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These tools can help financial institutions process information faster and spot patterns that are difficult to find manually. But inaccurate data, biased patterns, hard-to-explain outputs, security threats, and reliance on the same providers can harm customers or create risks that spread beyond one firm. Understanding where AI fits in the decision chain makes it easier to see both its potential and its limits.
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What “AI decisions” mean in finance
Financial AI refers to several kinds of systems, not a single machine that independently manages money or approves every transaction. In remarks published by the Financial Stability Board (FSB) 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 broad definition covers technologies with different capabilities. The distinctions matter: a language-generating tool has different strengths and failure modes from a system designed to score transactions or detect patterns in structured data.
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- Rule-based automation: A program follows predefined instructions, such as applying a threshold or routing a case for review. This kind of automation predates current generative AI.
- Machine learning: A model identifies patterns in data and uses them to produce a prediction, classification, or score. Financial institutions have used machine learning for tasks such as fraud detection for years.
- Generative AI: A model creates new material, such as text, code, or a summary, in response to an input. Large language models are trained to work with language, which makes them more naturally suited to tasks such as helping with customer-service responses than to numerical risk measurement without appropriate design and controls.
The FSB and the Bank for International Settlements’ Financial Stability Institute (BIS FSI) describe a wide range of potential uses: internal operations, customer service, fraud detection, compliance, risk modelling, trading, claims handling, financial-crime prevention, document summarisation, information retrieval, code generation, customer personalisation, and experiments with tailored investment advice. These examples do not imply that every use is widespread, effective, or fully automated. The BIS FSI’s June 2025 summary says current institutional use is concentrated mainly in internal operations and regulatory compliance; revenue-generating uses remain limited, while generative AI is being explored cautiously.
How a financial AI output becomes a decision
A useful way to understand AI in finance is to follow the path from information to action. The details vary by institution and task, but a typical decision chain has four stages:
- Collect and prepare information. A firm gathers relevant data, checks or transforms it, and supplies it to a rules system or model. For a credit assessment, data might include information used to estimate a person’s ability to repay. What is collected, where it came from, whether it is accurate, and whether it is appropriate to use all matter.
- Generate an output. The system may return a score, classification, forecast, flagged case, summary, or suggested response. An output is not automatically a finding of fact: a model’s prediction can be wrong, and generated text can be inaccurate.
- Apply institutional policy. The firm decides how the output should affect the case. Policies and thresholds may determine whether a transaction is flagged, an application receives additional review, or a suggested response is passed to an employee. The same model output could be treated differently under different policies.
- Take or review an action. A person or automated workflow may implement the resulting action, examine an exception, or review a recommendation. The amount and timing of human review vary. The existence of an AI system alone does not establish who had authority over a particular decision.
This is an explanatory framework, not a claim that every institution uses the same pipeline. It highlights why examining the model alone is not enough: the input data, model design, the rules for using its output, and the review process can each affect the result.
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What financial institutions may use AI for
Operations and customer support
Tools that search documents, summarise information, retrieve internal guidance, or draft text can help employees handle routine language-heavy work. Customer-service support is a plausible use for a language model because the task involves language, but a generated answer still needs appropriate safeguards. An incorrect summary or response may misstate a policy or a customer’s circumstances.
Fraud detection and financial-crime prevention
Pattern-recognition systems can help identify transactions or activity that merit investigation. Machine learning has been used in fraud detection for years, and machine learning can also help mobile check-deposit systems read handwritten checks. A flag is a signal for assessment, not proof that a customer committed fraud. A false positive can cause inconvenience or delay, while a missed signal can allow suspicious activity to go undetected.
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Credit and risk assessment
Lenders may use models to assess creditworthiness, including by considering more variables or less traditional data than a conventional process might use. A broader data picture could provide additional information, but only if the data are accurate, appropriate, and handled with care. More inputs also mean more questions about their origin, permitted use, privacy, and whether they introduce unfair patterns.
Other uses and experiments
Financial firms may use or explore AI for risk modelling, trading, insurance claims, compliance, code generation, customer personalisation, and tailored investment advice. The FSB and BIS FSI discuss these as a varied set of uses, not as equally mature deployments. In particular, the BIS FSI’s June 2025 account describes cautious experimentation with generative AI and says revenue-generating applications remain limited compared with internal operations and compliance.
How AI can make a financial decision unfair or inaccurate
Historical bias and proxy variables
A model learns from the data it is given. If historical records reflect unequal treatment or other distortions, a model may reproduce patterns that disadvantage people, even when its developers do not explicitly provide a protected characteristic. Variables that appear neutral can also act as proxies for protected characteristics. Adding more variables does not automatically make an assessment fairer; it can add more ways for past bias to influence a result.
The risk is especially important when an opaque model informs a decision that affects access to credit or another service. In her 2024 remarks, Liang warned that reliance on historical data can be particularly problematic when a model’s reasoning is unclear and the result may wrongfully deny a consumer service or credit. Without a clear explanation of how an output was reached, it can be harder for a firm to detect a harmful pattern or for a customer to understand what went wrong.
Inaccurate, incomplete, or hard-to-correct data
An AI system cannot reliably repair a false or incomplete input just by processing it quickly. If information is wrong, outdated, or misattributed, an output may be wrong as well. Less traditional sources can broaden a lender’s view of a person, but they can also make it more difficult for a consumer to locate and correct an error. Data provenance—where information came from and how it was used—is therefore important alongside data quality.
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Data use also raises privacy and permission questions. A firm needs to consider whether the information is suitable for the task and how it is protected. Those concerns grow when a decision relies on a wider mix of data than a customer expects or can readily inspect.
