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Banks use AI as an analytical input: it can help process market information, calibrate trading algorithms, generate investment insights and support risk management. It does not guarantee better returns or accurate risk estimates, and the sources do not establish that a particular bank uses AI to make final investment decisions. The potential benefits depend on the data, model, intended use and controls around it.
Where AI fits into market-risk and investment work
“AI” is not one system that independently decides what a bank should buy or sell. It can refer to different analytical techniques used at different points in a decision process. The Bank of England reports that algorithmic traders already widely use established techniques such as decision trees to calibrate trading algorithms, while some investment managers are using AI to generate insights. The extent and speed of broader deployment remain uncertain, according to its April 2025 review of AI in the financial system.
A useful way to understand the role is as a workflow, not a claim that every bank follows the same process:
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- Information enters the process. A model may process market and other data relevant to a firm’s analysis. Whether the inputs are suitable and reliable matters: flaws in data can contribute to exposures being measured or interpreted incorrectly.
- A model produces an output. Depending on its purpose, it may identify patterns, generate an investment insight, or help calibrate an algorithm. That output is an input to further decisions, not proof that the model understands every market condition.
- People and controls determine how it is used. The output may inform risk measurement, trading calibration or investment judgment. Governance, validation and permitted-use controls should fit the model and the decision it supports.
The available sources describe these uses at a broad level. They do not provide a bank-by-bank inventory or show that a model makes the final investment decision.
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What AI may help banks do—and what it does not prove
Process information and support analysis
AI may help market participants use available data and incorporate new information more quickly. The Bank of England says broader use to inform trading and investment decisions could be associated with greater market efficiency, but describes this as a possibility, not a guaranteed outcome. Faster processing is not, by itself, evidence of better investment performance or more accurate risk measurement.
Support risk management
Models can contribute to how a firm measures or interprets its exposures. That can be useful only if the model’s output is appropriate for the decision. An unknown model flaw or unsuitable data can instead leave a firm with a distorted picture of its exposure and less resilience to market stress.
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The cited sources do not establish a quantified causal improvement in market-risk accuracy or investment returns. They also do not rank specific models or providers. Treat claims of a particular accuracy gain, return uplift or universally superior AI approach as unsubstantiated unless they come with relevant evidence and clearly defined conditions.
Where AI can fail: firm-level and market-level risks
Incorrect exposure estimates
Errors in data, model design or interpretation can cause exposures to be mismeasured. Complexity can make it harder for an institution to understand how a model behaves, potentially encouraging risk-taking that decision makers do not properly understand. A model output should therefore be assessed in the context of its assumptions and intended use, rather than treated as an objective account of risk.
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Unprecedented market stress
AI may perform poorly when a shock is historically unprecedented or the market is subject to radical uncertainty. A backtest—an assessment using historical data—can show how a model would have behaved in the conditions represented in that data; it cannot establish how the model will behave in events outside those conditions. Stress assessment and other safeguards need to account for that limit.
Similar strategies across firms
If market participants use similar models, datasets or vendors, their strategies could converge and lead to correlated positions. The Bank of England identifies this as a potential market-level concern, while noting that the practical systemic effects remain uncertain. It is a reason to consider concentration and common dependencies, not evidence that AI has already caused a specific market disruption.
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How to assess an AI-supported approach
There is no supported basis here for ranking vendors or naming a best-performing model. When evaluating an approach, use criteria tied to its purpose and risks:
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- Data quality and coverage: Are the inputs suitable for that purpose, and could gaps or flaws distort the exposure picture?
- Validation and explainability: Can reviewers independently assess performance, limitations and assumptions? Can decision makers understand the output sufficiently for the way they use it?
- Behavior under stress: How is the approach assessed in extreme conditions, especially when historical evidence is limited?
- Concentration and correlation: Does the approach depend on a shared vendor, model or dataset that could contribute to similar positioning elsewhere?
- Governance and accountability: Who owns the model, what uses are permitted, how is it monitored, and what mitigants and third-party controls apply?
These criteria reflect supervisory concerns; they are not a scorecard showing that one model or provider is better than another.
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What current supervisory guidance says
UK PRA guidance: SS1/23
The current version of the Prudential Regulation Authority’s Supervisory Statement 1/23 was published and took effect on 23 April 2026. Its scope is specific: it applies to UK-incorporated banks, building societies and PRA-designated investment firms with specified internal model approvals for credit, market or counterparty-credit capital requirements. It should not be read as a universal rule covering every bank, firm or AI use.
SS1/23 sets out principles for model identification and classification, governance, development, implementation and use, independent validation, and mitigants. It also says applicable AI and machine-learning model risks should be identified and managed within broader model risk management. See the PRA’s SS1/23 statement for the full scope and principles.
FSB consultation: proposed practices, not an international standard
On 10 June 2026, the Financial Stability Board published a consultation report proposing 12 sound practices. They cover organisation-wide governance, risks across the AI development and deployment lifecycle, and cyber, information and communications technology, and third-party risks. The count refers to proposed practices, not evidence that they improve performance or reduce risk.
The FSB explicitly says the practices are not intended to establish an international standard or prescribe adoption of a particular technology. They are consultation proposals, not a rule that every bank has adopted. Read the FSB consultation report for its stated scope and status.
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