On October 14, 2025, Kristalina Georgieva, Managing Director of the International Monetary Fund, delivered a stark warning to the world: countries are catastrophically unprepared for artificial intelligence. Speaking at the annual IMF and World Bank meetings, Georgieva emphasized that the global community lacks the fundamental regulatory and ethical frameworks necessary to manage AI’s unprecedented technological transformation. Her comments paint a troubling picture of a widening digital divide and suggest that without urgent action, developing nations will be left further behind in the AI-driven global economy.

This analysis examines Georgieva’s warnings in depth, explores the IMF’s AI Preparedness Index framework, and evaluates what these global trends mean specifically for Singapore—a nation positioned as a regional AI leader yet facing its own regulatory challenges.

The Global AI Governance Gap

The Core Problem: Regulation and Ethics Lag Behind

Georgieva’s most striking observation was that “the world is falling shortest” on regulation and ethics. This represents a fundamental crisis in how the international community approaches AI governance. While countries have made progress on digital infrastructure and skills development, the crucial pillars of regulatory frameworks and ethical guidelines remain underdeveloped across nearly all nations.

The problem is not merely theoretical. As AI systems increasingly make decisions affecting employment, credit, healthcare, criminal justice, and government services, the absence of robust ethical frameworks creates real risks of discrimination, bias, and societal harm. Georgieva’s call for civil society groups to “ring the alarm bells” reflects the urgency of this moment—policymakers at all levels must prioritize regulation and ethics before AI systems become so embedded in society that retrofitting governance becomes impossible.

A Technological Revolution Dominated by Advanced Economies

Georgieva highlighted a crucial disparity: the AI revolution is overwhelmingly concentrated in advanced economies, particularly the United States, which holds “the lion’s share” of global AI capability and development. While some emerging markets—notably China—have developed substantial AI capabilities, the vast majority of developing nations remain largely excluded from the AI economy.

This concentration creates a dual problem. First, it means that AI development priorities reflect the values and interests of wealthy nations, potentially marginalizing the needs and concerns of poorer countries. Second, it reduces the opportunity for developing nations to build indigenous AI capability and reap the economic benefits of this technological transformation. The result is what Georgieva termed a “growing gap” between advanced and low-income countries that “makes it harder and harder for developing countries to catch up.”

The Vulnerability of Emerging and Developing Markets

The IMF expresses particular concern about emerging markets and low-income countries. Georgieva noted that the IMF is “quite worried” about the widening AI readiness gap because of its potential to exacerbate existing economic inequalities. For developing nations with limited resources, competing with advanced economies in AI development appears increasingly impossible.

More immediately concerning is the risk that as AI becomes central to global supply chains, financial systems, and trade networks, developing countries without adequate AI governance frameworks will be vulnerable to systemic risks. A financial contagion originating in poorly-regulated AI systems in one country could rapidly spread globally, with developing nations suffering disproportionate harm due to their lesser capacity to absorb shocks.

The IMF’s AI Preparedness Index: A Comprehensive Framework

Four Dimensions of AI Readiness

To address these concerns systematically, the IMF has developed an AI Preparedness Index that evaluates countries across four critical dimensions:

Digital Infrastructure encompasses the foundational physical and technological systems required for AI deployment. This includes broadband connectivity, data center capacity, computing power, and the telecommunications backbone necessary to support AI applications. Countries with weak infrastructure cannot effectively implement AI systems, regardless of other capabilities.

Labor and Skills assesses the availability of human capital capable of developing, deploying, and governing AI systems. This includes formal education in computer science and mathematics, technical training programs, and the broader population’s digital literacy. Equally important are labor market policies that facilitate worker transition and social safety nets that protect workers displaced by AI automation.

Innovation and Economic Integration measures a country’s capacity to develop new AI applications and integrate them into existing economic systems. This includes research and development spending, patent activity, entrepreneurial ecosystems, and the regulatory environment for startup activity. Countries that cannot innovate in AI risk becoming permanent consumers of foreign AI products rather than creators.

Regulation and Ethics represents the critical dimension where Georgieva says the world is falling shortest. This includes the existence and effectiveness of AI regulations, the adoption of ethical frameworks and guidelines, and mechanisms for stakeholder collaboration in managing AI risks. It also encompasses the adaptability of legal frameworks to digital business models and the capacity of regulators to keep pace with technological change.

The Troubling Trend: Regulation as the Weakest Link

While many countries have begun investing in digital infrastructure and pursuing skills development, progress on regulation and ethics has been sluggish. This reflects several challenges: the rapid pace of technological change outpaces legislative processes, there is limited global consensus on AI ethics principles, and many policymakers lack the technical expertise to craft effective regulations.

The consequence is a patchwork of inconsistent and sometimes contradictory AI regulations across countries. The European Union’s AI Act represents the most comprehensive approach, but even European nations struggle with implementation. Meanwhile, many countries have either minimal AI regulation or rely on outdated laws crafted for earlier technologies.

