Global Financial Regulators Sound AI Alarm: New Oversight Framework Tackles 'Herd Mentality' Risk

Financial Stability Board Warns of Critical AI Oversight Gaps

Global financial regulators are sounding an unprecedented alarm about artificial intelligence adoption in banking, with the Financial Stability Board (FSB) releasing a critical report on October 10, 2025, revealing that regulatory oversight of AI systems is "still at an early stage" despite massive industry adoption. The FSB's latest report identifies "herd mentality" behavior, where financial institutions make similar AI-driven decisions that amplify market correlations and systemic risks, as one of the most dangerous threats to global financial stability. With over 85% of financial firms actively deploying AI across critical functions from fraud detection to algorithmic trading, regulators warn that current monitoring frameworks are inadequate to address vulnerabilities including third-party dependencies, concentration risks, and the potential for cascading failures across interconnected financial systems.

❓ What Are the Key AI Risks That Have Regulators Concerned?

Financial regulators have identified a complex web of AI-related vulnerabilities that could threaten global financial stability, with systemic risks emerging from both individual institutional failures and coordinated market behaviors. The FSB's comprehensive analysis reveals that AI introduces novel risks while amplifying existing ones, creating potential contagion pathways that traditional risk management frameworks weren't designed to handle.

The primary risk categories include:

Risk Category	Specific Threats	Systemic Impact	Current Preparedness
Herd Behavior	Similar AI models making correlated decisions	Market-wide asset mispricing, liquidity crises	Limited monitoring tools
Third-Party Concentration	Dependence on few AI service providers	Single points of failure, operational disruption	Emerging frameworks only
Model Risk	AI opacity, hallucinations, bias amplification	Incorrect risk assessments, discriminatory outcomes	Traditional frameworks insufficient
Cyber and Fraud	AI-enabled attacks, data poisoning, disinformation	System-wide security breaches, market manipulation	Reactive rather than proactive

Herd Behavior and Market Correlations: Federal Reserve Vice Chair Michael Barr warned in February 2025 that "when technology becomes ubiquitous, use of GenAI could lead to herding behavior and the concentration of risk, potentially amplifying market volatility." This occurs when multiple institutions using similar AI models react to market events in synchronized ways, creating feedback loops that can destabilize entire market segments.

Third-Party Dependencies: The FSB's case study on generative AI reveals dangerous concentration risks, with financial institutions heavily reliant on a small number of providers for specialized hardware, cloud infrastructure, and pre-trained models. This "heavy reliance can create vulnerabilities if there are few alternatives available," particularly as institutions increasingly depend on external AI capabilities they cannot replicate internally.

Operational and Model Risks: AI systems introduce new forms of model risk through their opacity and potential for hallucinations—generating plausible but incorrect outputs. The Bank for International Settlements (BIS) notes that "understanding the quality and accuracy of model outputs is complicated by new inaccuracies" that traditional validation methods cannot easily detect.

❓ How Are Regulators Responding with New Oversight Frameworks?

Global financial regulators are rapidly developing comprehensive AI oversight frameworks, moving from general principles to specific supervisory requirements that address the unique challenges posed by artificial intelligence in financial services. The regulatory response represents the most significant evolution in financial supervision since the 2008 crisis, with authorities implementing risk-based approaches tailored to AI's specific characteristics.

FSB's Monitoring Framework:

The FSB's October 2025 report outlines a systematic approach to AI monitoring that includes both direct and proxy indicators for tracking adoption and vulnerabilities. Key recommendations include:

• Enhanced Data Collection: Leveraging surveys, supervisory engagement, publicly available data, and vendor information to build comprehensive AI usage profiles
• Cross-Border Cooperation: Facilitating information sharing and alignment of taxonomies across jurisdictions to prevent regulatory arbitrage
• Standardized Indicators: Developing common metrics for assessing AI adoption patterns, concentration risks, and systemic dependencies
• Real-Time Monitoring: Implementing dynamic assessment tools that can track rapidly evolving AI deployments

Sector-Specific Guidance:

Banking Supervision: The Basel Committee on Banking Supervision has integrated AI considerations into existing model risk management frameworks while developing new guidance for algorithmic decision-making in credit, market, and operational risk areas.

Insurance Oversight: The International Association of Insurance Supervisors (IAIS) published comprehensive guidance in July 2025 requiring insurers to implement governance measures proportionate to AI system risk profiles, with higher-risk applications subject to enhanced oversight.

Securities Regulation: The Securities and Exchange Board of India introduced five core principles for responsible AI use in securities markets, focusing on model governance, investor protection, robust testing, fairness, and cybersecurity.

