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Third-party AI risk management is how a bank identifies, assesses, contracts for, monitors and eventually exits the risks created when an outside organization supplies, develops, hosts, operates or supports an AI system used in the bank’s business. Outsourcing the technology does not outsource the bank’s accountability: the bank remains responsible for safe, compliant operations.
What the term covers
A third-party AI relationship can be much broader than buying access to a model through an application programming interface. An external organization might provide training data, model parameters, a complete credit or fraud model, a hosted generative-AI service, cloud infrastructure, monitoring, implementation work or ongoing human operations. The bank’s risk therefore comes from both the AI system and the supplier relationship around it.
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Third-party AI risk management sits inside ordinary third-party relationship management, but it intersects with several specialist disciplines:
- Third-party risk management examines the provider, the service, dependencies, contract, performance and exit options.
- Model risk management addresses risks from model development, use, validation, data, assumptions and limitations.
- Information security and privacy address access, confidentiality, data handling, cyber events and unauthorized disclosure.
- Operational resilience addresses whether important services can continue through outages, failures or a provider’s financial or technical problems.
- Consumer-protection and compliance programs address outcomes such as unfair discrimination, inaccurate decisions, inadequate explanations or prohibited uses.
- AI governance addresses intended purpose, data quality, performance, human oversight, testing and effects on people.
These controls overlap, but none automatically replaces the others. A contract with a supplier does not by itself constitute model validation, privacy compliance or resilience testing.
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Why a bank remains responsible
The US Federal Reserve, FDIC and OCC stated in their June 2023 Interagency Guidance on Third-Party Relationships: “A banking organization’s use of third parties does not diminish its responsibility to meet these requirements to the same extent as if its activities were performed by the banking organization in-house.” In practical terms, the bank must be able to show that it understands the service, controls the risks and can respond when the supplier or system fails.
This principle applies even when a supplier markets a system as compliant, explainable or independently certified. A certification can be useful evidence, but it is not a determination that the bank’s particular use, data, customer population or legal obligations are covered. The bank must evaluate the actual arrangement and retain enough expertise to challenge the provider.
The same US guidance says it is risk-based and does not impose new requirements on banking organizations; the Federal Reserve reiterated that point in administrative letter CA 24-2 on May 8, 2024. It is therefore supervisory guidance rather than a universal AI statute. Requirements differ by jurisdiction, institution, service and use case.
A lifecycle for managing third-party AI
1. Plan and inventory the use
Start with an inventory that is detailed enough to connect each AI service to a business process. Record:
- the business purpose and decision or workflow the system supports;
- whether it affects customers, employees, markets, financial reporting or internal operations;
- the provider’s role, including development, hosting, operation, maintenance and support;
- data supplied to the system, where it is processed, who can access it and whether it is retained;
- upstream and downstream dependencies, including cloud infrastructure and subcontractors;
- the model or service version, material limitations, human review and fallback process; and
- the consequences of an incorrect output, outage, data leak or delayed update.
Classify the activity by impact and criticality. The US interagency guidance emphasizes that an activity critical for one bank may not be critical for another. A tool that drafts internal summaries may warrant lighter oversight than a system that approves loans, sets credit limits, screens transactions or determines whether a customer receives essential services.
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2. Assess and select the provider
Due diligence should be tailored to the exact service, not copied from a generic vendor questionnaire. Evaluate the supplier’s capability, financial and operational capacity, governance, security controls, privacy practices, legal and regulatory compliance, testing and change management. Ask how the provider measures performance, handles uncertainty and detects harmful or degraded outputs.
Obtain evidence that can be checked rather than relying on marketing statements. Depending on the risk, that evidence may include independent assurance reports, penetration-test summaries, access-control descriptions, data-flow diagrams, validation documentation, incident records, business-continuity tests, version histories and information about known limitations. Prior experience with the same supplier is not a substitute for diligence on the particular activity.
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The agreement should match the service’s risk and the bank’s jurisdiction. Contract terms commonly need to address:
- Information and audit: the bank’s access to relevant records, control evidence, testing results and personnel, directly or through an agreed independent assessor.