Singapore: A Regional Leader Confronting Challenges

Singapore’s Position in the Global AI Landscape

Singapore presents an intriguing case study. According to IMF data, Singapore ranks among the world’s leaders on AI regulation and ethics, scoring 0.22—higher than most countries globally and among the highest in Asia. This reflects Singapore’s proactive approach to AI governance and its recognition of AI’s centrality to the nation’s economic future.

Singapore’s advantages in AI readiness extend beyond regulation. The city-state boasts excellent digital infrastructure, a highly educated workforce, and significant innovation capacity. Its position as a global financial hub and regional tech center provides strong incentives for responsible AI development. The government has also demonstrated strategic commitment to AI through substantial investment in research and development, including support for the AI Verify Foundation and various AI research initiatives.

Singapore’s Regulatory Approach: Sectoral Rather Than Comprehensive

However, Singapore’s regulatory framework reflects a distinctive philosophy that differs from more prescriptive approaches. Singapore has deliberately chosen not to enact comprehensive AI legislation covering all industries. Instead, it pursues a sectoral approach, with individual ministries, authorities, and regulatory bodies implementing targeted regulations within their domains.

For example, the Personal Data Protection Commission (PDPC) issued the Model AI Governance Framework to provide guidance on ethical AI practices in the private sector. The Cybersecurity Agency of Singapore released Guidelines on Securing AI Systems in October 2024, establishing best practices for AI system security throughout the design, deployment, and disposal stages. The Infocomm Media Development Authority spearheads broader AI innovation initiatives while fostering a responsible AI ecosystem.

This sectoral approach reflects Singapore’s broader regulatory philosophy: light-touch regulation that enables innovation while maintaining robust safeguards in high-risk sectors. It also reflects the reality that AI’s applications are so diverse that one-size-fits-all regulation may prove counterproductive.

Notable Gaps: Foundation Models and General-Purpose AI

Despite Singapore’s leadership position, significant regulatory gaps remain. Most notably, Singapore does not have laws specifically regulating foundation models or general-purpose AI systems. These large language models, which can be adapted to countless applications across industries, currently operate in a regulatory gray zone in Singapore. As generative AI becomes increasingly central to business operations, this gap becomes more consequential.

In response, the Ministry of Law announced in 2025 that it was developing guidelines to help legal professionals become “smart buyers and users of generative AI tools.” This initiative acknowledges the challenges even sophisticated users face in deploying AI responsibly, but it remains a guidance framework rather than binding regulation.

Singapore’s Unique Position: Innovation Versus Caution

Singapore faces an inherent tension that distinguishes it from larger economies. As a small, open city-state dependent on international finance and trade, Singapore has strong incentives to be an early adopter of AI technology and to position itself as a global AI hub. Yet as Georgieva’s comments emphasize, insufficient ethical foundations create systemic risks.

Singapore must balance its role as an innovation leader with its need to maintain trust in its regulatory systems. If Singapore becomes known for inadequate AI governance, it risks damaging its reputation as a reliable, trustworthy financial and business center—an asset far more valuable than any temporary competitive advantage from loose regulation.

Implications for Singapore’s Future

Economic Opportunities and Risks

Georgieva’s emphasis on the widening gap between advanced and developing economies presents both opportunities and risks for Singapore. On the positive side, Singapore’s relatively strong AI preparedness positions it to capture AI-driven economic growth. The city-state can attract AI companies, investment, and talent seeking a trusted regulatory environment and strong infrastructure.

However, Singapore’s success depends on maintaining this trust. If the nation fails to develop comprehensive regulations for emerging AI challenges like foundation models and autonomous systems, it risks undermining its regulatory credibility. Simultaneously, excessive regulation could drive away the innovation and investment that Singapore seeks to attract.

Regional Leadership Role

Singapore has positioned itself as a responsible technology leader in Southeast Asia. This role carries responsibility. As the IMF Index shows, many regional neighbors lag significantly behind Singapore in AI preparedness. Singapore could leverage its position to help other ASEAN nations develop their own AI governance frameworks, creating a more consistent regional approach to AI regulation.

Such leadership would be consistent with Singapore’s broader economic interests. A region with more uniform AI governance standards would facilitate cross-border AI services and reduce compliance complexity for companies operating across multiple markets. It would also enhance Singapore’s standing as a responsible global actor committed to using its technological advantages for broader benefit.

The Imperative for Comprehensive Foundation Model Regulation

Singapore’s most pressing task is developing specific regulations for foundation models and general-purpose AI systems. These technologies represent the frontier of AI development, and their applications will shape how AI impacts society for years to come.

Singapore could take several approaches. It might follow elements of the EU’s AI Act while adapting them to Singapore’s specific context and regulatory philosophy. It could develop sector-specific requirements for foundation models in high-risk sectors like finance, healthcare, and law enforcement. Or it could establish baseline transparency and risk management requirements that apply across all industries, allowing sector-specific regulators to impose additional requirements where appropriate.

Critically, Singapore should avoid waiting for global consensus or international standards. The rapid pace of AI development means that delaying regulation creates growing risks. Moreover, Singapore’s early move into foundation model regulation could position it as a thought leader in AI governance, influencing international norms and potentially attracting companies seeking regulatory clarity.

