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AI in finance is not one technology, and it does not always make a final decision on its own. Financial institutions use systems ranging from fixed rules to machine learning and generative AI to find patterns, produce scores or recommendations, flag activity, and assist staff. Their outputs can improve speed and analysis, but mistakes in data, model design, oversight, or security can harm customers and, when systems are widely shared, contribute to wider financial risks.
The examples and oversight described here reflect international sources and US community-bank guidance published through September 2025. Adoption and supervision continue to change; the evidence does not establish a single sector-wide adoption rate.
What “AI decisions” means in finance
“AI” is an umbrella term for systems that produce forecasts, content, predictions, or recommendations. In remarks published on 4 June 2024, Nellie Liang, then US Under Secretary for Domestic Finance and chair of the Financial Stability Board’s Standing Committee on Assessment of Vulnerabilities, distinguished among three broad approaches:
- Rule-based automation: software follows instructions written by people. A system might apply specified conditions to route a case or flag a transaction. It does not need to learn patterns from examples to do this.
- Machine learning: a model identifies patterns in data and uses them to classify cases, estimate outcomes, or produce a score. Machine learning has been used in financial fraud detection for years; it is not a new development created by generative AI.
- Generative AI: a model creates new content, such as text or code, in response to a prompt. Large language models are trained on language, making them more naturally suited to language tasks than to numerical risk measurement unless they are specifically designed and controlled for that purpose.
These approaches can be combined. For example, a fixed policy might set the conditions for a decision, while a machine-learning model supplies a score and a staff member reviews an unusual case. Calling the whole arrangement “AI” can obscure which component is doing what—and who is accountable for the outcome.
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Financial AI uses vary in maturity and purpose. The Bank for International Settlements’ Financial Stability Institute (BIS FSI) reported in June 2025 that institutional use was primarily aimed at improving internal operations and regulatory compliance; revenue-generating uses remained limited in its summary, alongside cautious experimentation with generative AI. That does not mean every institution uses every listed application, or that each application has the same level of adoption or effectiveness.
How a financial AI output becomes an action
A model’s output is usually one input to a larger process, rather than an automatic decision in isolation. A useful way to understand the chain is to separate the stages:
- Collect and prepare data. An institution gathers information relevant to the task, checks and formats it, and supplies it to a rule system or model. The usefulness of the result depends partly on whether the data are accurate, relevant, permitted for use, and representative of the cases being assessed.
- Generate an output. The system may return a score, classification, forecast, flag, summary, or recommendation. A generative system may produce text rather than a numerical result.
- Apply institutional policy. The institution decides how the output fits its rules, thresholds, and workflow. A score alone does not specify what action to take; the policy governing its use matters.
- Review or implement the action. A person or an automated process may act on the output, send it for further review, or reject it. The degree of human involvement and the options for correction or appeal depend on the particular process.
This is an explanatory framework, not a claim that every financial institution follows the same pipeline. Its practical value is that it helps locate where a problem may arise: in the underlying information, model, policy, or implementation. Governance and oversight have to cover the whole chain, not only the software that generated the score.
What financial institutions use AI for
Official sources describe uses across banking, insurance, and other financial activities. These include fraud detection, risk modelling, trading, claims handling, financial-crime prevention, customer service, regulatory compliance, and internal operations. Generative AI has also prompted experiments with document summarisation, information retrieval, code generation, customer personalisation, and tailored investment advice.
Detecting patterns and supporting assessments
Machine learning can help sift through large volumes of information to identify patterns that might be difficult to spot manually. In a fraud workflow, a system may flag activity for review rather than determine by itself that fraud occurred. A lender may use additional variables, including less traditional data, when estimating creditworthiness. That broader data picture can add information, but it also makes the source, accuracy, permitted use, privacy, and potential bias of each variable more important.
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A model’s output is not proof of what happened or a guarantee that a person will receive a particular result. Its meaning depends on what it was designed to estimate, what information it received, how the institution uses it, and whether a person or another process reviews the case.
Assisting staff and handling language tasks
Generative AI can assist with tasks involving language, such as summarising documents or retrieving information. Customer-service support is a natural fit for a language-trained system, but a fluent answer is not necessarily a correct one. Numerical risk measurement requires appropriate design and controls; simply asking a language model to provide a financial risk judgment does not establish that it is reliable for that purpose.
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Where the benefits come from—and their limits
Software can process more information, and more varieties of it, than a manual workflow can easily handle. Depending on the task and implementation, that capacity may help an institution increase productivity, reduce costs, identify suspicious patterns, or tailor services. A lender considering more variables may get a more comprehensive view than one relying on a narrower set of traditional information.
These are potential gains, not assurances that an AI system will be faster, cheaper, fairer, or more accurate in every setting. A larger volume of input does not automatically mean better evidence: irrelevant, inaccurate, or inappropriate data can make a result worse. More complex outputs can also be harder to explain and validate. The relevant question is whether the system improves a specific process under appropriate controls, not whether it uses AI.