- Security and confidentiality: approved data uses, retention and deletion, encryption, access controls, vulnerability management and restrictions on using bank data to train other systems.
- Incidents: prompt notification, escalation contacts, cooperation with investigation and regulatory reporting, and preservation of evidence.
- Performance: service levels, accuracy or quality measures where measurable, support response times and remedies for recurring failure.
- Changes: advance notice of material model, data, hosting, interface, subcontractor or policy changes; testing and approval thresholds; and a record of versions used in decisions.
- Subcontracting and concentration: disclosure of important subcontractors, approval or notification rights and visibility into common providers used by multiple critical services.
- Termination and transition: data return or destruction, assistance moving to another provider or an internal process, continued access to records and a defined wind-down period.
Not every clause is required in identical form. The bank should document why the selected protections are proportionate to the activity and the legal regime that applies.
4. Monitor after onboarding
Approval is the start of oversight, not its endpoint. Monitor the provider and the AI service for control deterioration, security events, data loss, outages, missed service levels, compliance concerns, unusual output patterns and material changes. Review whether the system is still being used for its approved purpose and whether users have found workarounds that create new risk.
Set review frequency according to risk. A high-impact decision system may need continuous indicators, periodic outcome testing and rapid escalation, while a low-impact internal assistant may be reviewed less often. Monitoring should combine provider reports with the bank’s own observations and testing; accepting a supplier’s dashboard without challenge can miss problems that appear only in the bank’s data or customer population.
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5. Review and exit
At scheduled intervals, reassess the relationship, the service’s criticality and the bank’s risk appetite. Confirm that controls remain effective, the contract still reflects the service and the bank can operate if the provider is unavailable. A workable exit plan should identify replacement options, retained data and documentation, manual or internal fallback procedures, customer communications and the people responsible for executing the transition.
Exit planning is also a test of dependency. If the bank cannot reconstruct decisions, retrieve required records or replace a model because the provider controls all relevant information, the arrangement may be too concentrated or insufficiently documented even if day-to-day performance is good.
How model risk management fits
Third-party oversight and model risk management answer different primary questions. Third-party management asks whether the bank can safely rely on a relationship and service. Model risk management asks whether a model is appropriate, understood, properly implemented, validated and controlled for its intended use.
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They overlap when a supplier provides data, parameters, a complete model or an AI service that drives a model-based decision. The Federal Reserve, FDIC and OCC’s April 2026 Supervisory Guidance on Model Risk Management identifies third-party products as a validation challenge and says its principles apply to traditional statistical and quantitative models and to non-generative, non-agentic AI models. That wording should not be read as settling every question about generative or agentic AI. Banks need to determine how their existing model-risk framework applies and where additional AI-specific controls are needed.
Validation should be able to examine purpose, data, methodology, implementation, performance, limitations, monitoring and human use. Where the provider will not disclose proprietary details, the bank should identify what independent testing, documentation, output testing or contractual evidence can compensate for the lack of direct access. “Black box” should be treated as a risk to resolve or explicitly accept, not as a reason to skip validation.
AI governance questions that a vendor review must answer
AI governance adds questions about the system’s intended use and effects that a conventional supplier review may miss. Before deployment, the bank should be able to answer:
- What decisions may the system support, and which decisions are prohibited?
- What data was used to develop or tune it, and what data will be sent during operation?
- How are data quality, representativeness, bias and drift assessed for this bank’s population?
- What accuracy, error, robustness and security tests were performed, under what conditions and with what results?
- When must a qualified person review, override or stop an output?
- Can the bank explain a material decision to a customer, auditor or supervisor at the level required by law?
- How are prompts, outputs, versions, approvals and overrides logged and retained?
- How are harmful content, prompt injection, data leakage and unauthorized use detected and handled for generative systems?
NIST’s AI Risk Management Framework and its Generative AI Profile, published as voluntary resources, can help organize these questions. They do not replace applicable banking supervision, privacy law, consumer-protection rules or a jurisdiction-specific AI regulation.
