Comprehensive AI model governance and risk assessment templates for financial services teams.
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Built for risk and compliance teams at financial-services organizations
◆ Quick buying summary
◆ What's included
Use rights: customize for internal business use and use outputs with your auditors, customers, bank partners, and regulators. Do not resell or redistribute the template files.
◆ Preview
Guide Key Takeaway: the template assesses 12 distinct AI risk domains, each with regulatory implications
Guide page: the fintech AI use case map — credit, fraud/AML, KYC and AI agents tier High; assistive support and marketing Medium; internal no-PII tools Low
Guide page: the US regulatory landscape for AI in financial services, opening with the Treasury Financial Services AI Risk Management Framework (February 2026, 230 control objectives)
● Case file
These public cases illustrate different AI and algorithmic-risk issues. They are context for scenario discussion, not proof that this template would have prevented a particular incident or outcome.
David Heinemeier Hansson tweeted that Apple Card gave him a credit limit 20× higher than his wife's on a joint application. Steve Wozniak echoed it. NYDFS opened an investigation within days.
Why it mattersYou don't need to lose the case to lose the year. Bias testing, explainability artifacts, and adverse action procedures need to exist before the first complaint — not be reverse-engineered after one.
Hello Digit's algorithm decided when customers could "safely" transfer to savings — and caused thousands of overdrafts when it was wrong. The app then failed to honor its written promise to reimburse the fees.
Why it mattersAlgorithmic outcomes that diverge from your marketing are UDAAP violations. The failure was the gap between what marketing promised and what the model produced. That gap belongs in pre-launch model review, not post-incident remediation.
Federal class action alleges Workday's AI screening tool systemically rejected applicants on race, age, and disability. In July 2024, the N.D. Cal. court let claims proceed on the theory that Workday acted as an "agent" of the employers using its tool, and in May 2025 it preliminarily certified a nationwide age-discrimination collective.
Why it mattersThird-party AI vendors are an extension of your model risk. The vendor questionnaire, indemnity clauses, and ongoing audit rights you don't have yet are exactly what you'll wish you had when a class action drops.
If you're reading this trying to make sure your fintech doesn't end up on this list — you're in the right place. Here's what you'd recognize:
◆ Good fit if any of these sound familiar
Your CTO just deployed GPT in production without telling compliance.
You can't assess AI use you haven't identified. The discovery survey and Shadow AI Register give teams a structured place to document it.
Your regulator just asked how you're managing AI risk.
Use the inventory and assessment records to organize an accurate answer, with organization-specific evidence and review added before sharing.
Your bank partner sent an AI governance questionnaire — due in two weeks.
The inventory, assessment scorecard, and vendor questionnaire can help organize source information, but each response still requires review and organization-specific evidence.
◆ Why now
The included materials reference current AI risk frameworks and selected legal and regulatory developments, including NIST AI RMF, SR 26-2 model-risk guidance, the Treasury Financial Services AI Risk Management Framework, Colorado SB 26-189, ECOA considerations, and the EU AI Act where applicable. Applicability and effective dates vary by organization and use case, so confirm current primary sources and obtain legal or compliance review before relying on the materials.
◆ Where this fits
◆ What this isn't
◆ 30-day rollout plan
Use this four-week sequence as a starting point for workshops, ownership, review, and leadership reporting. Actual timing depends on scope and stakeholder availability.
Run an AI Inventory Discovery workshop with engineering, product, ML, ops, and support leads. Populate Tab 1 with every AI/ML tool in production, development, and pilot. Frame it to teams as visibility, not restriction — bank partners and regulators need it documented.
Confirm the auto-tier for each use case (business function, consumer decisioning role, PII, autonomy). For each High-tier use case — including every AI agent — run a 90-minute workshop to complete the 52-question Risk Assessment Scorecard.
Send the 36-question Third-Party AI Due Diligence questionnaire to every AI vendor in the inventory. Run an org-wide amnesty survey to surface Shadow AI (the ChatGPT and Copilot usage nobody told you about) and log it in the Shadow AI Register.
Review the AI Governance Dashboard, draft the quarterly Board Report, and build your testing calendar from the Scenario Library. Present to risk committee or leadership: High-tier list, open red flags, 90-day remediation plan.