Skills Development and Digital Inclusion

While Singapore’s digital infrastructure and workforce skills are relatively strong compared to global peers, Georgieva’s broader point about the importance of labor and skills development remains relevant. Singapore must ensure that AI capabilities are not concentrated in a small elite group of companies and highly specialized workers.

Singapore’s education system should expand AI literacy across all levels of education. Technical programs should produce more professionals capable of developing and maintaining AI systems. Perhaps most importantly, workforce development programs should help workers displaced by AI automation transition to new roles, and social safety nets should protect vulnerable populations from bearing the costs of technological disruption.

Ethical Framework Development

Singapore has taken some steps toward establishing ethical frameworks for AI through the Model AI Governance Framework and various stakeholder initiatives. However, these remain voluntary guidance rather than binding principles. Singapore should consider formalizing ethical principles for AI development and deployment, potentially through legislation or binding industry standards.

Key principles might include transparency (disclosing when AI systems are being used in consequential decisions), fairness (designing AI systems to minimize discrimination and bias), accountability (establishing clear responsibility for AI systems’ harms), and human oversight (ensuring human judgment remains available for high-stakes decisions).

Georgieva’s Broader Message and Global Context

The Financial Stability Concern

Georgieva’s warnings about AI governance must be understood in the context of her broader concerns about financial stability. Days before her October 2025 comments on AI regulation, she warned that financial market valuations driven by AI enthusiasm were approaching levels last seen during the 1990s technology bubble. An abrupt shift in market sentiment could trigger sharp economic contraction, with developing countries suffering disproportionately.

This context adds urgency to her call for robust AI governance. Inadequate regulation of AI systems used in finance could amplify market instability. For example, if multiple financial institutions deploy similar AI trading algorithms that respond identically to market conditions, they could create sudden synchronized buying or selling that destabilizes markets. Regulatory frameworks that require transparency and risk management in AI-driven financial systems could help prevent such scenarios.

The Call to Civil Society

Georgieva’s emphasis on civil society’s role—calling on civil society groups to “ring the alarm bells”—reflects a recognition that government action alone is insufficient. Civil society organizations can mobilize public awareness, advocate for strong governance, hold both companies and regulators accountable, and provide expertise that governments lack.

In Singapore’s context, civil society could play an important role in raising awareness about AI governance challenges, advocating for comprehensive regulation of foundation models, and helping ensure that AI development reflects broader societal values rather than just corporate interests.

International Coordination and Harmonization

While Georgieva emphasizes the need for each country to develop regulatory foundations, she also implicitly acknowledges that AI governance challenges are inherently global. The IMF itself has become a forum for countries to share policy responses, foster international consensus, and harmonize regulations where possible.

Singapore could play a constructive role in these international efforts. The nation’s sophisticated regulatory apparatus and AI expertise position it to contribute meaningfully to discussions about global AI governance standards. Singapore could also advocate for approaches that balance innovation with responsibility, drawing on its own experience as a city-state dependent on both technological leadership and regulatory trust.

Conclusion: A Critical Juncture for Singapore and the World

Kristalina Georgieva’s October 2025 comments represent a crucial moment in the global AI governance debate. By identifying regulation and ethics as the dimension where “the world is falling shortest,” she has provided clarity about the most pressing challenge in AI governance. The development of robust regulatory and ethical frameworks is not a luxury or an optional component of AI policy—it is essential infrastructure for responsible AI development.

For Singapore specifically, Georgieva’s analysis both validates the nation’s leadership position in AI preparedness and highlights the work still to be done. Singapore’s relatively strong score on regulation and ethics reflects genuine accomplishments in developing governance frameworks and fostering responsible AI practices. However, the significant regulatory gap surrounding foundation models and general-purpose AI represents an urgent priority.

Singapore stands at a crossroads. The nation can continue its incremental, sectoral approach to AI regulation, watching as technological developments outpace regulatory frameworks. Or it can seize the moment to develop comprehensive, forward-looking regulations that position Singapore as a global leader in responsible AI governance while maintaining the regulatory clarity and trustworthiness that attract international investment and talent.

The choice matters not only for Singapore’s economic future but also for the broader global effort to ensure that artificial intelligence benefits humanity rather than concentrating power and resources among a narrow elite. By developing robust regulatory and ethical foundations, Singapore can demonstrate that economic success and responsible governance need not be mutually exclusive—a lesson the entire world needs to learn.

The alarm bells that Georgieva urges civil society to ring should be heard clearly in Singapore. The window for proactive regulation remains open, but it will not remain so indefinitely. The time for comprehensive AI governance is now.