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How AI-based financial decisions can fail
Biased data and unfair outcomes
Historical data can reflect past discrimination or other unequal treatment. A model trained on those data may reproduce patterns that disadvantage people, even if protected characteristics are not explicitly included. Less traditional variables may also act as proxies for protected characteristics. Using more variables therefore does not make a model inherently fair.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchOpacity can make the problem more difficult to detect. If a person is denied credit or another service, an institution may struggle to identify why a model produced its recommendation when the model’s reasoning is unclear. Liang warned in her 2024 remarks that reliance on historical data can be particularly problematic when a model’s reasoning is unclear and its output could result in a person being wrongfully denied service or credit.
Data breadth creates a separate consumer concern: it may be harder for people to find and correct errors when information comes from varied sources. An incorrect input can affect a score or recommendation even when the model behaves exactly as designed. Data provenance, accuracy, permitted use, privacy, and meaningful ways to address inaccuracies are therefore part of decision quality, not merely technical details.
Model errors, opacity, and weak governance
A poorly designed or misused model, bad inputs, weak validation, or inadequate governance can produce erroneous outcomes. The Office of the Comptroller of the Currency (OCC) describes adverse consequences resulting from incorrect or misused models as “model risk.” A model that performed acceptably on one set of examples may not be reliable in other conditions, so institutions need to assess whether its design and use remain suitable.
BIS FSI analysis identifies opacity, limited explainability, and opaque training data as obstacles to validation and monitoring. These problems matter because a convincing output is not the same as a verified one. Generative AI brings additional concerns: it can produce hallucinations—content that appears plausible but is wrong—and people may anthropomorphise it, treating its responses as if they came from an informed, accountable human.
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When an output is wrong, responsibility cannot be assigned to the model as though it were an independent decision-maker. The institution still has to understand how the tool is used, assess its risks, and oversee the workflow in which its output has an effect.
Fraud, cyberattacks, and manipulated information
AI can help institutions detect suspicious patterns, but the same capabilities can also assist attackers. FSB and BIS FSI materials identify risks including more effective fraud, deepfakes, synthetic identities, model poisoning, and disinformation. Model poisoning involves corrupting information used by a model in an attempt to distort its behaviour.
These risks are reasons for security and fraud controls, not evidence that every institution is experiencing them at a particular rate. The official material cited here does not establish a sector-wide frequency for each threat. A sound assessment distinguishes a plausible threat from a measured level of actual incidents.
Shared providers and correlated decisions
AI can create risks beyond an individual customer or firm. If multiple institutions rely on similar models, data, infrastructure, or a concentrated group of cloud or AI providers, their decisions and vulnerabilities may be connected. A disruption at a shared provider can affect more than one institution; similar models can also react in similar ways to the same conditions.
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How to compare two financial AI uses
When assessing a proposed use or trying to understand an existing one, compare the whole decision process rather than focusing only on the model’s name. The following questions synthesize risks and controls identified by the FSB, BIS FSI, and OCC; they are a practical framework, not a regulator-issued scorecard.
- Decision impact: What can the output change, and who could be harmed if it is wrong?
- Data: Where does the information come from? Is it accurate and representative? Can an affected person identify and correct an error?
- Validation and explanation: Can the institution test the system’s performance and explain how it contributes to an outcome?
- Automation and review: Does the output trigger an action, inform a human decision, or prompt additional review? Is there a way to challenge or correct the result?
- Dependencies: Does the use depend on a third-party model, cloud provider, or other concentrated infrastructure? What security risks follow from those dependencies?
- Applicable controls: Which jurisdiction’s laws and supervisory expectations apply, and how will the institution monitor the system over time?
The answers can differ substantially even for two systems described with the same label. A language assistant used to retrieve internal information and a model that contributes to a credit assessment have different potential impacts and require scrutiny appropriate to their roles.
What regulators and financial institutions are doing
In her 2024 account, Liang said that established model-risk principles address model design, governance, audit, and data quality; third-party-risk principles address vendors; and fair-lending, fair-credit, privacy, and securities laws address consumer and investor risks. She described these frameworks as technology-neutral, while also noting the need to assess whether they remain sufficient as uses change. This is her explanation of the US framework and should not be read as a legal conclusion applying identically in every country.
US guidance for community banks
The OCC’s Bulletin 2025-26, dated 18 September 2025, clarifies model-risk management expectations for community banks under US supervision. It says that management should be commensurate with a bank’s exposure, activities, and the complexity and extent of its model use. The bulletin also says the OCC will not provide negative supervisory feedback solely because of validation frequency or scope that a bank reasonably determines based on those factors.
The bulletin is specific to US community-bank supervision; it is not a universal standard for all financial firms. Its risk-based approach also does not mean that a bank can ignore model risk. The OCC’s stated principle is that “Model risk should be managed like other types of risk.”
International monitoring and remaining questions
The FSB’s November 2024 report calls on authorities to address information gaps about AI adoption, assess whether policy frameworks are adequate, and improve supervisory and regulatory capabilities. BIS FSI’s June 2025 summary likewise describes limited uptake data and fast innovation as challenges for surveillance. It points to measures such as surveys, regulatory reporting, disclosures, and industry engagement as ways to improve monitoring.
That evidence does not support a precise, sector-wide adoption percentage. It does support a more careful conclusion: authorities are tracking the uses and risks they can identify, while seeking better information and continuing to assess whether existing controls are sufficient as financial applications evolve. Established controls apply in many settings, but the specific requirements depend on the jurisdiction, institution, and use.
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