Free AI Model Inventory Template for Banks and Credit Unions

Why model inventory is the first AI governance file

Banks and credit unions cannot govern AI systems they cannot see. A model inventory is the operating file that tells compliance, model risk, vendor management, IT, audit, and leadership which AI models exist, where they are used, who owns them, and what evidence supports their approval.

For community banks, the inventory should cover more than internally developed statistical models. It should include vendor-provided AI, embedded machine learning features, fraud and AML scoring tools, credit decision support, customer communication tools, analytics models, and material employee-use AI systems.

The practical goal is simple: if an examiner, auditor, or board committee asks which AI models are in use, the institution can produce a current, risk-tiered list with owners, status, and evidence links.

SR 11-7 and SR 26-2 inventory fields

SR 11-7 model risk management work made model inventory a core governance discipline. Current SR 26-2 and OCC Bulletin 2026-13 language makes that discipline more urgent for AI systems because AI exposure can sit inside vendor platforms, change through updates, or affect customers without being labeled as a traditional model.

A useful inventory should connect legacy model risk fields to current AI governance fields. That means retaining model owner, validation, monitoring, and use-case fields while adding vendor ownership, customer impact, AI system category, data sensitivity, change cadence, explainability limits, and remediation status.

• Model name, system ID, owner, and business purpose
• Internal, vendor-provided, embedded, or employee-use classification
• Risk tier, customer-impact flag, and materiality rating
• Validation status, monitoring cadence, and issue history
• Data inputs, sensitive data exposure, and output use
• SR 11-7 continuity and SR 26-2 evidence mapping

How to use the template

Start with a cross-functional sweep. Ask business lines, analytics, IT, information security, compliance, vendor management, operations, and internal audit to identify systems that score, classify, predict, recommend, generate, summarize, monitor, or automate decisions.

Next, risk-tier every entry. Customer-impacting, credit, fraud, AML, compliance monitoring, cybersecurity, regulatory reporting, and critical operations models should receive deeper review than low-risk internal productivity tools.

Finally, turn missing fields into remediation work. A blank validation date, unclear owner, missing vendor evidence, or undefined monitoring metric should become a tracked gap with an owner, due date, and governance reporting path.

AI model inventory template fields

These are the core columns a bank or credit union should capture before deciding validation depth, monitoring cadence, vendor follow-up, or board reporting status.

1. 1Model or AI system name
2. 2Business purpose and approved use
3. 3Business owner and model risk owner
4. 4Vendor, developer, or platform provider
5. 5Internal, vendor, embedded, or employee-use type
6. 6Risk tier and materiality rating
7. 7Customer-impact and consumer-compliance flag
8. 8Data inputs and sensitive data exposure
9. 9Output type and downstream decision use
10. 10Validation status and last review date
11. 11Monitoring metrics, thresholds, and cadence
12. 12Known limitations and prohibited uses
13. 13Change history and vendor release notes
14. 14Open issues, remediation owner, and due date
15. 15Evidence links for audit, board, or examiner review

FAQ

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