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A vendor may also face liability, but that cannot be decided from the fact that its model caused harm. The answer depends on the contract, the vendor’s conduct, the claims being made, the type of decision, and the law that applies. U.S. supervisory guidance clearly assigns continuing responsibility to the bank; it does not declare that every vendor is automatically liable or immune.
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The three meanings of “responsible”
When people ask who is responsible for an AI-driven banking error, they may be asking about three different duties. Separating them prevents a common mistake: treating supervisory responsibility, a consumer’s legal rights, and a vendor’s private-law liability as if they were the same question.
| Role | What the role generally covers | What the cited authorities establish |
|---|---|---|
| Bank or banking organization | Safe and sound operations, compliance management, third-party oversight, and remediation of risks created by outsourced activities. | Interagency guidance says using a third party does not diminish the bank’s responsibility to meet applicable requirements as if the activity were performed in-house. |
| Creditor | Consumer-credit duties when an application is denied, conditioned, or otherwise subject to adverse action. | ECOA and Regulation B require specific, accurate principal reasons for an adverse action, even when a complex algorithm produced the decision. |
| AI vendor | Contractual promises, representations, service obligations, and any duties imposed by applicable law. | Vendor liability is fact-specific. The cited supervisory and CFPB materials do not make a categorical finding that every vendor is liable or that no vendor can be sued. |
The decision involved matters too. A credit denial has a defined adverse-action notice framework. A fraud alert, account closure, deposit decision, underwriting recommendation, or customer-service output may be governed by different laws and supervisory expectations.
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Why outsourcing does not transfer the bank’s duty
The Federal Reserve, FDIC, and OCC’s Interagency Guidance on Third-Party Relationships: Risk Management, issued June 6, 2023, states that a banking organization remains responsible for safe and sound operations and compliance with applicable legal requirements when a third party performs an activity. Its central principle is that third-party use does not diminish the bank’s responsibility “to the same extent as if its activities were performed by the banking organization in-house.”
That principle does not mean a bank must build every model itself. It means the bank must govern the outsourced activity as a bank activity. Oversight should be proportionate to the bank’s size, complexity, risk profile, and the nature of the relationship. More comprehensive controls are appropriate when the arrangement supports a higher-risk or critical activity, particularly one that can significantly affect customers.
What risk-based oversight involves
- Due diligence before selection: assess the vendor’s financial condition, expertise, security, data practices, compliance history, business continuity, and ability to explain and support the product.
- Clear contracting: define performance standards, data ownership and access, audit and testing rights, incident notification, subcontracting controls, records retention, change management, and exit assistance.
- Ongoing monitoring: track performance, complaints, exceptions, security events, drift, disparate outcomes, and failures against the bank’s stated use case.
- Escalation and remediation: establish who can suspend the model, investigate an incident, correct affected accounts, notify customers or regulators when required, and document the decision.
The 2024 joint statement from the Federal Reserve, FDIC, and OCC concerning banks’ arrangements with third parties to deliver deposit products reemphasized existing guidance. It said the statement did not change legal requirements or create new supervisory expectations. That distinction matters: a policy statement or supervisory reminder is not automatically a new cause of action against a bank or vendor.
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For a credit application, the creditor cannot answer a denied applicant with “the vendor’s model is too complex to explain.” The CFPB’s Consumer Financial Protection Circular 2022-03, issued May 26, 2022, says ECOA and Regulation B’s adverse-action requirements apply regardless of the technology used. Its rule is direct: “Whether a creditor is using a sophisticated machine learning algorithm or more conventional methods to evaluate an application, the legal requirement is the same: Creditors must be able to provide applicants against whom adverse action is taken with an accurate statement of reasons.”
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Regulation B § 1002.9 requires reasons that are specific and identify the principal factors for the adverse action. A generic statement such as “internal policy,” “score too low,” or “algorithmic assessment” ordinarily does not tell the applicant what principal characteristics led to the result. The creditor must be able to connect the notice to the actual decision rather than inventing a plausible explanation after the fact.
Who must send the notice?
The creditor that takes the adverse action must comply directly or through a third party in the circumstances described by the rule. Hiring a scoring provider does not turn the provider into the party that bears the creditor’s consumer-notice duty. A bank therefore needs a process that preserves the relevant decision factors, maps them to legally sufficient reasons, and checks that the notice is accurate for the individual application.
What if the model is proprietary?
Limited access to source code, training data, weights, or methodology is a vendor-management problem, not an exemption from the adverse-action rule. Before deployment, the creditor should establish how it will obtain and verify principal reasons. If the vendor cannot support accurate, specific explanations, the creditor faces a compliance problem even if the model performs well on other measures.
CFPB materials describe amendments to Regulation B in 2026. Because the operative rule and examination procedures can change, a bank or adviser should check the current text of § 1002.9 and the applicable CFPB procedures for the relevant date, product, and transaction.
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How model-risk expectations apply to vendor products
The Federal Reserve’s revised Supervisory Guidance on Model Risk Management, issued April 17, 2026, expressly addresses vendor and other third-party models. Proprietary components may limit a bank’s access to underlying code, data, and methodology, but the guidance says model-risk principles still apply.
