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AI in finance is not one technology, and it does not always make the final decision. Banks, insurers and other financial institutions can use rule-based automation, machine learning and generative AI to detect patterns, produce forecasts, flag suspicious activity, assess information and support operational work. In a typical decision process, data feed a model or rules system; its output is considered under the institution’s policies; and a human or automated workflow acts on or reviews the result. Every stage can introduce errors or harm—especially when data are biased or inaccurate, outputs are difficult to explain, controls are weak, or institutions depend on similar models and providers.
International assessments by the Financial Stability Board (FSB) and the Bank for International Settlements (BIS), together with US guidance from the Office of the Comptroller of the Currency (OCC), describe both potential benefits and risks. The findings below reflect official material published through September 2025 and adoption and supervision conditions reported as of October 2026; practices and rules vary across jurisdictions and continue to evolve.
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What does it mean for AI to make a financial decision?
“AI” describes a range of systems, not a single kind of software. In remarks published on 4 June 2024, Nellie Liang, then US Under Secretary for Domestic Finance and chair of the FSB Standing Committee on Assessment of Vulnerabilities, described AI broadly as systems that produce forecasts, content, predictions or recommendations. That includes familiar automation as well as newer generative models.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →- Rule-based automation follows instructions written by people: for example, a system may route a transaction for review when a specified condition is met.
- Machine learning uses patterns in data to classify, score or predict. It has been used in financial services for years, including fraud detection; mobile check deposit can use it to read handwriting.
- Generative AI creates new content, such as text or code, in response to prompts. Large language models are trained to work with language, so they are more naturally suited to tasks such as summarising documents or assisting customer-service staff than to numerical risk measurement without suitable design and controls.
These categories can overlap in a financial workflow. An institution might use a model to flag a case, a rules system to determine which cases need review, and an employee to decide what action to take. An AI-generated output may inform a decision without being the decision itself. The degree of automation and human review differs by task and institution; the official assessments do not establish that every firm uses autonomous systems to make final decisions.
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How an AI-supported financial decision is made
A useful way to understand the process is to follow the information from collection to action. This is a general framework, not a claim that every institution uses the same pipeline.
- Collect and prepare information. A firm selects data relevant to a task, checks or transforms them, and supplies them to a rules system or model. The source and quality of those data matter: inaccurate, incomplete or unsuitable inputs can undermine later steps.
- Produce an output. Depending on the system, the result may be a score, classification, forecast, alert, summary or generated recommendation. A score is not, by itself, a lending policy or a complete explanation of an outcome.
- Apply policy and thresholds. The institution determines how the output is used. A threshold might direct a case for investigation, while a policy may combine model output with other information. The model’s role and the institution’s rules both affect the result.
- Review or act. A human employee or automated workflow may investigate, approve, deny, escalate or otherwise respond. Where a person reviews the output, the usefulness of that safeguard depends on whether the reviewer can understand its limits and challenge it.
- Monitor the process. Institutions need to assess whether the model, inputs and controls continue to work as intended. The BIS Financial Stability Institute (FSI) identifies validation and monitoring as particular challenges when models or their training data are opaque.
This chain explains why a correct calculation alone cannot guarantee a sound decision. Data selection, model design, institutional policy, deployment and oversight each influence the result. The OCC describes harmful outcomes from incorrect or misused models as “model risk.”
Where financial institutions use AI—and what it may help them do
Official reports describe applications across internal operations, customer-facing work and risk management, but they do not establish that every use is equally widespread or effective. The BIS FSI’s June 2025 executive summary says institutional use is concentrated primarily in internal operations and regulatory compliance. Revenue-generating uses remain limited in that summary, while generative AI has prompted cautious experimentation.
- Operations and information work: document summarisation, information retrieval, code generation and process support can help staff handle large volumes of material.
- Customer service and personalisation: language models may assist with customer-service tasks; institutions may also explore ways to tailor services or advice. Experiments with tailored investment advice should not be mistaken for evidence that such systems are broadly deployed or reliably make investment decisions.
- Fraud and financial-crime prevention: pattern-detection systems can flag suspicious activity for further investigation. Machine-learning fraud detection predates the recent expansion of generative AI.
- Credit and risk assessment: lenders may use additional variables or less traditional data when estimating creditworthiness, while institutions also use AI in risk modelling, trading, claims handling and compliance.
Processing more information can help institutions find patterns, improve productivity, reduce costs and identify activity that merits attention. But a larger data set is not automatically a better or fairer one. More variables can bring more opportunities for useful analysis and more responsibility to check data provenance, accuracy, permitted use, privacy and bias.
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How AI can produce unfair or inaccurate outcomes for people
Historical data can carry forward past bias
A model trained or informed by historical decisions may reproduce patterns that reflect past discrimination or unequal access. If its reasoning is difficult to inspect, it may be hard to tell whether a result reflects a relevant measure of risk or an unfair pattern embedded in the data. In her 2024 remarks, Liang warned that reliance on historical data can be particularly problematic when a model’s reasoning is unclear and a consumer may be wrongfully denied credit or service.
