Managing Third-Party AI Risk Through Strong Vendor Oversight

Financial institutions do not always build or operate the AI capabilities they rely on. Third-party solutions can support fraud detection, credit decisioning, customer interactions, and other banking functions. But relying on an outside provider does not transfer responsibility for the outcomes those technologies produce.

As AI becomes more embedded in third-party technology, financial institutions need visibility into where these capabilities are being used, how they are monitored, and who is accountable for their performance.

Why Third-Party AI Requires Stronger Oversight

AI introduces risks that can evolve as models, data, and system behavior change. When those capabilities are embedded in vendor solutions, institutions may have less visibility into how decisions are generated or how models are updated.

This can make it more difficult to identify issues early, understand root causes, and ensure appropriate controls are in place.

Third-party AI risk can include:

• Model risk: Changes in model behavior can affect the consistency and reliability of decisions.
• Data risk: Incomplete, outdated, or unverified data can affect model performance and decision accuracy.
• Explainability risk: Institutions may need to explain AI-driven outcomes without having direct control over the underlying technology.
• Operational risk: System failures or control issues can affect multiple processes when AI is embedded across the organization.

These risks make vendor oversight an important component of an institution's broader AI governance strategy.

Bringing Vendor AI Into Existing Risk Management Frameworks

Third-party AI should not be treated as a separate category of risk. The white paper emphasizes that institutions should incorporate AI into existing model risk management and compliance frameworks, applying consistent standards for development rigor, validation, documentation, and ongoing monitoring.

A coordinated approach can help financial institutions establish:

• A centralized inventory of AI use cases to provide visibility into where third-party AI is deployed.
• Consistent validation and testing standards to evaluate performance and potential unintended impacts.
• Clear ownership and accountability across business, risk, compliance, and technology teams.
• Ongoing monitoring of model performance, data inputs, fairness, accuracy, and customer outcomes.
• Senior management and board-level visibility into how AI is being used and governed.

Maintaining Accountability for AI Outcomes

Effective vendor oversight extends beyond the initial implementation of an AI solution. Institutions need ongoing visibility as models and data evolve and should be prepared to identify and address issues as they emerge.

This is particularly important when third-party AI supports customer-facing activities or core risk functions. Without defined ownership and consistent oversight, issues can persist longer and become more difficult to resolve.

Strong third-party AI governance does not require financial institutions to build entirely new risk frameworks. Instead, it requires discipline in applying existing governance practices to AI wherever it is used, including through external providers.

Key Takeaways

• Financial institutions remain responsible for outcomes produced by third-party AI solutions.
• Vendor AI should be incorporated into existing model risk management and compliance frameworks.
• A centralized inventory provides visibility into where third-party AI is being used.
• Validation, documentation, and ongoing monitoring are essential to managing third-party AI risk.
• Clear ownership and senior-level oversight help institutions maintain accountability as AI use expands.

Download ARGO's white paper, AI in Banking: Aligning Innovation with Risk and Governance, to learn more about managing AI risk, regulatory expectations, and enterprise oversight.