## CHAPTER 3

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**Canonical URL:** [CHAPTER 3](https://www.imf.org/-/media/files/publications/gfsr/2024/october/english/ch3sum.pdf)

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### Overview
- Assesses recent developments in AI and Generative AI and their implications for capital markets.
- Presents new analytical work and results from a global outreach to market participants and regulators.
- Delineates potential benefits and risks from widespread adoption of these technologies, and makes suggestions for policy responses.
- Notes that current use of AI appears to be an extension of existing trends in machine learning and other advanced analytics; more significant changes are a medium- to long-term concern.

### Potential benefits for capital markets
- Dramatically increase the efficiency of capital markets—trading, investments, asset allocation—through AI-assisted process automation and analysis of complex unstructured data.
- Evidence suggests effects are already beginning to be felt:
  - New evidence from labor markets and patent filings suggests adoption of AI in capital markets is likely to increase significantly in the near future.
  - Analyses of pricing patterns and trading dynamics already show changes in some markets consistent with adoption of these new technologies.
- AI may reduce financial stability risks by enabling:
  - Superior risk management.
  - Deepening market liquidity.
  - Improved market monitoring by both participants and regulators.

### Market structure and dynamics implications
- AI could cause large changes in market structure through greater and more powerful use of algorithmic trading and novel trading and investment strategies.
- Potential consequences include:
  - Increased turnover and asset correlations.
  - Prices reflecting new information at an ever-increasing pace.
  - Migration of market-making and investment activities to hedge funds, proprietary trading firms, and other nonbank financial intermediaries (NBFIs).

### New and amplified risks
- Increased market speed and volatility under stress, especially if trading strategies of AI models all respond to a shock in a similar manner or shut down in response to an unforeseen event.
- More opacity and monitoring challenges from:
  - Further migration of activities to NBFIs.
  - Uncertainty about how AI models used by different investors and traders could interact.
- Increased operational risks due to reliance on a few key third-party AI-service providers that dominate computational power and large language model services.
- Increased cyber and market manipulation risks, particularly in generating fraud and social media disinformation.

### Policy recommendations and regulatory actions
- Many risks are addressed by existing regulatory frameworks, but authorities should consider additional measures:
  - Undertake the calibration of circuit breakers and a review of margining practices in light of potentially rapid AI-driven price moves.
  - Enhance monitoring and data collection of the activity of large traders, including NBFIs.
  - Address dependency on data, models, and technological infrastructure by requesting a risk mapping from regulated entities (that is, data on the internal and external interconnections and interdependencies that are necessary to deliver the institutions’ critical services).
  - Adopt a coordinated approach for the definition of critical AI third-party service providers and continue to strive for resilience in capital markets by enhancing cyberattack protocols.
  - Adopt measures that ensure continued market integrity, efficiency, and resilience of over-the-counter markets when AI use proliferates.

### Outreach and evidence base
- Findings draw on a global outreach to market participants and regulators and on new analytical work.
- Observed current patterns are consistent with incremental extensions of machine learning usage; more transformative impacts remain a medium- to long-term consideration.

*CHAPTER 3, GLOBAL FINANCIAL STABILITY REPORT (ch3sum).*

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_Source: https://www.imf.org/-/media/files/publications/gfsr/2024/october/english/ch3sum.pdf_