Extra variables can act as proxies
Using less traditional information may make a lender’s picture of an applicant more comprehensive, but a variable need not explicitly identify a protected characteristic to raise concerns. It may act as a proxy for one. A system that considers many inputs is not inherently accurate, neutral or fair simply because it uses more data.
Errors may be hard to find and correct
Incorrect input data can distort an output, and a consumer may have difficulty discovering which information contributed to a result—especially when many data sources are involved. Opaque models can make it harder for a firm to explain why it acted and for affected people to identify what should be corrected. The risk is not limited to lending: the same questions about data quality, explanation and review apply wherever an output affects access to a financial service.
How models, data and governance can fail inside a firm
Financial decisions can be wrong because a model was poorly designed, used outside its appropriate purpose, fed bad data, inadequately validated or governed without effective oversight. These are connected but distinct failure points: a well-designed model can still be undermined by unsuitable inputs or misuse, while a sound process can still fail to catch a limitation if monitoring is weak.
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The BIS FSI’s December 2024 analysis highlights opacity, limited explainability and opaque training data as obstacles to validating and monitoring AI. Generative AI adds specific concerns: it can produce plausible but incorrect content, often called hallucination, and people may attribute human-like understanding or authority to a system that does not have it. Those properties make it important to check generated answers rather than treating fluent output as proof.
Responsibility does not disappear when a model is hard to explain or a vendor supplies it. Institutions still need governance that addresses design, use, data and oversight. A model output should be treated as an input to a controlled process, not as evidence that the process is automatically correct.
How AI changes fraud and cyber risk
AI can help defensive teams find suspicious patterns, but the same broad capabilities can assist attackers. The FSB’s November 2024 assessment and the BIS FSI’s June 2025 summary identify concerns including more effective fraud, deepfakes, synthetic identities, model poisoning and disinformation. These are possible risk channels, not evidence that every threat is common or that a particular isolated incident represents the whole financial sector.
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Why similar models and providers can create system-wide risk
A problem confined to one model or firm can become more consequential if many institutions rely on similar tools, data, infrastructure or third-party providers. The FSB has identified provider concentration and market correlation among vulnerabilities with potential financial-stability implications. The BIS FSI likewise discusses risks from interconnected use and common dependencies.
If institutions respond to the same signals with similar models or rules, their lending, trading, liquidity or risk decisions may move in the same direction. Under stress, that correlation could amplify a common market movement rather than offset it. Concentrated reliance on a small set of cloud or AI providers can also transmit disruption across firms if a shared service fails or becomes unavailable. These are systemic pathways supervisors monitor; they do not mean that AI has already caused a particular crisis.
Measuring the exposure is difficult when authorities have limited data about which institutions use which systems, how extensively they use them and what dependencies they share. The FSB’s November 2024 report called on authorities to address information gaps, assess existing policy frameworks and strengthen supervisory and regulatory capabilities. The BIS FSI’s June 2025 summary points to surveys, regulatory reporting, disclosures and industry engagement as ways to improve monitoring.
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What controls and oversight apply?
In 2024 remarks, 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 presented these as existing, generally technology-neutral frameworks in the US context, while noting that authorities must assess whether they remain sufficient as AI uses change. This is an account of existing frameworks, not a universal legal opinion: obligations vary by jurisdiction, institution and activity.
For US community banks specifically, OCC Bulletin 2025-26, published on 18 September 2025, clarifies that model-risk management should be proportionate to a bank’s exposure, activities, and the complexity and extent of its model use. It says the OCC will not give negative supervisory feedback solely because a bank reasonably determines the frequency or scope of validation based on those factors. The bulletin states, “Model risk should be managed like other types of risk.” This clarification is limited to community banks under OCC supervision; it should not be applied as a blanket rule for every institution or country.
Supervisors are also adapting how they observe adoption and dependencies. The FSB has called for better information on AI use, assessment of whether policy frameworks are adequate, stronger supervisory capabilities and cross-border information sharing. The BIS FSI notes that rapid innovation and limited uptake data complicate surveillance. Monitoring methods it highlights include surveys, regulatory reporting, disclosures and engagement with industry.
How to compare two AI-supported financial decisions
The following questions form a practical comparison framework synthesized from risks and controls identified by the FSB, BIS FSI and OCC. It is not a regulator-issued scoring standard. Use it to compare a proposed use with an existing one, or to understand why two systems with similar labels may carry different risks.
- Decision impact: What outcome can the system influence, and who could be harmed if it is wrong?
- Data: Where do the inputs come from? Are they accurate and representative, permitted for this use, and correctable when they are wrong?
- Explainability and validation: Can the institution understand and test the output, including the limitations of the model and its data?
- Automation and recourse: Does the system merely flag or recommend, or can it trigger action? Is there meaningful human review and a way to challenge an outcome?
- Dependencies: Which vendors, cloud services, models or shared data sources does the use depend on, and could a failure affect other firms too?
- Applicable controls: Which jurisdiction’s laws and supervisory expectations apply, and how will the institution monitor the system over time?
This comparison keeps attention on the whole decision process rather than the AI label. A language model that drafts an internal summary and a model that contributes to a credit decision may both use AI, but the potential harm, need for explanation and appropriate safeguards are not the same.
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