The Last Window

Risks and Effects of AI Market Manipulation

Systemic Risks to Market Integrity

1. Eroding Market Efficiency

Price Discovery Distortion: AI manipulation disrupts the fundamental process of efficient price discovery, preventing markets from accurately reflecting actual asset values
Liquidity Illusions: Manipulative AI systems can create false impressions of market depth and liquidity, leading to mispriced assets and execution risks
Capital Misallocation: When prices are artificially inflated or deflated, capital flows to unproductive sectors, reducing overall economic efficiency

2. Amplification of Market Volatility

Flash Crash Acceleration: AI systems can trigger and exacerbate flash crashes through cascading sell orders and feedback loops
Contagion Effects: Manipulation in one market can rapidly spread to correlated markets as AI systems detect and react to artificial movements
Volatility Clustering: Periods of manipulation-induced volatility tend to cluster, creating extended periods of market instability

3. Undermining Market Trust

Participation Deterrence: Retail and institutional investors may withdraw from markets perceived as unfair or manipulated
Premium for Opacity: Companies and assets with less transparent information become riskier investments, raising capital costs
Regulatory Trust Gap: Public confidence in regulatory bodies diminishes if they appear unable to address sophisticated manipulation

Technical Risks and Vulnerabilities

1. AI-Specific Manipulation Vectors

Data Poisoning: Manipulating the data sources that AI trading systems rely on
Model Exploitation: Reverse-engineering predictable behaviours in widely-used AI trading models
Adversarial Attacks: Crafting market signals specifically designed to trigger specific AI responses

2. Detection Challenges

Attribution Problems: Difficulty in attributing manipulation to specific actors when AI systems act autonomously
False Positive Risks: Legitimate trading patterns may be incorrectly flagged as manipulation
Cross-Platform Complexity: Manipulation schemes operating across multiple venues and asset classes evade single-platform monitoring

3. Speed and Scale Factors

Microsecond Manipulation: Manipulation occurring at speeds beyond human monitoring capability
Pattern Sophistication: AI systems are developing increasingly subtle manipulation techniques that avoid triggering alerts
Scalability: Ability to simultaneously manipulate multiple securities or markets with minimal additional resources

Economic Effects

1. Direct Market Impacts

Wealth Transfer Effects: Systematic transfer of wealth from less-sophisticated to more-sophisticated market participants
Transaction Cost Increases: Higher bid-ask spreads as market makers protect themselves against manipulation
Arbitrage Breakdown: Traditional pricing relationships between related assets become unreliable

2. Corporate Consequences

Financing Disruptions: Companies face unpredictable costs of capital due to artificially volatile stock prices
Executive Decision Distortion: Management teams making decisions based on manipulated stock price signals
Innovation Penalties: Firms with complex business models are becoming more vulnerable to narrative manipulation

3. Broader Economic Consequences

Risk Premium Elevation: Overall market risk premiums increase, raising costs across the economy
Investment Horizon Shortening: Focus shifts to shorter time frames, where manipulation effects can be better predicted
Resource Diversion: Productive capital diverted to defensive trading technology rather than value creation

Social and Distributional Effects

1. Widening Knowledge Gap

Asymmetric Understanding: Growing divide between those who understand AI market dynamics and those who don’t
Technical Elite Advantage: Disproportionate benefits flowing to those with access to sophisticated AI systems
Retail Investor Vulnerability: Smaller investors are particularly susceptible to narrative-based manipulation strategies

2. Retirement and Savings Impacts

Pension Fund Vulnerability: Long-term investors like pension funds are becoming unwitting counterparties to manipulation
Retirement Timing Risk: Manipulation spikes near retirement dates can permanently impact retiree outcomes
Savings Confidence Erosion: Reduced public confidence in market-based retirement savings vehicles

3. Global Market Disparities

Regulatory Arbitrage: Manipulation migrating to markets with weaker AI oversight
Market Development Barriers: Emerging markets are struggling to develop robust markets in the face of sophisticated manipulation
Cross-Border Contagion: Manipulation effects spreading across global markets regardless of individual market protections

Regulatory and Governance Challenges

1. Enforcement Limitations

Intent Ambiguity: Difficulty proving manipulative intent when outcomes emerge from complex AI systems
Jurisdictional Constraints: Cross-border manipulation schemes exploiting regulatory gaps
Resource Asymmetry: Regulators consistently outpaced by technological developments in private markets

2. Market Structure Vulnerabilities

Exchange Fragmentation: Multiple trading venues creating arbitrage opportunities for manipulative strategies
Dark Pool Exploitation: Less transparent trading venues provide cover for manipulation
Order Type Complexity: Sophisticated order types are being leveraged for manipulative purposes

3. Accountability Gaps

Responsibility Diffusion: Unclear liability when manipulation emerges from autonomous systems
Explainability Challenges: Difficulty explaining exactly how manipulation occurred in complex AI systems
Proportional Response: Determining appropriate penalties when harm is widely distributed

Real-World Consequences

1. Case Studies of AI Manipulation Impacts

Social Media-Driven Surges: Coordinated amplification of specific stocks causing extreme price movements
Crypto Market Manipulation: Less-regulated markets are experiencing sophisticated pump-and-dump schemes
Index Exploitation: Strategies targeting index rebalancing events to extract predictable profits

2. Emerging Vulnerable Sectors

ESG Investments: Susceptibility to narrative manipulation around environmental and social metrics
Biotech and Complex Technology: Industries where retail investors lack the technical knowledge to evaluate claims
Small and Mid-Cap Stocks: Lower liquidity makes them easier targets for coordinated manipulation