Risk-Based Regulatory Approach:

Regulators are implementing "sliding scale" oversight where scrutiny intensity correlates with risk levels:

• High Scrutiny: AI in credit decisions, algorithmic trading, and fraud detection faces maximum oversight due to consumer impact and systemic risk potential
• Moderate Scrutiny: Risk modeling and customer personalization applications require explainability but face less intensive oversight
• Low Scrutiny: Back-office automation with minimal human impact receives proportionate regulatory attention

❓ What Is the "Herd Mentality" Risk and Why Is It Dangerous?

The "herd mentality" phenomenon represents one of the most insidious systemic risks in modern finance, where AI systems across different institutions make similar decisions that amplify market movements and create dangerous feedback loops. Unlike traditional market correlation, AI-driven herd behavior can occur at machine speed across vast numbers of institutions simultaneously, potentially triggering market-wide instability before human intervention is possible.

How Herd Behavior Manifests:

Similar Model Architecture: Many financial institutions rely on similar AI frameworks, training methodologies, and data sources, leading to convergent decision-making patterns. When these systems encounter similar market conditions, they tend to generate similar responses across multiple institutions.

Correlated Risk Assessments: AI credit models trained on similar historical data may simultaneously tighten lending standards during market stress, creating credit crunches. The Bank of England warns that "a high level of reliance on AI models for key risk management decisions could impact firms' resilience, such as liquidity preparedness."

Algorithmic Trading Synchronization: High-frequency trading algorithms using similar AI strategies can create rapid, synchronized market movements that overwhelm traditional market-making mechanisms and liquidity buffers.

Systemic Consequences:

Market Volatility Amplification: The BIS notes that "AI's rapid, real-time responses may increase volatility and herding behavior, creating destabilizing feedback loops" that can turn minor market corrections into major disruptions.

Liquidity Evaporation: When AI systems simultaneously attempt to reduce risk exposures, they can create liquidity shortages across multiple markets, as seen in flash crash scenarios but potentially at much larger scale.

Procyclical Behavior: AI systems trained on historical patterns may amplify economic cycles by tightening credit and reducing investment during downturns while becoming overly optimistic during booms.

Historical Parallels:

Regulators draw parallels to the 2008 financial crisis, where widespread use of similar risk models led to collective underestimation of housing market risks. However, AI-driven herd behavior could be more dangerous because it operates at digital speed and can involve thousands of institutions making coordinated decisions within milliseconds rather than months.

❓ How Are Major Financial Institutions Adapting Their AI Governance?

Leading financial institutions are implementing comprehensive AI governance frameworks that go beyond regulatory compliance to address operational resilience and competitive positioning in an AI-driven landscape. These frameworks reflect a fundamental shift from viewing AI as a technology tool to treating it as a core business capability requiring board-level oversight and enterprise-wide risk management.

Goldman Sachs's Approach:

Goldman Sachs has established a dedicated AI governance committee at the board level, implementing what they term a "three lines of defense" model specifically for AI systems. The bank requires all AI applications to undergo risk classification, with high-risk systems subject to enhanced validation, continuous monitoring, and quarterly reviews by independent risk teams.

HSBC's Framework:

HSBC has developed an AI ethics board that reviews all customer-facing AI applications for bias, fairness, and explainability before deployment. The bank maintains "kill switches" for AI systems and requires human oversight for any AI decision affecting customer credit or investment outcomes.

JPMorgan Chase's Integration:

JPMorgan has integrated AI governance into existing model risk management frameworks while establishing new AI-specific controls. The bank employs "shadow AI" systems that run parallel to production AI applications to detect performance degradation or unexpected behavior patterns.

Common Governance Elements:

• Board Oversight: 84% of financial organizations have implemented or are planning comprehensive AI governance frameworks with board-level accountability
• Risk Classification: Systematic categorization of AI systems based on potential impact, customer interaction, and regulatory requirements
• Continuous Monitoring: Real-time performance tracking, bias detection, and model drift identification
• Third-Party Management: Enhanced due diligence on AI service providers, including security assessments, data handling practices, and business continuity planning
• Human Oversight: Mandatory human intervention points for high-stakes decisions and exception handling

Regulatory Compliance Integration:

Institutions are aligning their governance frameworks with emerging regulatory requirements while preparing for additional oversight. Many are implementing "regulatory readiness" programs that exceed current requirements in anticipation of stricter future regulations.

❓ What Role Do Third-Party AI Providers Play in Systemic Risk?

