1 Creating a Distinct Regulatory Category

The most fundamental solution would involve creating a distinct regulatory category for information markets or event contracts, separate from traditional gambling. This approach would recognize that prediction markets serve a different social function than casino gambling or sports betting, even when both involve monetary stakes on uncertain outcomes.

A tiered classification system could distinguish between:

• Pure Entertainment Gambling: Casino games, slot machines, and games of pure chance with no informational component
• Sports and Event Betting: Wagering on predetermined events with some skill component
• Information Markets: Event contracts designed primarily for forecasting and information aggregation, with clear public interest applications

Each category would be subject to different regulatory requirements, with information markets receiving treatment more akin to derivatives than traditional gambling. This approach mirrors the U.S. CFTC’s emerging framework, which treats prediction markets as event contracts under commodity exchange regulation rather than gambling regulation.

3.2 Licensing Framework for Qualified Platforms

Singapore could develop a licensing regime specifically for prediction market platforms that meet stringent criteria. This would maintain regulatory oversight while enabling innovation. Key licensing requirements could include:

Requirement	Implementation
Corporate Governance	Incorporation in Singapore or approved jurisdiction, detailed rulebooks, transparent governance structures, independent board oversight
AML/CFT Compliance	Robust customer due diligence, identity verification for all participants, enhanced checks for high-value transactions, transaction monitoring systems
Market Integrity	Insider trading prohibitions, manipulation prevention systems, fair pricing mechanisms, dispute resolution procedures
Consumer Protection	Position limits for retail participants, educational requirements, cooling-off periods, self-exclusion mechanisms, problem gambling safeguards
Market Scope	Restrictions on contract types (e.g., no assassination markets, no markets on local elections), focus on events with clear public interest, objective resolution criteria
Data and Research	Public data sharing for academic research, regular reporting to regulators, transparency in pricing and volume data

This approach would allow Singapore to maintain its reputation for strong financial regulation while creating space for innovation in information markets.

3.3 Academic and Research Partnerships

Singapore could leverage its strong academic institutions to develop evidence-based policy. Universities such as the National University of Singapore (NUS) and Singapore Management University (SMU) could establish research centers focused on prediction markets, studying their accuracy, manipulation resistance, and social value.

These institutions could operate licensed experimental prediction markets in controlled environments, generating local data on: forecasting accuracy for Singapore-relevant events (economic indicators, policy outcomes), manipulation attempts and countermeasures, participant behavior and decision-making patterns, optimal market design for different contract types, and the distinction between information aggregation and gambling behavior.

Academic partnerships would provide the empirical foundation for evidence-based regulation, potentially demonstrating that prediction markets can serve public interest functions while maintaining appropriate safeguards.

3.4 Sandboxes and Pilot Programs

Following Singapore’s successful model with fintech sandboxes operated by MAS, the GRA could establish a regulatory sandbox specifically for prediction market platforms. This would allow select platforms to operate under temporary licensing with close regulatory supervision, enabling:

• Testing of compliance frameworks in live market conditions
• Evaluation of consumer protection measures
• Assessment of market manipulation risks
• Comparison of forecasting accuracy against traditional methods
• Identification of unintended consequences or regulatory gaps

Sandboxes would provide regulators with practical experience before committing to permanent policy frameworks, reducing the risk of either overly restrictive or insufficiently protective regulations.

3.5 Regional Coordination

Given the cross-border nature of blockchain-based platforms, effective regulation may require regional coordination. Singapore could take a leadership role in ASEAN discussions on prediction market regulation, potentially developing harmonized standards that would:

• Reduce regulatory arbitrage between jurisdictions
• Share enforcement resources and intelligence
• Create larger, more liquid regional markets for licensed platforms
• Establish common standards for AML/CFT compliance
• Facilitate cross-border research and data sharing

4. IMPACT ANALYSIS

4.1 Economic Value of Prediction Markets

Research demonstrates that prediction markets offer substantial economic value through superior information aggregation. Studies show that prediction markets typically outperform professional forecasters, opinion polls, and individual experts across diverse forecasting domains.

Key findings from academic research include:

• Election Forecasting: Analysis of Iowa Electronic Markets data from 1988-2004 found that prediction markets provided more accurate estimates than 74% of opinion polls, with particular advantages in longer-term forecasting (100+ days before elections)
• Corporate Forecasting: Companies using internal prediction markets have successfully forecast product launch dates, sales figures, and project completion timelines more accurately than traditional methods
• Economic Indicators: Event contracts on Federal Reserve decisions and economic data releases have proven effective at aggregating market expectations
• Healthcare: Pilot studies show prediction markets can forecast disease outbreaks 2-4 weeks in advance of traditional surveillance methods

The efficiency of prediction markets stems from three key mechanisms: they aggregate diverse and dispersed information, they create financial incentives for truthful revelation of private information, and they provide ongoing incentives for discovering and trading on novel information.

4.2 Impact on Singapore’s Innovation Ecosystem

Singapore has cultivated a reputation as a global hub for financial innovation, blockchain technology, and fintech development. The prohibition of prediction markets creates several potential impacts on this ecosystem:

Impact Area	Analysis
Talent Migration	Blockchain developers and prediction market entrepreneurs may relocate to jurisdictions with clearer regulatory pathways, such as the United States or Switzerland
Capital Flows	Venture capital interested in prediction market platforms will direct funding elsewhere, reducing Singapore’s participation in this growing sector
Academic Research	Limited ability for Singaporean researchers to study live prediction markets, potentially reducing Singapore’s thought leadership in this area of economics and finance
Corporate Innovation	Singapore-based corporations cannot experiment with internal prediction markets for forecasting and decision-making, missing potential efficiency gains
Regulatory Leadership	Opportunity to shape global standards and best practices passes to other jurisdictions, particularly the United States and Switzerland

These impacts must be balanced against the legitimate regulatory concerns about consumer protection, money laundering, and problem gambling that motivate the current restrictive approach.