3. Nascent Defence Mechanisms

AI Manipulation Detection Tools: Emerging technologies designed to identify artificial price movements
Circuit Breakers and Speed Bumps: Trading pause mechanisms that may limit manipulation effectiveness
Transparency Initiatives: Efforts to increase visibility into order flow and market structure

The Future Landscape

1. Evolutionary Trajectories

Adaptive Manipulation: AI systems that continuously evolve to evade detection mechanisms
Legitimate Strategy Blurring: Increasingly difficult distinction between legitimate trading strategies and manipulation
Defensive AI Arms Race: Competing systems designed to detect, prevent, and execute manipulation

2. Policy Responses on the Horizon

Explainability Requirements: Potential mandates for AI trading systems to provide interpretable decision logic
Preventative Design Standards: Technical standards focusing on manipulation-resistant AI architectures
Systemic Risk Management: Central bank involvement in addressing market-wide manipulation threats

3. Long-term Market Adaptation

Market Microstructure Evolution: Trading venues redesigning rules and structures to resist manipulation
Investor Behavioural Changes: Adaptation of investment strategies to account for manipulation risks
New Market Equilibria: Potentially more resilient but less efficient market structures are emerging over time

Conclusion

AI-driven market manipulation presents unprecedented challenges to financial systems globally. Unlike traditional manipulation schemes, AI-powered approaches operate at machine speed, learn from experience, and potentially develop strategies beyond human conception. The effects ripple through not just financial markets but economic systems, retirement savings, and public trust in institutions.

The most concerning aspect may be the difficulty in detecting manipulation when it emerges organically from AI decision-making rather than explicit human design. This suggests that preventative approaches—focusing on system architecture, transparency requirements, and market structure—may ultimately prove more effective than traditional enforcement-based approaches.

For market participants, understanding these risks requires a fundamental shift in perspective—recognising that markets now operate in an environment where manipulation may be algorithmic, emergent, and difficult to distinguish from legitimate trading. For regulators, the challenge involves not just keeping pace with technological developments but anticipating how market structures themselves may need to evolve in an era of increasingly autonomous trading systems.

AI Trading Bots and Market Manipulation: Impact on Singapore’s Financial Sector

The Evolution of AI Trading Bots

AI trading bots have evolved significantly beyond basic algorithmic trading systems:

From Rule-Based to Autonomous Systems
- Traditional algorithms followed precise, human-programmed rules
- Modern AI bots employ machine learning to develop strategies independently
- Advanced systems can process vast datasets in real time, including market movements, news, social media, and alternative data.
Key Technological Advances
- Natural language processing (NLP) capabilities allow bots to analyse sentiment in news and social posts.
- Reinforcement learning enables bots to optimise strategies through trial and error.
- Deep learning models can identify complex patterns invisible to human traders.s
- Multi-agent systems potentially allow for coordinated trading strategies

Emerging Manipulation Techniques

Modern market manipulation through AI is becoming increasingly sophisticated:

Information Amplification
- AI systems can identify and amplify specific news across platforms
- Coordinated bots can create an illusion of widespread interest in specific stocks
- “Echo chamber” effects magnify selected narratives without creating explicitly false information
Autonomous Collusion
- AI systems can develop implicit cooperative strategies without explicit programming.
- Unlike traditional collusion, there may be no traceable communication or explicit agreement.
- Systems may develop behaviours organically through reinforcement le.
High-Speed Manipulation Tactics
- “Spoofing” – placing and cancelling orders to create false impressions of market activity
- “Layering” – placing multiple orders at different price levels to manipulate the order book
- “Quote stuffing” – overwhelming exchanges with rapid orders and cancellations
- Flash crashes triggered by cascading algorithmic sell-offs

Singapore’s Vulnerability and Preparedness

Singapore’s position as a global financial hub makes it both vulnerable and potentially well-positioned:

Vulnerability Factors
- High digitisation in Singapore’s financial sector
- Significant retail investor participation in markets
- Proximity to less-regulated crypto markets in the region
- Interconnectedness with global financial systems
Regulatory Framework

Monetary Authority of Singapore (MAS) has proactively addressed fintech regulation
Securities and Futures Act (SFA) prohibits market manipulation, but may need expansion for AI-specific scenarios
Singapore’s Technology Risk Management Guidelines require financial institutions to ensure sound governance of AI systems
MAS’s Fairness, Ethics, Accountability and Transparency (FEAT) principles provide guidelines for responsible AI use

Impact on Singapore Banks and Financial Institutions

The rise of AI trading bots and new manipulation techniques creates multi-faceted challenges:

Technological Arms Race
- Singapore banks face pressure to deploy sophisticated AI monitoring systems..
- Substantial investments are required in infrastructure, talent, and research
- Competition with global financial institutions and tech-focused entrants
Risk Management Challenges
- Need to detect manipulation attempts targeting their systems or clients
- Enhanced due diligence is required for automated trading platforms
- Potential legal liability if their AI systems engage in manipulative behaviours
- Reputational risks if clients suffer losses due to manipulation
Client Protection Issues
- Retail investors are potentially vulnerable to sophisticated manipulation
- Need for education and safeguards for clients using robo-advisors
- Wealth management businesses must adapt to protect high-net-worth clients
Competitive Landscape Shifts
- Traditional banks are competing with fintech firms offering AI-powered trading.
- Pressure to provide more sophisticated trading tools to retain clients
- Need to balance innovation with compliance and risk management