◆ Full playbook in the PDF guide
The complete rollout plan — including who to invite to each workshop, the messaging to give teams, and what each meeting's deliverable looks like — is in the PDF guide you get with the template.
◆ Regulatory alignment
The guide includes references intended to help reviewers trace relevant prompts. Confirm applicability, effective dates, and current primary-source text before relying on them.
Built for financial-services risk and compliance teams; organization-specific review and evidence are still required.
Last updated: September 30, 2026
◆ Template guide
How to build an AI risk assessment template for financial services: model inventory fields, risk scoring, vendor due diligence, and governance evidence.
◆ FAQ
Each model entry captures model name and type, use case, risk tier (High/Medium/Low), development source (in-house vs. vendor), potentially applicable frameworks and laws, assessment status, owner, and last review date. Completion time depends on the number of use cases and the information available.
A 52-question scorecard: 4 questions in each of 11 core domains — model risk, bias and fairness, explainability and transparency, data privacy and security, third-party and vendor AI, cybersecurity and adversarial risk, operational risk, regulatory and legal, consumer protection, reputational and ethical, and intellectual property — plus 8 Agentic AI questions for agents that take actions (tool permissions, hard limits, human approval, action logging, kill switch, indirect prompt injection, agent identity and credentials, and third-party connectors). Each question is rated 1–4 or N/A with space for evidence; domain averages and red flags calculate automatically, and the overall rating is the worst domain rating, lifted to at least High by any red flag (it shows Incomplete until every question is answered), followed by an assessor and reviewer approval block.
It's a structured questionnaire you can tailor and send to an AI vendor before onboarding, with 36 questions in 8 categories: model transparency, bias and fairness, data handling and privacy, security and resilience, governance, performance monitoring, subprocessors, and agents, tools and connectors. You rate each answer; the vendor rating stays Incomplete until every item is rated (at least 10 other than N/A) and goes High if a critical item (such as training on your data, audit access, agent limits and kill switch, or agent logging) is Unsatisfactory. Your legal, security, and compliance teams should adjust it for the vendor, use case, contracts, and applicable laws.
The materials were updated in September 2026. They reference SR 26-2 / OCC Bulletin 2026-13, which replaced SR 11-7 in April 2026, is most relevant to banks over $30 billion in assets, and leaves generative and agentic AI out of scope; the CFPB's Reg B amendments effective July 21, 2026, which state that ECOA does not provide for the disparate-impact effects test but did not change adverse action notice requirements; Colorado SB 26-189, which repealed and replaced the Colorado AI Act for decisions made on or after January 1, 2027; Texas TRAIGA (effective January 1, 2026); California's CCPA automated-decision rules (from January 1, 2027); the December 11, 2025 executive order (EO 14365) directing a federal challenge to state AI laws; and the EU AI Act only where it applies. Confirm current primary sources before relying on any reference.
Bias and Fairness is one of the 12 scored domains, covering disparate impact testing, ongoing monitoring, training data representativeness, and adverse action reasons. The guide walks through a proxy-discrimination credit example, the Scenario Library maps use cases to bias tests and cadences, and the Bank Partner Response Library shows how a four-fifths-rule testing program is typically described to a partner bank. It is a governance framework, not a statistical testing tool; your data team or vendor still runs the tests.
Yes. The inventory has an AI Agent type and an autonomous-agent autonomy level, and either one tiers the use case High. A dedicated Agentic AI domain adds eight questions on tool permissions, hard limits, human approval for high-impact actions, action logging and replay, a kill switch, indirect prompt injection, agent identity and payment credentials, and vetting of third-party connectors. The vendor questionnaire asks about agents inside vendor products, the Scenario Library adds three agent tests, and a worked example assesses a customer-servicing agent that reverses fees and schedules payments. OCC Bulletin 2026-13 / SR 26-2 leaves agentic AI out of scope, so these controls fill that gap.
Yes — a large portion of the kit is designed specifically for vendor AI, including the third-party questionnaire, vendor risk tiering criteria, and TPRM integration guidance. The model inventory covers both in-house models and vendor-supplied AI tools.
Yes. You can use completed outputs with auditors, customers, bank partners, regulators, and internal stakeholders. Customize the template for internal business use — just don't resell or redistribute the source template files.
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141 pre-populated fintech risks across 21 categories. ISO 31000 structure.
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