Third-Party AI Risk Assessment Questionnaire for Fintechs

Why fintechs need an AI-specific questionnaire

Fintech teams assessing AI vendors, embedded AI features, and third-party AI agents need due diligence that reflects how AI systems behave in production. Standard vendor security reviews remain important, but they do not fully address model behavior, retrieved context, agent permissions, runtime policy enforcement, tool calls, human approvals, or evidence needed for investigation and oversight.

An effective questionnaire should help reviewers identify the AI systems involved, understand what data they process, determine what actions they can take, and assess whether the vendor can provide records that support auditability, governance, and supervisory review.

Core domains for the questionnaire

1. AI system inventory and use case scope

Ask the vendor to identify every model, AI agent, retrieval source, plugin, API, tool, database, subcontractor, and downstream system used to deliver the service. Require the vendor to distinguish model-only functions, embedded AI features, and autonomous or semi-autonomous agents that can take actions.

2. Governance and accountability

Assess who owns AI risk, how the vendor approves new AI capabilities, how changes are reviewed, and how third parties or subcontractors are governed. Request AI risk assessments, governance policies, system documentation, model cards or equivalent summaries, and evidence of review for material changes.

3. Data governance and privacy

Determine what data is collected, retained, transformed, embedded, logged, used for fine-tuning, or shared with subprocessors. Confirm whether tenant isolation, encryption, key management, retention, deletion, and access controls apply consistently to prompts, generated outputs, retrieved context, embeddings, logs, and training-related data.

4. Model evaluation and behavior controls

Ask how the vendor evaluates validity, reliability, safety, robustness, explainability, interpretability, privacy, and fairness where relevant. For regulated or customer-impacting uses, require evidence that the system supports human review, contestability, documentation, and records suitable for audit or supervisory review.

5. Runtime governance and security

Evaluate how policies are enforced while the AI system is operating. The questionnaire should cover agent identity, authentication, authorization, least-privilege permissions, scoped tool access, retrieval controls, output controls, tool-call approvals, and restrictions on transaction-impacting actions.

6. Monitoring, incident response, and change management

Request logging schemas, sample audit records, monitoring procedures, incident response processes for AI behavior, security testing results, and notification commitments for model changes, new data sources, expanded tool access, security incidents, and critical subcontractor changes.

Runtime governance questions for AI agents

For vendors that provide autonomous or semi-autonomous AI agents, the questionnaire should test whether the vendor can explain and evidence controls at the point of action, not only during design or procurement review.

1. 1

   Agent identity

   Does each AI agent have a distinct identity, or does it operate under broad shared application credentials? Can the vendor show which agent performed a specific action?

2. 2

   Least-privilege permissions

   Are permissions scoped by user, tenant, role, data object, tool, environment, and action type? How are permissions approved, reviewed, revoked, and restricted after role or use case changes?

3. 3

   Tool-call governance

   Which tools, APIs, plugins, databases, and downstream systems can the AI system call? Are high-impact actions blocked, rate-limited, approval-gated, or restricted by policy before execution?

4. 4

   Policy enforcement

   Are policies enforced before retrieval, model invocation, tool execution, data export, and workflow action? Are controls applied at runtime, or only during configuration and periodic review?

5. 5

   Audit logging

   Do logs capture prompts, retrieved context, model outputs, tool calls, permission decisions, human approvals, errors, overrides, administrative changes, and timestamps sufficient for investigation?

6. 6

   Excessive agency testing

   Has the vendor tested whether the agent can act beyond intended scope, chain tools in unexpected ways, expose sensitive information, or continue actions without appropriate human approval?

7. 7

   How to use responses for tiering and decisions

   Use the responses to determine the level of review, approval, monitoring, and ongoing evidence required for the vendor and the specific AI use case.

Vendor evidence to request

The questionnaire should ask for concrete evidence rather than only narrative responses. Evidence should be specific enough to support approval decisions, ongoing oversight, and later investigation when needed.

• AI risk assessments, governance policies, system documentation, and model cards or equivalent summaries.
• Evidence of review for material changes to models, data sources, tools, permissions, or subcontractors.
• Documentation for prompts, generated outputs, retrieved context, embeddings, logs, and training-related data handling.
• Logging schemas and sample audit records that show prompts, retrieved context, model outputs, tool calls, permission decisions, approvals, errors, overrides, administrative changes, and timestamps.
• Monitoring procedures and incident response processes for AI behavior, security incidents, expanded tool access, and critical subcontractor changes.
• Security testing results and evidence of excessive agency testing for AI agents that can call tools or take workflow actions.

Review matrix for fintech due diligence

This matrix organizes the supplied questionnaire areas into a practical review format. It can be used during onboarding, reassessment, or change review.

Regulatory and governance considerations for fintech

For regulated or customer-impacting uses, the questionnaire should require evidence that the system supports human review, contestability, documentation, and records suitable for audit or supervisory review. This makes the review more useful than a one-time onboarding check, because it connects vendor answers to ongoing governance, incident response, and change management.

Fintech teams should use the questionnaire to understand how AI systems act in production, what they can access, what actions they can take, how those actions are controlled, and what evidence is available for review.