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Models can be poorly designed, inadequately tested, misused, or deployed beyond the conditions for which they were assessed. Weak governance can let problems persist or make it unclear who is responsible for detecting them. The OCC calls adverse consequences resulting from incorrect or misused models “model risk.” Its September 2025 guidance stresses that model risk should be managed like other types of risk.
Opacity makes these problems harder to find. The BIS FSI identifies explainability and opaque training data as obstacles to validating and monitoring AI systems. Generative AI adds specific concerns: it can produce plausible-sounding but incorrect information, a failure often described as hallucination. People may also over-trust a conversational system or treat it as more human or authoritative than it is—an issue the BIS FSI describes as anthropomorphism risk. Fluency is not evidence of accuracy.
How AI-related risks can affect more than one customer
Fraud and cybersecurity
AI can assist defensive work, including the analysis of patterns that may indicate suspicious activity. The same general advances can also help bad actors create more convincing fraud, deepfakes, or synthetic identities, or support disinformation. Model poisoning—manipulating data or inputs in ways that undermine a model—is another security concern identified in the official analysis.
These are risk channels, not evidence that every financial institution is experiencing them at the same rate. The FSB’s November 2024 report and the BIS FSI’s June 2025 summary identify cyber and fraud concerns as vulnerabilities for authorities and firms to monitor; they do not establish a sector-wide frequency for each type of incident.
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Provider concentration and connected firms
When many institutions rely on a small group of cloud, data, or AI providers, an outage or other disruption at one provider could affect several firms at once. Concentration can also make it harder for institutions to switch providers quickly. This is different from a failure inside a single bank: shared dependencies can transmit disruption across institutions.
Correlated decisions and financial stability
Institutions that use similar models, data, or infrastructure may react to conditions in similar ways. If many firms tighten lending, change trading positions, or respond to liquidity pressures in parallel, their actions can amplify a common market movement. The FSB identifies provider concentration, market correlation, cyber risk, and weaknesses in model and data governance as vulnerabilities with potential financial-stability implications.
This is a systemic concern rather than a claim that AI has already caused a particular crisis. The risk depends on how widely systems and providers are shared, what decisions they influence, and how firms and supervisors respond when conditions change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How regulators and firms oversee financial AI
Financial regulation has not necessarily waited for a separate AI rulebook before addressing AI-related risks. In her June 2024 remarks, Liang said existing model-risk principles cover 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 noting that authorities still need to assess whether they remain sufficient as AI use changes. This is her explanation of the US and international frameworks she discussed, not a universal legal opinion about every jurisdiction.
Specific supervisory expectations depend on location, institution type, and the activity involved. For example, OCC Bulletin 2025-26, issued on 18 September 2025, concerns community banks under US supervision. It says model-risk management should be commensurate with a bank’s exposure, activities, and the complexity and extent of its model use. The OCC also says it will not give negative supervisory feedback solely because a bank reasonably determines the frequency or scope of validation based on those factors. This clarification is not a blanket exemption from model-risk management, nor is it guidance for every institution or country.
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Supervision itself faces a visibility challenge: authorities have limited information about where and how quickly firms are adopting AI. In its November 2024 report, the FSB called on authorities to address information gaps, assess whether policy frameworks are adequate, and strengthen supervisory and regulatory capabilities. The BIS FSI’s June 2025 summary points to tools such as surveys, regulatory reporting, disclosures, and industry engagement to improve monitoring, alongside cross-border information sharing.
What to ask when AI affects a financial decision
If a financial firm tells you that AI was involved in a decision, the label alone does not show what the system did or whether it decided the outcome. Focus on the specific action, the information used, and the available review process. The following questions can help you get a clearer account without assuming that every institution uses the same system:
- What did the system do? Ask whether it generated a recommendation, assigned a score, flagged activity, drafted content, or took an action through an automated workflow.
- Did a person review the result? Ask what role a human reviewer had and whether the system’s output was advisory or directly affected the action.
- What information was considered? If the decision seems to rely on incorrect or unfamiliar information, ask how to identify the data involved and how to submit a correction or request a review. Available procedures and rights depend on the relevant product and jurisdiction.
- How can you challenge the outcome? Ask the institution what process applies to your case, what information it needs, and whether a review can consider circumstances the model may not have captured.
- What explains the result? Ask for the institution’s explanation of the decision and the factors it says mattered. A model may be difficult to interpret, but that does not make a fluent or generic explanation proof that the output was correct.
For a consumer, the practical issue is the financial outcome, not whether a company uses the word “AI.” Keep the decision notice and relevant communications, check your own records for errors, and use the institution’s stated review or complaint channel. Do not assume that a model’s score is a definitive measure of your financial reliability.
A framework for comparing financial AI uses
There is no single regulator-issued scoring system in the cited material for ranking financial AI applications. The following comparison questions synthesize risk and oversight themes raised by the FSB, BIS FSI, and OCC. They are useful for understanding why two applications that both use AI may warrant different safeguards.
Quick Recap
| Comparison question | What to examine |
|---|---|
| Who could be affected, and how consequential is the decision? | Distinguish a tool that helps retrieve an internal document from a system that can influence access to credit, an insurance claim, a payment, or an investment-related service. |
| What data does it use? | Consider source, accuracy, representativeness, permitted use, privacy, and whether an affected person can identify and correct an error. |
| Can the output be validated and explained? | Ask whether the institution can test performance, identify limitations, understand how the system behaves, and monitor changes over time. |
| How automated is the action? | Establish whether the output is advisory, reviewed by a person, or used directly in a workflow, and what review or appeal route exists. |
| What external dependencies exist? | Identify relevant vendors, cloud or AI infrastructure, cybersecurity exposure, and the institution’s ability to manage provider disruption or concentration. |
| Which rules and supervisory expectations apply? | Check the relevant jurisdiction, institution type, product, and activity rather than assuming one country’s guidance governs all financial AI. |
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