Third-party AI service providers have emerged as critical nodes in the global financial system, creating new systemic risk pathways that regulators are scrambling to understand and manage. The FSB's analysis reveals that financial institutions' growing dependence on a small number of specialized AI providers creates "single points of failure" that could trigger widespread disruptions across the financial system.

Concentration Risk Factors:

Hardware Dependencies: The generative AI ecosystem relies heavily on specialized hardware from a handful of manufacturers, particularly advanced GPUs from Nvidia. Any supply chain disruption or capacity constraints could simultaneously affect hundreds of financial institutions.

Cloud Infrastructure: Major cloud providers (AWS, Microsoft Azure, Google Cloud) host the majority of financial AI workloads. Service outages, security breaches, or policy changes at these providers could cascade across multiple institutions.

Model Providers: Companies like OpenAI, Anthropic, and Google provide pre-trained models that financial institutions customize for their specific needs. Changes in model performance, availability, or pricing could force simultaneous adjustments across many institutions.

Systemic Vulnerability Pathways:

Operational Disruption: The FSB notes that "such relationships expose financial institutions to operational vulnerabilities" where a single provider's failure could disable AI capabilities across multiple institutions simultaneously.

Model Performance Correlation: When many institutions use similar underlying models, they may experience performance degradation simultaneously, creating synchronized risk assessment failures.

Data Contamination: A security breach or data poisoning attack against a major AI provider could compromise the integrity of AI systems across multiple financial institutions.

Regulatory Response to Third-Party Risk:

Enhanced Due Diligence: Regulators are requiring financial institutions to conduct more thorough assessments of AI service providers, including their security practices, business continuity plans, and financial stability.

Concentration Monitoring: The FSB recommends tracking "criticality, concentration, substitutability, and systemic relevance of third-party AI service providers" to identify potential single points of failure.

Business Continuity Requirements: Institutions must demonstrate they can continue operations if key AI providers become unavailable, including maintaining alternative providers or reverting to non-AI processes.

❓ Real-World Case Study: How Bank of England Is Leading AI Risk Management

The Bank of England has emerged as a global leader in developing practical approaches to AI risk management in financial services, implementing comprehensive frameworks that other central banks are adopting as models for their own jurisdictions.

The Strategic Approach:

The Bank of England's Financial Policy Committee has integrated AI risk assessment into its core financial stability mandate, treating AI not as a separate issue but as a cross-cutting risk factor that affects all traditional risk categories.

Supervisory Innovation:

AI Impact Assessment Framework: The Bank requires regulated institutions to conduct comprehensive AI impact assessments that evaluate potential effects on financial stability, consumer outcomes, and operational resilience.

Stress Testing Integration: AI scenarios are now embedded in regular stress testing exercises, examining how AI failures or correlated AI behaviors might affect bank capital and liquidity during stressed conditions.

Cross-Sector Coordination: The Bank coordinates with the Financial Conduct Authority (FCA) and Prudential Regulation Authority (PRA) to ensure consistent oversight across different types of financial institutions.

Practical Implementation:

Machine Learning Model Governance: In July 2025, the Bank published explicit expectations for banks using machine learning in internal models, requiring enhanced validation, ongoing monitoring, and clear governance structures.

Third-Party Risk Management: The Bank has established specific requirements for managing AI-related third-party risks, including mandatory contingency planning for critical AI service provider failures.

Industry Engagement: Regular industry forums allow financial institutions to share AI risk management practices and discuss emerging challenges with supervisors.

Measurable Outcomes:

• Risk Identification: UK banks have identified and catalogued over 3,000 AI use cases across their operations, enabling targeted risk assessment
• Governance Enhancement: 95% of major UK banks have established board-level AI governance committees since the Bank's guidance was issued
• Stress Testing Integration: AI scenarios are now standard components of UK bank stress tests, revealing potential vulnerabilities before they manifest
• International Influence: The Bank's frameworks are being adopted by central banks in Canada, Australia, and several EU jurisdictions

Global Impact:

The Bank of England's approach demonstrates that effective AI risk management requires proactive supervision, industry engagement, and integration with existing regulatory frameworks rather than separate AI-specific regulations. This model is influencing regulatory approaches worldwide and contributing to international coordination efforts through the FSB and Basel Committee.

🚫 Common Misconceptions About Financial AI Regulation

Misconception 1: AI Regulation Will Stifle Financial Innovation

Reality: Regulatory frameworks are designed to enable responsible innovation while managing systemic risks. The FSB emphasizes that proper oversight helps "fully harness the potential benefits of AI" by building confidence and reducing uncertainty that currently limits adoption.

