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Third-party AI risk management is how a bank identifies, assesses, controls, and continually monitors risks arising when an outside organization supplies, builds, hosts, operates, or supports artificial intelligence used in the bank’s work. The bank remains accountable for safe, lawful operations even when a vendor performs the technical task.
The discipline combines ordinary third-party relationship management with model risk management, information security, privacy, operational resilience, consumer protection, and AI governance. The right controls depend on the use case, the provider’s role, the data involved, the effect on customers, and the bank’s jurisdiction.
What third-party AI risk management covers
A third-party relationship exists whenever an external organization contributes materially to an AI capability. That can include a hosted machine-learning API, a vendor-built credit-scoring model, a cloud service running a bank’s own model, outsourced data preparation, a generative-AI assistant used by employees, or a managed service that monitors and updates an AI system.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteRisk management follows the entire relationship rather than just the purchase decision. The bank should understand the business purpose, the system’s boundaries, the information it receives and produces, the people affected by its output, and the dependencies that could interrupt the bank’s operations. It should then set controls proportionate to the activity’s risk, complexity, and criticality.
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The bank’s responsibility does not move to the vendor
The US Federal Reserve, FDIC, and OCC state 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, outsourcing development or hosting does not outsource accountability for fair treatment of customers, accurate decisions, secure data, resilient services, or regulatory compliance.
Jurisdiction and legal status matter
There is no single worldwide rule called third-party AI risk management. A bank must identify its jurisdiction, its role (developer, deployer, or both), the service being outsourced, and the use case before treating any guidance as a legal requirement.
United States
The 2023 US interagency guidance supplies risk-management principles for supervised banking organizations. It uses a lifecycle of planning, due diligence and selection, contracting, ongoing monitoring, and termination. It says criticality is institution-specific: an activity that is critical for one bank may not be critical for another. The Federal Reserve’s May 8, 2024 CA 24-2 letter explains that the guidance does not impose new requirements on banking organizations; it describes supervisory expectations and sound practices.
NIST’s AI Risk Management Framework and its Generative AI Profile are voluntary resources. A bank can use them to organize work on validity, reliability, safety, security, privacy, transparency, accountability, and human oversight, but citing NIST alone does not establish compliance with a banking rule.
European Union
The Digital Operational Resilience Act (DORA), Regulation (EU) 2022/2554, has applied since January 17, 2025. For covered financial entities, it establishes ICT-risk, resilience, incident, testing, and oversight requirements that affect relationships with technology providers.
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The European Banking Authority’s third-party-risk guidance addresses the lifecycle of ICT and non-ICT arrangements, with particular attention to services supporting critical or important functions. A bank should map those functions, dependencies, concentration risks, access rights, and exit arrangements rather than assuming every vendor receives identical treatment.
The EBA’s analysis updated November 20, 2025, explains that AI used to assess the creditworthiness or establish the credit score of natural persons in banking and payments is classified as high-risk under the EU AI Act. Duties vary according to whether the financial institution develops the system, deploys a third-party system, or performs both roles. The classification of a specific arrangement requires a fact-specific legal assessment.
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A five-stage lifecycle for managing an AI provider
1. Plan and inventory the arrangement
Start with a complete inventory, not a list limited to formally purchased “AI products.” Record each system and service, its owner, business purpose, model or model family, provider, hosting location, data flows, interfaces, subcontractors, update process, and users.
- Describe the decision or task the AI supports and what happens when it is unavailable.
- Identify customers, employees, counterparties, or communities that could be affected by an output.
- Mark whether the system handles personal, confidential, payment, biometric, or regulated information.
- Map upstream data, foundation models, cloud infrastructure, human review, and downstream automated actions.
- Classify criticality using the bank’s own risk appetite and business-impact analysis.
Inventory records should distinguish an AI model supplied by a vendor from ordinary software that merely contains an automated rule. The distinction determines which validation, explainability, and change controls are needed.
