Machine learning is used across banking, asset management, trading, insurance and financial supervision. It can help institutions process information, identify patterns and tailor services, but its outputs are not automatically accurate or fair. The practical effects depend on what data a system uses, how the institution applies its output and what human or other controls oversee it.
What does machine learning mean in finance?
Machine learning (ML) is a set of methods that find patterns in data and use them to classify information, estimate outcomes or recommend actions. In finance, the term covers conventional supervised, unsupervised and reinforcement-learning systems, as well as newer generative-AI components. A generative chatbot and a model that flags unusual transactions are different applications; the risks and appropriate controls depend on the particular task.
ML is part of the broader category of artificial intelligence (AI). Not every AI application is generative AI, and the use of AI does not necessarily mean a system makes a final decision on its own. Financial institutions may use a model to prioritize cases or produce an estimate that is considered alongside other information.
Where is machine learning used?
The OECD’s 2021 review describes uses across financial activities. The examples below show the task and the potential value to an institution; they do not establish that every firm uses each application or that a model improves outcomes in every case.
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| Area | Examples of use | What the system may help with |
|---|---|---|
| Retail and corporate banking | Credit underwriting and scoring; credit-loss forecasting; anti-money-laundering (AML) monitoring; fraud monitoring and detection; tailored products; chat-based customer service | Assessing applications or expected losses, flagging transactions for review, and responding to or personalizing customer interactions |
| Asset management | Robo-advice; portfolio strategies; risk management | Supporting investment recommendations, portfolio analysis and risk assessment |
| Trading | Algorithmic trading | Analyzing data and supporting trading strategies |
| Insurance | Robo-advice; claims management | Supporting advice or the handling of claims |
| Financial supervision | Risk identification, research, and detection of possible legal violations, reporting errors or outliers | Helping regulators search for patterns or cases that may need closer attention |
The first four rows reflect applications identified in the OECD’s 2021 review. The supervisory examples come from the U.S. Government Accountability Office (GAO), which reported in 2025 on regulators’ use of AI.
What can it improve—and what is not established?
At the level of an institution, ML can process information quickly, support efficiency, personalize services and detect relationships or patterns that may be difficult for people to spot. GAO reported that regulators saw potential for AI to improve efficiency and effectiveness and identify issues, patterns and relationships that are hard for humans to find. Similar capabilities can be useful in financial firms, but the result depends on the data, model and workflow.
There is no single cross-industry performance figure established by the cited official sources for how much ML improves credit accuracy, reduces defaults, saves on fraud or increases trading returns. A model’s usefulness in one task or institution cannot, by itself, establish a general result for consumers or the financial system.
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What might consumers notice?
For a customer, ML is often embedded in a process rather than presented as a separate product. A system might contribute to a credit assessment, flag an account activity for review, route a service request or assist with an insurance claim. A chatbot may use generative AI, while scoring and anomaly-detection systems use other methods. The presence of a model does not tell you whether a person reviewed a particular result or how much weight the model carried.
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What are the main risks?
ML can create or amplify risks when inputs are incomplete, inaccurate, unrepresentative or used inappropriately. Some problems affect an individual customer; others can affect the resilience of firms or markets.
| Risk | How it can matter |
|---|---|
| Bias and consumer harm | Data or model choices may produce unfair outcomes or disadvantage some consumers. Treasury’s 2024 report identifies bias and potential consumer harm as concerns. |
| Privacy and data quality | Use of sensitive information raises privacy concerns; poor-quality or unsuitable data can undermine model outputs. Treasury identifies privacy risks, while the Financial Stability Board (FSB) groups data quality and model risk among its financial-stability vulnerabilities. |
| Opacity and accountability | If a model’s reasoning is difficult to interpret, it may be harder to explain an output, detect an error or establish who is responsible for acting on it. |
| Cyber risk and fraud | AI systems and their data can be exposed to cyber threats. The FSB also warns that generative AI can increase the potential for financial fraud and disinformation in markets. |
| Third-party concentration | Reliance on a limited set of external service providers can create dependencies that matter beyond a single firm. The FSB identifies third-party dependencies and service-provider concentration as a vulnerability cluster. |
| Correlated behavior and market effects | When many firms or systems respond similarly to signals, their actions could reinforce one another, potentially affecting volatility or market functioning. This is a market-wide concern, distinct from whether one institution’s model is useful. |
These risks do not mean that every ML application is unsafe. They mean that an institution’s controls, data and accountability arrangements matter alongside the model itself.
How are financial regulators approaching AI?
Requirements differ by jurisdiction and by use case; there is no single global rule that covers every financial ML system. The OECD’s 2024 survey examines regulatory approaches in 49 OECD and non-OECD jurisdictions and discusses how prudential rules, privacy laws and the EU AI Act can interact. It also notes a need for clarification around ML in internal-ratings and credit-assessment models.
When comparing rules or an institution’s approach, the useful questions include:
- Permitted use and risk classification: Is the application allowed, and is it treated as higher risk because of the decision or people it affects?
- Explainability and adverse-action duties: Must a firm explain a result or provide a reason when a decision negatively affects a customer?
- Data protection and retention: What data may be used, how is it protected, and how long may it be retained?
- Validation and monitoring: How does the institution test the model before use and watch for errors or changed performance afterward?
- Human oversight: When must a person review, challenge or override an output?
- Third-party accountability: Who is responsible when a service provider supplies the model or infrastructure?
- Incident reporting: What events must be reported, and to whom?
These are comparison questions, not a universal checklist of legal duties. The answer depends on the jurisdiction, financial activity and particular model. The U.S. Treasury’s 2024 report recommends further coordination on standards, analysis of consumer-harm gaps, supervisory clarification, information sharing and periodic compliance review of AI use cases—an indication that oversight continues to evolve.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do regulators use AI for?
Regulators may use AI to support research, identify potential risks and flag possible legal violations, reporting errors or unusual observations. These tools can help staff decide where to look more closely; they do not necessarily replace supervisory judgment.
GAO reported in 2025 that, as of December 2024, the regulators it asked said they used AI outputs alongside other supervisory information and did not use AI as an autonomous, sole source for supervisory or market-oversight decisions. That finding is specific to those regulators and the date in GAO’s account, not a claim about every regulator worldwide or later practice.
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Could AI trading create systemic risk?
Trading systems that use ML can contribute to correlated behavior if they react to similar data or incentives. In its November 2025 analysis, the Federal Reserve discusses possible risks involving correlated trading, collusion, manipulation, volatility and concentration. If multiple market participants respond in similar ways, their actions may reinforce a market move rather than counterbalance it.
The same analysis also notes a possible countervailing effect: richer information and more complex logic may diversify trading signals. The overall market effect is therefore not predetermined by the use of AI alone. It depends on how systems are designed and deployed, how concentrated their use is and how they interact with one another. Institution-level gains in speed or pattern detection do not automatically imply better outcomes for the market as a whole.
What should readers take away?
Machine learning is already used for varied financial tasks, from credit and fraud processes to portfolio analysis, claims and supervision. It can help institutions process information and find patterns, but it is not a guarantee of fair, accurate or beneficial decisions. For consumers, the key practical question is how a model contributes to a decision and what review or correction process applies. For markets and regulators, the additional questions are whether firms can monitor model behavior, manage shared dependencies and remain accountable when systems fail.
The FSB stated on November 14, 2024: “The rapid adoption of AI in finance means that authorities should address information gaps for monitoring, assess the adequacy of current policy frameworks and enhance supervisory and regulatory capabilities.”
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