4.3 Consumer Protection vs. Innovation Trade-offs

The current policy represents a clear prioritization of consumer protection over innovation. This choice reflects several valid concerns:

• Problem Gambling: Any form of wagering on uncertain outcomes carries addiction risks, and Singapore has strong social policies against gambling harm
• Financial Crime: Cryptocurrency-based platforms present unique AML/CFT challenges that traditional financial institutions have developed decades of infrastructure to address
• Market Manipulation: The recent Polymarket incidents (e.g., the Venezuelan political outcome trade) demonstrate real manipulation risks
• Insider Trading: The Super Bowl advertising case study shows how prediction markets can be exploited by those with material nonpublic information

However, completely prohibiting prediction markets may represent an overcorrection. The licensing framework proposed in Section 3.2 would address these concerns while preserving the benefits of information markets. Platform-level restrictions on contract types, participant limits, and enhanced monitoring could mitigate risks without requiring total prohibition.

4.4 Social and Political Considerations

Beyond economic impacts, prediction markets raise important social and political questions. The Singapore government may have specific concerns about prediction markets on domestic political events, which could be seen as encouraging speculation on sensitive topics or potentially influencing political processes.

This concern is not unique to Singapore. South Korea experienced significant public attention when Polymarket featured contracts on President Yoon Suk-yeol’s impeachment crisis in early 2025. Korean media uniformly characterized such markets as “illegal private betting,” and the government emphasized that Korean citizens participating could face gambling charges.

Any licensing framework would need to carefully consider what types of contracts should be permitted. Options range from permitting only non-political forecasting (economic indicators, scientific outcomes) to allowing broader event coverage with safeguards against manipulation and insider trading. The appropriate balance depends on Singapore’s specific social values and political context.

4.5 Opportunity Costs

The prohibition of prediction markets creates several opportunity costs that should be quantified in any comprehensive impact assessment:

• Foregone Forecasting Accuracy: Singapore’s government and corporations miss opportunities to leverage market-based forecasts for planning and decision-making
• Lost Tax Revenue: Licensing fees and transaction taxes on prediction market activity would generate government revenue currently captured by offshore platforms or not occurring at all
• Reduced Market Efficiency: Traditional financial markets benefit from the price discovery and information aggregation that prediction markets provide. Singapore’s capital markets may be less efficient without this complementary information source
• Academic Disadvantages: Singaporean universities cannot conduct cutting-edge research on prediction market design, potentially affecting their competitiveness in economics and finance programs

5. CONCLUSIONS AND RECOMMENDATIONS

5.1 Summary of Key Issues

Prediction markets represent a fundamental tension between innovation and regulation. These platforms demonstrate clear value in aggregating dispersed information and producing accurate forecasts, yet they also present legitimate concerns about gambling harm, financial crime, and market manipulation.

Singapore’s current approach—complete prohibition under gambling law—represents one end of the regulatory spectrum. This approach prioritizes consumer protection and social stability but potentially sacrifices economic efficiency, innovation opportunities, and Singapore’s position as a global fintech leader.

5.2 Recommended Policy Framework

Based on this analysis, we recommend a middle-path approach that would:

• Create a distinct regulatory category for “information markets” separate from traditional gambling
• Establish a licensing framework with stringent requirements for corporate governance, AML/CFT compliance, market integrity, and consumer protection
• Launch a regulatory sandbox to test different approaches before committing to permanent policies
• Partner with academic institutions to build an evidence base for policy decisions
• Engage in regional coordination to develop harmonized ASEAN standards
• Implement restrictions on contract types, particularly regarding domestic political events and sensitive topics
• Require licensed platforms to contribute data for public research and policy analysis

5.3 Implementation Timeline

A phased implementation would allow for careful evaluation at each stage:

Phase	Timeline	Activities
Phase 1	6-12 months	Public consultation, academic research partnerships, international regulatory comparison, drafting of proposed framework
Phase 2	12-24 months	Regulatory sandbox launch with 2-3 selected platforms, academic pilot programs at NUS and SMU, data collection and analysis
Phase 3	24-36 months	Evaluation of sandbox results, refinement of licensing requirements, preparation of final regulatory framework
Phase 4	36+ months	Full licensing regime implementation, ongoing monitoring and adjustment, regional coordination initiatives

This timeline allows for thorough evaluation while maintaining Singapore’s cautious approach to financial innovation.

5.4 Final Observations

Prediction markets sit at the intersection of finance, technology, and social policy. Singapore’s response to this innovation will signal its broader approach to emerging financial technologies and could influence regional policy development.

The current outright ban represents a low-risk but potentially high-cost approach. While it eliminates concerns about gambling harm and financial crime from prediction markets, it also forecloses opportunities for Singapore to lead in developing appropriate regulatory frameworks for information markets.

As other jurisdictions, particularly the United States, move toward creating clear rules for prediction markets, Singapore faces a choice: maintain its restrictive stance and cede this space to other financial centers, or develop a sophisticated regulatory approach that preserves its values while capturing the benefits of this innovation.

The recommended phased approach would allow Singapore to carefully test different models, build an evidence base for policy decisions, and maintain its reputation for both regulatory rigor and financial innovation. This balanced path acknowledges both the legitimate concerns about prediction markets and their potential value as tools for information aggregation and economic forecasting.

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