2. Assess and select the provider
Due diligence should test the particular arrangement. A provider’s reputation, prior engagement, or generic certification is not a substitute for evidence about this service, this data, and this use case.
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- Review corporate governance, financial capacity, staffing, relevant experience, and use of subcontractors.
- Examine development and testing practices, training-data provenance, performance measures, bias testing, security controls, and privacy practices.
- Determine how the provider identifies model drift, harmful content, data leakage, prompt attacks, and unauthorized use.
- Request independent assurance reports, penetration-test summaries, validation documentation, incident history, and remediation records where appropriate.
- Check whether the provider can support the bank’s recordkeeping, regulatory reporting, customer notices, and audit needs.
For a high-impact decision, the bank should be able to explain why the provider’s evidence is sufficient, what gaps remain, who accepted them, and what compensating controls apply.
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3. Contract for oversight and control
The agreement should reflect the risk classification. Contract language cannot make a prohibited activity acceptable, but it can preserve the bank’s ability to supervise the service.
- Define the service, permitted uses, performance measures, security requirements, and data ownership or usage limits.
- Require timely notice and escalation of security incidents, material errors, outages, regulatory inquiries, and suspected data loss.
- Set information, audit, testing, and access rights that are workable for the bank and its supervisors.
- Control subcontracting, material changes, model retraining, version upgrades, geographic processing, and changes to terms.
- Specify retention, deletion, return of data, cooperation with investigations, and support for customer or regulator requests.
- Set termination, transition, portability, and orderly wind-down obligations, including assistance with replacement or internal operation.
Rights that exist only on paper are weak controls. The bank should verify that the provider can actually deliver requested logs, test results, notices, and transition support.
4. Monitor throughout the relationship
Approval is the beginning of oversight, not its end. Monitoring should combine provider evidence with the bank’s own operational and outcome data.
- Track service availability, latency, error rates, decision outcomes, complaints, overrides, and exceptions.
- Watch for model drift, degraded accuracy, disparate outcomes, unsafe generated content, and unexplained changes in output.
- Monitor incidents, vulnerabilities, access violations, data loss, subcontractor changes, concentration exposure, and financial or organizational instability.
- Reconcile invoices and service-level reports with what the bank actually received.
- Increase review frequency when the use case becomes more critical, the provider changes the system, or controls deteriorate.
Document findings, management responses, due dates, and escalation. A missed service level or unexplained model change should trigger investigation rather than automatic renewal.
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5. Review and exit
At defined intervals and after material events, reassess whether the arrangement still fits the bank’s risk appetite and legal obligations. Maintain a practical exit plan before a crisis occurs.
- Identify replacement providers, an internal fallback, or a manual process for essential decisions.
- Test data and model portability, credentials, interfaces, documentation, and operational runbooks.
- Confirm that records needed for customer disputes, audits, validation, and regulatory examinations remain accessible.
- Revoke access, recover or delete data, and verify deletion when the relationship ends.
Questions for comparing AI providers
These questions turn the lifecycle into a usable comparison. They are practical risk-management prompts, not a universal regulator-mandated checklist.
| Area | Questions to ask | Evidence to request |
|---|---|---|
| Use case and impact | What decision or process does the system support? Who can be harmed by an error? Is human review required? | Use-case description, impact assessment, decision workflow, escalation rules |
| Provider role and dependencies | Does the provider develop, host, operate, tune, or merely supply data? Which cloud, model, and subcontractor dependencies exist? | Architecture and dependency maps, subcontractor register, responsibility matrix |
| Data and confidentiality | What data enters prompts, training, logs, and support channels? Is customer data reused or transferred across borders? | Data-flow diagrams, retention terms, privacy controls, deletion attestations |
| Model evidence | How were accuracy, robustness, bias, security, and limitations tested? Can the bank reproduce or independently validate results? | Validation reports, test sets, performance by relevant segments, known-limitations register |
| Change management | What triggers notice, approval, regression testing, or rollback when a model, data source, or component changes? | Version history, release policy, rollback procedure, change notices |
| Resilience and incidents | What are recovery objectives, support hours, outage procedures, and notification deadlines? | Business-continuity plans, test results, incident playbooks, service reports |
| Audit and concentration | Can the bank and supervisors obtain information and test controls? Does reliance on this provider create concentration risk? | Audit clauses, assurance reports, concentration analysis, access procedures |
| Exit | How quickly can the bank move data, configurations, prompts, and models to another service or manual process? | Portability format, transition plan, termination assistance terms, exit-test results |
How third-party risk overlaps with other disciplines
Third-party risk management versus model risk management
Third-party risk management centers on the relationship, provider, service, and activity. Model risk management centers on risks from a model’s development, inputs, implementation, use, and validation. 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. It applies its principles to traditional statistical and quantitative models and to non-generative, non-agentic AI models. That stated scope should not be treated as resolving every question about generative or agentic AI. A bank should have model-risk staff assess validation, conceptual soundness, outcomes analysis, and limitations while third-party-risk staff assess the commercial and operational relationship.