Understanding the model before relying on it
Sound practice begins with a documented understanding of the model’s purpose, design, assumptions, intended population, inputs, limitations, and dependencies. The bank should understand the development data and how the vendor measured performance. “Black box” should describe an access limitation to be managed, not a reason to skip investigation.
Independent validation
Validation may be performed by qualified internal staff or outside parties, provided they have appropriate independence and expertise. Testing should address conceptual soundness, implementation, data quality, accuracy, stability, known limitations, and whether the model is fit for the bank’s actual use rather than merely the vendor’s demonstration environment.
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Monitoring and outcome analysis
After deployment, the bank should monitor performance and outcomes over time. Useful controls can include exception rates, approval or denial patterns, error analysis, complaint trends, data drift, and results for relevant customer segments. Outcome analysis can reveal that a model that passed predeployment tests is no longer reliable after a population, product, policy, or vendor update changes.
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The 2026 guidance says these practices should be tailored to the bank’s model-risk profile, size, and complexity. It is supervisory guidance, not a prescriptive regulation. The document states that noncompliance with the guidance alone will not result in supervisory criticism. Supervisory action can still arise from a violation of law or from unsafe or unsound practices caused by inadequate model-risk management.
What regulators have said about generative AI
In an April 4, 2025 speech, Federal Reserve Governor Michael S. Barr said: “To the extent banks are using Gen AI or offering Gen AI products and services, they have the responsibility to manage their risk, and should use their relationships to incentivize good risk management practices for fintechs.”
That statement expresses a regulator’s supervisory view; it is not a new statute or regulation. Its practical message is nevertheless important: a bank should use its commercial relationship to require evidence, controls, reporting, and correction from a fintech or other technology provider instead of assuming the provider’s assurances end the bank’s responsibility.
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There is no universal rule that the vendor pays whenever its system contributes to harm. Liability can turn on the wording of the services agreement, warranties, indemnities, limitations of liability, insurance, intellectual-property terms, data obligations, and the parties’ actual conduct. The claimant’s theory also matters: possible theories in a particular dispute could involve contract, negligence, consumer-protection statutes, discrimination law, privacy or security law, or another jurisdiction-specific cause of action.
A contract may allocate costs between the bank and vendor, but a private allocation does not necessarily erase the bank’s regulatory duties to supervisors or consumers. Conversely, the bank’s continuing supervisory responsibility does not automatically eliminate a vendor’s exposure under a contract or other applicable law. Determining the allocation requires the facts, the agreement, the evidence about how the model was designed and used, and the law governing the dispute.
A practical accountability process for banks
The following sequence turns the broad responsibility principle into operational decisions. The exact controls should be scaled to the use case and risk.
- Define the decision and harm scenario. Identify whether the system recommends or makes a credit decision, fraud intervention, account action, pricing decision, or another outcome. Record who has final authority and which customers can be affected.
- Classify the activity’s risk. Treat customer-critical or high-impact uses as requiring deeper diligence, testing, documentation, monitoring, and senior oversight than low-impact administrative uses.
- Test explainability and data fitness before launch. Confirm that the bank can identify principal factors for an adverse credit action, that inputs are permitted and accurate, and that the model’s documented purpose matches the proposed deployment.
- Put evidence and controls in the contract. Require timely access to documentation, testing results, incident information, change notices, records, and cooperation with audits or regulatory inquiries. Address subcontractors and the process for suspending or replacing the service.
- Validate independently and approve a use case. Document limitations, thresholds, compensating controls, and the person or committee that accepts residual risk. Approval for one product or population should not silently expand to another.
- Monitor live outcomes. Set thresholds for errors, drift, unexplained changes, complaints, and adverse customer outcomes. Assign owners and deadlines for investigation.
- Respond quickly to a harmful result. Preserve logs and decision data, stop or limit the affected function when appropriate, determine the population of potentially affected customers, correct decisions where required, and document communications and remediation.
- Review material vendor changes. A model update, new training data, changed threshold, subcontractor, or altered use case should trigger change-management review rather than being treated as routine maintenance.
What an affected customer can do
A person who receives a harmful credit decision should keep the application, decision notice, dates, communications, and any records showing the information supplied to the creditor. The first request should be directed to the creditor named in the notice: ask for the specific principal reasons and for correction of inaccurate application or account information.
If the response is incomplete or the issue involves potential discrimination, privacy, security, or another legal right, the person can consider a complaint to the appropriate banking or consumer-protection regulator and seek advice from a qualified lawyer. The correct forum depends on the institution, product, location, timing, and facts. A vendor’s name appearing in a privacy policy or technical document does not by itself establish that the vendor is the legally responsible party.
Quick Recap
Common mistakes to avoid
- “The vendor made the decision, so the bank is off the hook.” The third-party guidance rejects that premise for the bank’s supervisory and compliance responsibilities.
- “The guidance is a new enforceable rule.” The 2026 model-risk guidance is expressly non-prescriptive; separate violations of law or unsafe and unsound practices remain consequential.
- “A black-box model makes credit reasons impossible.” For adverse credit action, opacity does not excuse the creditor from providing specific and accurate principal reasons.
- “The vendor is always the defendant.” Vendor liability depends on the contract, claims, conduct, and applicable law.
- “The credit rule covers every AI harm.” The CFPB circular addresses adverse action in credit decisions. Other banking decisions require analysis under their own legal and supervisory frameworks.
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