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AI is changing finance by helping institutions process information, assess risk, monitor activity and support decisions—from evaluating credit quality to analyzing company filings and assisting trade execution. These tools can make particular workflows faster or more efficient, but they do not guarantee fair decisions, safer markets or better investment returns. Their value depends on the task, the data, the system’s design and the oversight around it.
How banks and financial firms use machine learning
Machine learning (ML) is a branch of AI in which systems use data to identify patterns or produce predictions. In financial services, that can mean estimating risk, flagging unusual activity or helping staff handle information-heavy work. The Financial Stability Board (FSB) described a broad range of such applications in its 2017 report, Artificial intelligence and machine learning in financial services. The examples below are possible uses, not evidence that every firm has adopted them or that every deployment works well.
| Workflow | What the tool may help with | What to keep in mind |
|---|---|---|
| Credit assessment | Assessing information relevant to credit quality. | A model’s output is an input to a decision process; it does not establish that the model understands an applicant’s full circumstances. |
| Insurance | Supporting pricing and marketing analysis. | Results depend on the data and criteria used, and should be considered alongside the potential for bias. |
| Customer interaction | Automating or assisting parts of customer service. | Automation does not remove the need to make clear how customers can obtain help or challenge consequential outcomes. |
| Fraud and compliance | Detecting potentially fraudulent activity, supporting regulatory compliance and monitoring transactions or communications. | Alerts need appropriate review; a flagged pattern is not, by itself, proof of wrongdoing. |
| Operations and risk | Supporting data-quality checks, capital optimization and model back-testing. | These uses depend on reliable data and suitable controls over how models are evaluated and used. |
| Trading | Supporting trade execution and analysis of market information. | Automated strategies may interact with one another and can affect market behavior, particularly under stress. |
The FSB’s 2017 report says more efficient processing can benefit credit decisions, markets, insurance and customer interactions. That is a potential workflow benefit, not a guarantee of better outcomes for every customer or institution.
How AI is used in investment research and trading
Research and information analysis
Investment professionals face large volumes of company and market information. The International Monetary Fund (IMF) wrote on July 23, 2026, that generative AI can parse earnings calls, regulatory filings and economic news in real time. ML models can also generate trading signals from market data. These capabilities can help organize or analyze information; they do not establish that an AI-generated signal is accurate or that using it will improve investment returns.
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Trade execution and market effects
AI-assisted execution may, under normal market conditions, improve liquidity, reduce transaction costs or speed price discovery, according to the IMF’s July 2026 discussion. These are possible mechanisms rather than assured results, and normal conditions do not describe every market episode.
The Federal Reserve’s November 2025 Financial Stability Report: Asset Valuations offers useful context: “The majority of AI applications in trading today seem to be building upon established practices in machine learning and sophisticated data analysis techniques, rather than representing a significant departure from existing methods.” In other words, many applications extend established analytical approaches rather than introducing an entirely new kind of trading.
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The Federal Reserve also describes concerns that AI-driven algorithmic trading could contribute to correlated trading, collusion, manipulation, concentration, rapid price swings, flash crashes or other market dislocations. More information and more complex logic could also lead to a wider variety of reactions. The direction and scale of these effects depend on how systems behave and interact; AI does not make market outcomes inherently more stable or more volatile.
What AI can improve—and what it cannot guarantee
For financial firms, the practical appeal is often speed and scale: systems can help process information, monitor activity, support execution and assist supervisory analysis. Whether those capabilities improve a decision depends on the quality and relevance of the data, how the model performs on the specific task, and the controls applied in operation. Faster analysis is not automatically more accurate analysis, and an efficiency gain for a firm is not necessarily a better outcome for a customer.
AI tools also do not remove uncertainty from lending, insurance, markets or investing. A prediction is not a certainty, and a trading signal is not a promise of profit. Neither machine learning nor generative AI should be treated as a substitute for accountability, appropriate review or sound risk management.
Risks for customers, firms and financial markets
Model and customer risks
The IMF’s August 22, 2023 note, Generative Artificial Intelligence in Finance: Risk Considerations, identifies bias, privacy, opaque outcomes, weak robustness, generative-AI hallucinations and cybersecurity threats as risks to consider. These are not inevitable features of every system, but they matter when a model influences consequential decisions or handles sensitive information. A generative system may produce plausible-sounding but incorrect material; a complex model may also make it difficult to explain why a particular result was produced.
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Dependencies and risks that can spread
Risks can extend beyond an individual model. The FSB’s 2017 report highlights new interconnections, reliance on third-party providers and the broader risks that can arise from opaque or unauditable methods. The IMF’s June 30, 2026 discussion of AI and cybersecurity adds that shared infrastructure or a common service provider can allow an incident to affect multiple institutions. It also warns that AI can intensify machine-speed attack and defense dynamics.
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What responsible AI oversight involves
Governance needs to cover the AI lifecycle, from deciding whether to adopt a system through development, deployment and ongoing use. The FSB’s June 10, 2026 Sound Practices for Responsible Adoption of Artificial Intelligence (AI): Consultation report proposes 12 sound practices for boards and senior management to consider when managing AI strategy and risk. It is consultation guidance—a proposed menu of practices, not a binding universal rule.
For a customer, investor or business partner assessing a financial firm’s use of AI, these practical questions can help reveal whether oversight is more than a label:
- What task does the system perform? Distinguish analysis or recommendations from decisions that directly affect customers or transactions.
- What data does it rely on? Ask whether the information is suitable for the task and how privacy is protected.
- How are reliability and bias checked? Look for testing and monitoring suited to the system’s real use, rather than relying only on a model’s initial performance.
- Who is accountable? A firm should be able to identify the people responsible for oversight and handling failures.
- Can an affected customer challenge a consequential outcome? Ask how a person can request an explanation or review where appropriate.
- How are cyber and vendor dependencies managed? Consider whether the firm can respond to a security incident or disruption involving shared infrastructure or an outside provider.
These questions draw on risk themes identified by the FSB and IMF; they are practical prompts, not a verbatim checklist from either organization.
How to judge claims about AI in finance
When a bank, investment firm or technology provider describes an AI capability, evaluate the use case rather than the label. A model that helps detect potentially fraudulent activity is doing a different job from a generative tool that summarizes earnings calls, and both differ from an automated trading system. The relevant evidence should match the claimed task and the conditions in which the system is used.
- Separate a tool’s ability to process information from proof that it improves decisions or investment performance.
- Check whether a claim concerns a potential benefit, a demonstrated result or a specific deployment; do not treat those as interchangeable.
- Consider who reviews outputs, how errors are detected and what happens when the system is unavailable.
- For consequential decisions, look for accountability and a way to raise concerns—not only a description of the model.
- For market-facing systems, consider how strategies, data sources and providers may interact with those used elsewhere.
Applied this way, AI is best understood as a collection of tools embedded in financial workflows, not as a single technology that independently makes finance fairer, safer or more profitable.
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