AI governance
AI governance addresses intended use, data quality, human oversight, explainability, performance, accountability, and effects on people. A bank can use NIST’s AI RMF and Generative AI Profile to structure those questions alongside applicable supervisory, privacy, consumer-protection, and resilience requirements. A framework does not replace legal analysis, risk acceptance, or independent validation.
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Proportional controls for common scenarios
Employee productivity assistant
An internal drafting assistant may be lower criticality than a lending model, but confidential information, inaccurate advice, prompt injection, and retention still require controls. Restrict data entry, define approved uses, log access, require human review, and prohibit the assistant from making unsupervised customer decisions.
Fraud or transaction monitoring
False positives can deny legitimate transactions, while false negatives can increase losses. Establish performance thresholds, review outcomes across relevant customer groups, preserve an analyst override, and maintain a tested fallback for outages or degraded model quality.
Creditworthiness or credit scoring
This is a high-impact use in the EU when it assesses the creditworthiness or establishes the credit score of natural persons in banking and payments. The bank must determine whether it is developing or deploying the system, identify applicable EU AI Act duties, validate the model, document data and decisions, and provide the human and customer safeguards required by the applicable framework.
Customer-facing generative AI
Set boundaries for claims, disclosures, escalation, accessibility, and records. Test for hallucinations, prompt manipulation, unauthorized disclosure, and inconsistent treatment. Keep a human route for complaints and consequential requests.
Common failure modes and corrective actions
- “The vendor is certified, so approval is automatic.” Certifications may cover a defined control scope and period. Map that scope to the bank’s actual service and obtain evidence for gaps.
- “We completed onboarding, so monitoring is finished.” Establish recurring reviews and event-driven reassessment for incidents, releases, outages, and deteriorating outcomes.
- “The contract grants audit rights, so we are covered.” Test whether logs, personnel, environments, and subcontractors are reachable within the required time.
- “It is only an AI tool, not a model.” Determine whether it influences a regulated decision, processes sensitive data, or creates material operational dependency; classify it on effects, not marketing labels.
- “One control set fits every bank.” Criticality is institution- and activity-specific. Scale diligence, approval, monitoring, and exit testing to the actual exposure.
- “We can leave later.” Without tested portability and a fallback process, termination may be theoretical. Exercise the exit plan for critical services.
Who is responsible when a bank uses a third-party AI model?
The bank’s board and senior management remain responsible for governance, risk appetite, resources, and oversight. Business owners are responsible for the use case and outcomes. Procurement and third-party-risk teams manage due diligence, contracting, monitoring, and exit. Information-security, privacy, legal, compliance, model-risk, validation, operational-resilience, and consumer-protection specialists provide control assessments within their mandates. The provider remains responsible for performing its contracted duties and supplying accurate, timely information, but its obligations do not displace the bank’s accountability.
The practical test is simple: if a supervisor, customer, or auditor asks why the AI was used, how it was controlled, or what happened when it failed, the bank must be able to answer with its own records and evidence—not merely forward the question to the vendor.
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