Strategic Responses for Singapore’s Financial Sector

To adapt to these challenges, Singapore’s financial institutions can pursue several strategies:

Enhanced Surveillance Systems

Implementing AI-powered market surveillance to detect manipulation
Real-time monitoring of trading patterns and social media sentiment
Cross-platform analysis to identify coordinated manipulation attempts
Public-Private Collaboration
- Working with MAS on regulatory sandboxes for AI trading oversight
- Information sharing about emerging manipulation techniques
- Developing industry standards for responsible AI trading
Client Education and Protection
- Educating retail investors about market manipulation tactics
- Implementing circuit breakers in retail trading platforms
- Providing transparent information about AI-driven investment products
Talent Development
- Building specialised teams combining finance, AI, and regulatory expertise
- Partnering with local universities on relevant research
- Attracting global experts in financial AI and market integrity

The Path Forward

Singapore has an opportunity to establish itself as a leader in fair and transparent AI-powered financial markets by:

Setting regulatory standards that balance innovation with market integrity
Developing technological solutions to detect and prevent manipulation
Fostering a financial ecosystem that values transparency and responsible AI use
Leading regional cooperation on cross-border manipulation issues

The transition to AI-powered markets presents both substantial risks and opportunities. Singapore’s financial institutions that proactively address these challenges will be better positioned to thrive in this evolving landscape. At the same time, those that fail to adapt may face increasing regulatory scrutiny and competitive disadvantages.

Part One: The Warning

The rain fell on Marina Bay like whispered urgency, each droplet tapping against the glass of the conference room on the thirty-fifth floor. Dr. Priya Sharma stood at the window, watching the city lights blur beneath the downpour. Below her, Singapore glittered—a constellation of financial towers, tech hubs, and innovation centers that had earned the nation its reputation as a global leader in artificial intelligence.

She held her phone loosely, the screen still glowing with Kristalina Georgieva’s words, transmitted just hours ago from Washington: “The regulatory ethical foundation for AI for our future is still to come into place.”

The warning had arrived like a match thrown into dry kindling. Priya had spent the last twelve years at Singapore’s Ministry of Digital Development, watching the nation navigate the AI revolution with characteristic pragmatism and precision. Sectoral approaches, they called it. Light-touch regulation. Room for innovation. It had worked brilliantly—until recently, when Priya began to see the cracks.

She turned from the window to face her team gathered around the conference table. There was Marcus, the economist who understood Singapore’s precarious position in global finance. Chen Wei, the lawyer who specialized in emerging technologies. And Fatimah, the ethicist and former engineer, whose concerns had grown louder with each passing month.

“Georgieva just rang the alarm bell,” Priya said quietly. “The question is: will we hear it, or will we pretend the rain is just rain?”

Part Two: The Fault Lines

The trouble had started three months earlier, though none of them realized it at the time.

A major Singapore-based fintech company, DataFlow Analytics, had deployed an advanced AI foundation model to manage algorithmic trading and risk assessment for regional hedge funds. The system was sophisticated—trained on petabytes of historical market data, capable of processing thousands of variables in milliseconds, and responsive to the slightest shifts in sentiment across global markets.

It was also entirely unregulated.

There was no law requiring disclosure of its algorithms. No mandatory testing for bias or systemic risk. No requirement to maintain human oversight. No regulatory body had even looked at it before it went live.

When Fatimah first raised concerns about the system at an informal industry forum, she was told the Model AI Governance Framework provided sufficient guidance. It was voluntary. Companies that wanted to be responsible could follow it. Innovation should not be strangled.

But then something unexpected happened.

On a Tuesday morning in September, the system had a glitch—a minor calculation error in one of its neural network layers, the kind of thing that shouldn’t matter because of the system’s redundancy and error-checking protocols. Except that error occurred at precisely the moment when three other financial institutions’ AI systems, all trained on similar data, all responding to similar signals, decided to simultaneously reduce their exposure to emerging market currencies.

For ninety-three seconds, the Singapore dollar dropped 2.7 percent. It recovered just as quickly when the glitches were resolved and human traders intervened. To most investors, it was a blip. To Priya and Fatimah, it was something else entirely: a preview of what could happen if multiple opaque AI systems were all making coordinated decisions in the financial system, unobserved and unconstrained.

That was when Priya had begun to feel the weight of Singapore’s regulatory gap. That was when she started reading Georgieva’s speeches.

Now, gathered around the conference table on this rainy evening, they faced the reality that had been hiding in plain sight: Singapore’s reputation as a trustworthy financial center depended on regulatory competence and transparency. If the world discovered that billion-dollar AI systems were operating in Singapore’s financial system without meaningful oversight, trust would evaporate like morning fog.