Misconception 2: Existing Financial Regulations Are Sufficient for AI

Reality: While traditional risk categories apply to AI, the technology introduces novel risks requiring specific oversight. The BIS notes that AI may "heighten some existing risks" like model risk and data-related risks in ways that existing frameworks don't fully address.

Misconception 3: AI Risks Are Primarily Technical Issues

Reality: The most significant AI risks are systemic and behavioral, including herd mentality, market correlations, and third-party dependencies. These require regulatory rather than purely technical solutions.

Misconception 4: Small Financial Institutions Don't Need AI Governance

Reality: Smaller institutions often face greater AI risks due to limited resources for governance and higher reliance on third-party providers. Regulatory frameworks increasingly require proportionate but comprehensive oversight regardless of institution size.

Misconception 5: AI Regulation Is Too Premature Given Rapid Technology Evolution

Reality: The FSB argues that waiting for technological maturity is dangerous given AI's current widespread adoption in critical financial functions. Regulatory frameworks must evolve alongside technology rather than lag behind it.

❓ Frequently Asked Questions

Q: How quickly are these new AI oversight frameworks being implemented?

A: Implementation varies by jurisdiction, but major regulators are moving rapidly. The FSB's October 2025 report includes immediate recommendations, while specific regulations like the EU AI Act's financial services provisions are already in effect. Most major jurisdictions expect comprehensive frameworks operational by mid-2026.

Q: What happens to financial institutions that don't comply with AI governance requirements?

A: Regulators have broad enforcement powers including operational restrictions, capital penalties, and in severe cases, revocation of operating licenses. The Bank of England has indicated it will integrate AI governance quality into overall supervisory ratings.

Q: How do these regulations affect fintech companies and smaller financial institutions?

A: Most frameworks use proportionate approaches based on systemic importance and risk levels. However, any institution using AI for customer-facing decisions or critical operations faces governance requirements, often scaled to their size and complexity.

Q: Will AI regulation harmonize globally or remain fragmented?

A: The FSB is working toward harmonization through common taxonomies and indicators, but implementation will vary by jurisdiction. The goal is international coordination on systemic risks while allowing national flexibility on specific requirements.

📝 Key Takeaways

• Regulatory oversight lags dangerous AI adoption pace—The FSB warns that monitoring efforts are "still at an early stage" while over 85% of financial firms actively deploy AI across critical functions from credit decisions to algorithmic trading
• "Herd mentality" emerges as top systemic risk—Coordinated AI decision-making across institutions creates dangerous feedback loops that can amplify market volatility and trigger system-wide liquidity crises at digital speed
• Third-party concentration creates single points of failure—Heavy dependence on few AI service providers for hardware, cloud infrastructure, and models exposes the entire financial system to cascading operational failures
• Comprehensive oversight frameworks rapidly emerging—Regulators implement risk-based supervision with "sliding scale" intensity, ranging from maximum oversight for credit and trading applications to proportionate monitoring for back-office automation
• Leading institutions build board-level governance—84% of financial organizations implement comprehensive AI governance frameworks with enhanced due diligence, continuous monitoring, and mandatory human oversight for high-stakes decisions
• Global coordination accelerates through FSB leadership—International alignment on taxonomies, indicators, and monitoring approaches aims to prevent regulatory arbitrage while addressing systemic risks that transcend national boundaries

Conclusion

The global financial regulatory community faces an unprecedented challenge in overseeing artificial intelligence adoption that is transforming the financial system at breakneck speed while introducing entirely new categories of systemic risk. The FSB's October 2025 report represents a watershed moment in regulatory evolution, acknowledging that traditional oversight approaches are inadequate for AI's unique characteristics and systemic implications.

The "herd mentality" phenomenon and third-party concentration risks identified by regulators represent genuine threats to global financial stability, potentially more dangerous than the interconnected risks that led to the 2008 financial crisis because they operate at digital speed and involve thousands of institutions making synchronized decisions within milliseconds. The challenge is no longer whether AI will transform finance—it already has—but whether regulatory frameworks can evolve quickly enough to manage the resulting risks.

Success in this endeavor requires unprecedented coordination between regulators, financial institutions, and AI service providers to build oversight frameworks that protect financial stability while enabling continued innovation. The next 18-24 months will be critical as comprehensive AI governance frameworks move from regulatory guidance to operational reality, determining whether artificial intelligence becomes a source of enhanced financial stability or the trigger for the next systemic crisis.

The stakes are enormous: effective AI regulation could unlock artificial intelligence's full potential to improve financial services while preventing catastrophic failures, while inadequate oversight could allow emerging risks to mature into systemic vulnerabilities that threaten the global economy. The regulatory community's response to this challenge will shape the financial system for decades to come.