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US and EU rules are not interchangeable
| Regime or resource | What it contributes | Important qualification |
|---|---|---|
| US interagency third-party guidance (2023) | Risk-based principles for planning, due diligence, contracts, ongoing monitoring and termination of third-party relationships. | Applies to supervised banking organizations; the Federal Reserve says the guidance does not impose new requirements. |
| US model-risk guidance (April 2026) | Principles for model development, use, validation and controls, including challenges presented by third-party products. | It expressly discusses traditional and non-generative, non-agentic AI models; do not assume it resolves every generative or agentic-AI issue. |
| NIST AI RMF and Generative AI Profile (July 26, 2024) | Voluntary methods for identifying, measuring and managing AI risks. | They are framework resources, not bank-specific regulations. |
| DORA, Regulation (EU) 2022/2554 | ICT-risk management, digital operational resilience and oversight of ICT third-party arrangements for covered financial entities. | It has applied since January 17, 2025. Confirm whether the entity and service fall within its scope. |
| EBA third-party-risk guidance | Lifecycle management for ICT and non-ICT arrangements, with particular attention to services supporting critical or important functions. | Application depends on the institution, arrangement and applicable EU supervisory framework. |
| EBA AI Act analysis (updated November 20, 2025) | Explains implications for EU banking uses, including creditworthiness or credit-score assessment of natural persons. | Those uses are classified as high-risk under the EU AI Act; obligations depend in part on whether the bank develops the system or deploys a third-party system. |
A bank operating across jurisdictions should map the same service to each applicable regime instead of treating one framework as a global checklist. The relevant facts include the bank’s location, the customers affected, the provider’s location, the service’s function and whether the bank is developing, modifying or merely deploying the system.
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Questions to ask before signing with an AI provider
- What exact business process and customer outcome does the service affect?
- Who owns each control: the bank, the provider or a subcontractor?
- What data enters the system, where is it processed, how long is it retained and can it be used for training?
- What documentation and test evidence will the bank receive, and how quickly will it be updated?
- How can the bank audit the service or obtain equivalent independent assurance?
- What events trigger notification, suspension, revalidation or regulatory escalation?
- How will material changes be disclosed, tested and approved?
- Which subcontractors and infrastructure providers are involved, and how is concentration risk managed?
- What happens during an outage, degraded performance, cyber incident or provider insolvency?
- Can the bank retrieve data, logs, model outputs and decision records and move to a replacement process?
Answers should be specific to the proposed use. A supplier’s general security brochure or a promise that its product is “compliant” does not answer these operational questions.
Common failure modes and practical fixes
“The vendor is reputable, so diligence is complete.”
Why it fails: reputation does not establish that this model, data flow and customer impact are suitable. Fix: perform arrangement-specific diligence and document residual risk and approval conditions.
“The contract covers security, so AI risk is covered.”
Why it fails: ordinary security language may omit model changes, validation evidence, explainability, output monitoring and decision records. Fix: add AI- and service-specific information, audit, change, incident and exit provisions.
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Why it fails: data, models, threats, providers and business uses change. Fix: monitor performance and controls continuously enough for the risk, with defined triggers for reassessment.
“A supplier’s model cannot be validated because it is proprietary.”
Why it fails: lack of transparency is itself a validation and governance constraint. Fix: seek independent evidence, output and outcome testing, documentation, access for qualified reviewers and contractual remedies; do not deploy if residual uncertainty exceeds the bank’s tolerance.
“One global checklist will satisfy every regulator.”
Why it fails: US guidance, NIST resources, DORA, EBA guidance and EU AI Act obligations have different legal status and scopes. Fix: perform a jurisdiction-and-use-case mapping before making compliance claims.
Who is responsible when a third-party AI model causes harm?
The bank remains accountable for its regulated activities and must manage the provider relationship, even where the supplier caused the technical failure. Responsibility may be allocated contractually between the parties, and a provider may face its own legal duties, but a bank cannot defend inadequate oversight by saying that an outside system produced the result. The practical response is to maintain documented approvals, human and operational controls, monitoring, incident escalation and a tested fallback that can protect customers while the cause is investigated.
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