“The fintech incident rattled people,” Marcus said, pulling up data on his laptop. “I’ve been monitoring investor sentiment. There are quiet conversations happening in London and New York. Questions about whether Singapore really has adequate AI governance.”

“We were lucky that time,” Chen Wei added. “But Marcus is right. If something like that happens again and causes real damage, we have no regulatory framework to point to. We have no evidence of oversight. We have nothing.”

Priya looked at Fatimah. “The ethical foundation?”

“Non-existent,” Fatimah said flatly. “We have guidelines that companies can choose to follow. We have a framework that sounds impressive until you realize it’s entirely voluntary and lacks enforcement mechanisms. We have sector-specific regulators doing their best, but none of them have been given the mandate or resources to manage foundation models. And we have this growing ecosystem of advanced AI systems that absolutely nobody is systematically monitoring or understanding.”

She leaned forward. “Priya, I’ve been in this field for fifteen years. I’ve watched AI go from academic curiosity to global infrastructure. And I’m telling you—we’re at the point where incremental, sectoral approaches don’t work anymore. These foundation models don’t fit neatly into sectors. They cut across everything. Finance, healthcare, government, media. They’re general-purpose tools that reshape every industry they touch.”

Priya nodded slowly. She had known this for weeks, maybe months. But hearing it said aloud by someone she trusted crystallized something she had been resisting: Singapore could no longer maintain its current approach. The window for incremental adjustment was closing.

“Georgieva said the alarm bells need to be rung,” Priya said. “Not by her. By us. By civil society, by industry, by everyone who understands what’s at stake.”

Part Three: The Crossroads

The next morning, Priya sent a message to her counterparts at Singapore’s Personal Data Protection Commission, the Cybersecurity Agency, the Infocomm Media Development Authority, and the Monetary Authority of Singapore. “Meeting. Urgent. This afternoon.”

By 2 PM, the small conference room at the Ministry of Digital Development held the most senior AI regulators in the city-state. Priya had prepared a presentation, but she didn’t need slides to make her point. She simply started with the fintech incident and built from there.

“We got lucky in September,” she began. “And we will not be lucky forever. Foundation models are now central to Singapore’s economy. They power our financial systems, our healthcare platforms, our government services. They’re about to transform everything. And we have no comprehensive framework for managing them.”

She watched the faces around the table. Some nodded in recognition. Others shifted uncomfortably. One of the Monetary Authority representatives looked like he wanted to interrupt but held back.

“We have a choice,” Priya continued. “We can maintain our sectoral approach and hope that each regulator stays on top of their domain. Or we can acknowledge that sectoral regulation doesn’t work for technologies that cut across all sectors, and we can develop a comprehensive framework.”

“The business community will resist comprehensive regulation,” warned David, from the Infocomm Media Development Authority. “They’ll say we’re stifling innovation. They’ll threaten to move AI research and deployment elsewhere.”

“Some will threaten,” Priya acknowledged. “But I don’t think most will leave. Singapore isn’t attractive just because of light regulation. We’re attractive because we’re trustworthy, because our infrastructure is excellent, because our workforce is skilled, and because we’re a stable, rules-based society. Companies that are doing sophisticated AI work want to operate in places where they don’t have to worry about regulatory chaos or reputational damage.”

She pulled up a chart comparing Singapore’s AI scores with major global competitors. “Look at this. Europe went aggressive with the AI Act. The US is moving toward sectoral regulation similar to ours. China is taking a more centralized approach. These aren’t radically different models—they’re all trying to find the balance between innovation and oversight. But the key difference is that Europe and the US are at least trying to be comprehensive. We’re the only major AI hub that hasn’t.”

Fatimah spoke up. “I’ve talked to foundation model developers. The sophisticated ones—the ones building serious technology for serious applications—they actually want regulation. Not because they’re altruistic, but because regulatory clarity is worth more than regulatory freedom. If you don’t know what rules you have to follow, you have to assume the worst case, and that’s expensive.”

Over the next two hours, the conversation shifted from resistance to possibility. They discussed what comprehensive AI regulation might look like for Singapore. Not the European model, which was seen as too prescriptive. Not the complete deregulation that some tech companies advocated. But something distinctly Singaporean—forward-looking, proportionate to risk, enabling innovation within clear ethical boundaries.

By the time they left the meeting, an agreement had formed. They would recommend to the Ministry and, ultimately, to Parliament that Singapore develop a comprehensive AI governance framework focused specifically on foundation models and general-purpose AI systems.

It would be difficult. It would require coordination across multiple agencies. It would face pressure from companies that benefited from the current light-touch approach. But it was necessary.

That evening, Priya called her mother, who lived with her in a modest apartment in the eastern part of the island.

“Big changes coming at work,” Priya said over dinner.

Her mother, a retired educator, looked at her daughter carefully. “The kind of changes that matter?”

“I think so. We’re trying to do something that might actually influence how AI develops in this country. How it gets used. Whether it benefits everyone or just enriches a few companies.”

Her mother nodded thoughtfully. “That’s good work, Priya. That’s the kind of work that matters.”

Part Four: The Resistance

The backlash came swiftly.

