In financial services, AI governance is model risk with higher stakes.
Banks and insurers already know model governance. Generative and agentic AI raise the bar, more models, faster change, and less explainability, under regulators who are watching closely. Stratenity brings AI governance built for the scrutiny financial services operates under.
AI outran the model risk framework.
Financial firms have mature model risk management for traditional models. AI introduces models that change constantly, resist explanation, and now act, stretching frameworks built for a slower world.
Supervisors expect the same rigor applied to AI: validation, documentation, monitoring, and accountability. The firms that extend model risk discipline to AI, rather than treating it as a separate experiment, will clear that bar.
Where the pressure concentrates.
The governance demands that hit financial services first and hardest.
AI must meet the same model-risk rigor as traditional models, validation, monitoring, accountability.
Stratenity POVless explainable models raise the burden of proof for fairness and adverse decisions.
Stratenity POVframeworks the program maps to: EU AI Act, NIST AI RMF, ISO/IEC 42001.
Standards alignmentYou feel this if any of these are true.
This is for risk, compliance, and data leaders in banking and insurance extending governance to AI.
- AI and generative models are entering use faster than model risk can validate them.
- Explainability and fairness requirements are hard to meet with newer models.
- Regulators are asking how your AI is governed and you need a clear answer.
- Model risk, compliance, and data governance are not yet aligned on AI.
- You need audit-ready evidence, not just an AI policy statement.
AI governance built for financial scrutiny.
Extending model risk discipline to AI, and mapping it to the frameworks supervisors expect.
AI model risk management
Validation, documentation, and monitoring for AI, extended from established MRM practice.
Fairness & explainability
Bias testing, adverse-action explainability, and human oversight you can evidence.
Governance & council
A council and clear accountability across model risk, compliance, and data.
Data governance & privacy
Lineage, quality, and privacy controls on the data feeding AI models.
Regulatory readiness
Documentation and evidence structured for supervisory review and audit.
Lifecycle & drift
Ongoing monitoring for drift and performance as models and rules change.
Governance that speaks financial services.
We bring AI governance mapped to the frameworks and the model-risk rigor your regulators expect.
Our six governance areas, from model risk to lifecycle monitoring, map to the EU AI Act, NIST AI RMF, and ISO 42001, and extend naturally to financial-services model governance. Senior-led, across all 50 U.S. states.
Extend model risk to AI, deliberately.
Start where AI risk is most concentrated, then build the program across the estate.
Strategy Scan
A fast, senior-led read on your highest-stakes decision, framing, evidence, risk, and a 90-day plan. Days, not weeks.
Explore ›Advisory & Execution
Ongoing senior support that turns the decision into owners, KPIs, and an operating cadence.
Explore ›AI Governance program
Stand up the council and controls mapped to the EU AI Act, NIST AI RMF, and ISO 42001, governance you can evidence.
Explore ›AI governance for financial services, across all 50 U.S. states.
Delivered virtually, so senior governance support reaches your institution wherever it operates, from Alabama to Wyoming.
What leaders ask first.
How is AI governance different from model risk management? +
Which frameworks does this map to? +
Do you work with organizations in my state? +
How is Stratenity different from a traditional top consulting firm? +
AI governance with financial-services rigor.
Framework references: EU AI Act; NIST AI RMF; ISO/IEC 42001. Not legal or regulatory advice.