Trustible for Financial Services
AI governance for financial services

Financial services AI moves fast. Your examiners move methodically.

Banks, asset managers, and insurers are deploying AI in credit decisioning, fraud detection, and customer interactions faster than the governance behind it — and the OCC, Federal Reserve, and CFPB are examining AI programs with new rigor. Trustible gives financial services teams the structured intake, risk assessment, and audit-ready documentation that SR 11-7, the EU AI Act, and NIST AI RMF actually require.

<30%
of deployed AI models at financial institutions have documented risk assessments that would withstand examiner scrutiny. The governance gap isn’t a future problem — examiners are already asking for model inventories, validation evidence, and bias-testing documentation that most institutions can’t produce on demand.
The challenge

Financial services AI governance is uniquely exposed

SR 11-7 wasn’t written for AI

Guidance written for regression hasn’t kept pace with a portfolio of systems that behave nothing like a statistical model — leaving model risk teams applying the wrong framework.

Explainability isn’t optional

Credit decisioning and fraud models drive adverse action notices that require specific, defensible explanations. A model your team can’t fully explain can’t satisfy that obligation.

Fair lending exposure is growing

Models trained on historical data can encode and amplify discriminatory patterns invisible until a regulator runs a disparate-impact analysis. Most programs don’t catch it before examination.

Third-party AI is your fastest-growing risk

Vendors embed AI into origination, servicing, and fraud tools. As the deployer, you’re accountable — even when the vendor won’t share model details.

Most programs aren’t examination-ready

When an OCC or Fed examiner asks for your inventory, validation evidence, and governance history, producing it in hours rather than weeks is the difference between an MRA and an MRIA.

How Trustible helps

Governance built for examiner scrutiny

Examiner-ready context on every model

Structured intake captures what examiners require for every use case — model purpose, data inputs, affected populations, third-party dependencies, and human oversight level — building an SR 11-7-aligned record the moment a model enters the pipeline.

Use case — A model risk team auto-generates the model inventory documentation for its annual model risk report, replacing a manual process stitched together from spreadsheets across business units.
Deterministic scoring and a centralized register

A rules engine scores risk across Performance, Data Privacy, Cybersecurity, Ethical, and Legal categories, tiering high-risk models for proportionally deeper validation, with inherent and residual risk in one register.

Use case — A model risk committee tracks inherent and residual risk across the portfolio, with automated alerts when a model’s profile changes and mitigation evidence linked to each record.
Documented ongoing oversight

Periodic review workflows and owner attestations create the evidence of continuing oversight SR 11-7 expects — scheduled reassessments, structured check-ins, and automatic re-governance when material model changes occur.

Use case — A compliance team produces a complete review history for every active model on demand for its federal examiner, without manual reconstruction.
Map once, satisfy every framework

Governance activity maps simultaneously to SR 11-7, the EU AI Act (high-risk for credit scoring and fraud detection), and NIST AI RMF — so firms under multiple regimes document once and produce framework-specific evidence on demand.

Use case — A firm with EU operations prepares for EU AI Act high-risk obligations while maintaining NIST AI RMF alignment for US submissions — from a single set of records.
Customer story

"The scale of our organization requires a complementary scale in AI governance. Trustible gave us the structure to approve AI at the pace the business demands without creating regulatory exposure."

Geoff Schaefer, Chief AI Officer · Leidos · 47,000 employees · Defense & regulated sectors
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Regulatory & framework coverage

Frameworks that govern financial services AI

SR 11-7 model risk management
Trustible operationalizes the Federal Reserve / OCC guidance for AI: model inventorying, risk tiering by materiality, validation evidence, and ongoing oversight workflows that satisfy examiner expectations.
EU AI Act
For firms with EU operations, Trustible maps use cases to high-risk classifications (credit scoring, fraud detection, insurance underwriting), generates Annex IV technical documentation, and maintains the post-market monitoring evidence Article 72 requires.
NIST AI RMF
All four functions — GOVERN, MAP, MEASURE, MANAGE — connected to policy management, AI inventory, structured risk assessment, and ongoing oversight for organizations using the AI RMF as their foundation.
OCC model risk guidance
Support for OCC expectations: model inventory documentation, tiered governance by complexity and impact, and evidence of ongoing validation and monitoring activities.
Recommended

Where financial services teams go next

AI Risk & Impact Management
Deterministic risk scoring and impact assessment for consequential financial models.
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AI Compliance & Audit
Map SR 11-7, EU AI Act, and NIST evidence from one set of records.
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Centralized AI Inventory
The model inventory examiners ask for, kept current automatically.
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Your next AI examination is coming. Be ready.

Trustible gives financial services governance teams audit-ready AI documentation built from real governance activity — not assembled under pressure.