Within a week of word leaking about the proposed comprehensive AI framework, industry groups were mobilizing. Tech companies published op-eds warning that Singapore would fall behind if it imposed excessive regulation. Venture capitalists released statements questioning whether Singapore was still “innovation-friendly.” One prominent AI researcher announced he was moving his lab to the UAE.

But something else was happening too.

Civil society organizations began to mobilize. Ethicists, labor advocates, consumer protection groups, and community organizations started speaking out about the need for AI governance. A coalition called “AI for Good Singapore” formed, bringing together diverse voices arguing that regulation and innovation were not opposites—that responsible governance could actually build trust and create a sustainable ecosystem for AI development.

Priya found herself at the center of this emerging movement. She gave interviews to journalists. She spoke at community forums. She collaborated with Fatimah on a white paper about why comprehensive AI governance was essential to Singapore’s long-term interests.

The turning point came in November, during a panel discussion at the Singapore International Tech Summit. Priya was scheduled to speak about the current approach to AI regulation, but she chose to be direct about the limitations.

“We have done good work with sectoral regulation,” she said to the assembled tech leaders, investors, and policymakers. “We have guidelines and frameworks that have helped companies think about responsible AI. But we have reached the limit of what sectoral approaches can accomplish. Foundation models are not sector-specific. They are tools that cut across every sector. We need comprehensive governance to match the comprehensive impact of this technology.”

She paused, letting the room settle.

“Some will say this will drive innovation away from Singapore. I don’t believe that’s true. What drives companies away is uncertainty, reputational risk, and regulatory chaos. What attracts sophisticated companies is regulatory clarity, trustworthiness, and the confidence that they can operate responsibly. Singapore can offer all of that.”

She looked directly at the tech leaders in the audience. “And frankly, the companies that would leave because we require them to be transparent and ethical about their AI systems—those are not the companies we want here anyway.”

The room was quiet for a moment, then applause began—not thunderous, but thoughtful, measured. When Priya stepped down from the stage, three journalists were waiting to interview her. Within two hours, video clips of her remarks were trending on social media.

The narrative had shifted. Suddenly, comprehensive AI governance in Singapore was not about stifling innovation—it was about securing Singapore’s future.

Part Five: The Decision

In early December, the Ministry formally presented a proposal to Parliament. It was bold but carefully crafted. The framework would establish a Foundation Model Oversight Board, a new body specifically tasked with monitoring large-scale AI systems that cut across sectors. Companies deploying foundation models would need to register them and provide information about their training data, testing procedures, and risk mitigation measures. High-risk applications—in finance, healthcare, criminal justice, and government services—would require additional scrutiny and human oversight requirements.

The framework balanced innovation with responsibility. It didn’t prohibit AI development. It required transparency and accountability.

The parliamentary debate was fierce but substantive. Opposition members raised concerns about regulatory burden. Tech industry representatives testified about competitiveness. But civil society voices were there too—Fatimah spoke about ethical imperatives, workers’ representatives discussed the need to protect employment, and a representative from the Consumers Association outlined the risks of unaccountable AI systems.

When the vote came, it was decisive. Parliament approved the comprehensive AI governance framework with 79 votes in favor and 22 against.

Singapore had chosen its path. The window that had been closing—the moment when incremental approaches could still work—had narrowed to a closing of the door. But Singapore had stepped through before that door shut completely.

Epilogue: Six Months Later

The Foundation Model Oversight Board held its first public hearing on a warm June morning. Priya sat on the panel, alongside Fatimah and two other experts. They were reviewing the registration of Singapore’s first foundation models.

DataFlow Analytics was there, presenting its algorithmic trading system. The company’s executives were nervous—they weren’t sure how this would go. But Priya had prepared them thoroughly. When the hearing concluded, the Board had approved the system’s registration with specific requirements: monthly risk reporting, stress testing protocols, and mandatory human oversight for trades above a certain threshold.

It wasn’t prohibition. It wasn’t even particularly onerous. But it was meaningful oversight.

That evening, Priya received an email from Kristalina Georgieva. The IMF was updating its AI Preparedness Index. Singapore’s scores had been recalculated based on the new comprehensive framework. On regulation and ethics, Singapore had jumped from 0.22 to 0.71—climbing into the global elite on AI governance while maintaining robust support for innovation.

But more importantly, the email contained a link to a new IMF report. It was titled “Comprehensive AI Governance Models: Singapore’s Leadership in Balancing Innovation and Responsibility.”

Georgieva had written, “Singapore’s choice to develop comprehensive governance for foundation models demonstrates that economic success and regulatory responsibility are not mutually exclusive. Other nations should learn from this example.”

Priya read the words twice, then she stepped out onto her apartment balcony. Below her, Singapore glowed in the evening light—the same constellation of towers and lights, but now representing something different. Not just a city built on pragmatism and efficiency, but one that had chosen to use those qualities in service of wisdom.

The alarm bells had been heard. The window had been used. The choice had been made.

And the work of building a responsible AI future had only just begun.

The End

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Global AI Governance Crisis: IMF Chief Georgieva’s Warning