AI Compliance for Financial Institutions
No complete inventory of AI systems, models, vendors, and use cases
Vendor AI features released or updated after procurement
Shadow AI adopted outside formal review
Manual approval workflows with limited audit trail
Inconsistent risk and obligation mapping
Limited evidence for regulators, auditors, boards, and executives
Difficulty monitoring AI risk after initial review
“
Mani Massoomi
Chief Risk Officer, Mercury
01
What AI compliance means for regulated financial institutions
02
Which financial services use cases carry higher AI risk
03
How the EU AI Act, NIST AI RMF, ISO 42001, and emerging US state laws are shaping expectations
04
Why AI compliance requires continuous oversight as systems and vendors change
05
How to build a governance program across inventory, obligations, ownership, and monitoring
06
How to move from manual tracking to defensible, audit-ready governance
Step 1
Inventory AI systems
Document every AI system, model, vendor capability, use case, owner, data source, and risk level.
Step 2
Map obligations and controls
Connect systems and use cases to applicable regulations, internal policies, controls, and evidence requirements.
Step 3
Establish governance workflows
Define ownership, approvals, review paths, escalation, and documentation across risk, compliance, technology, legal, AI, and the business.
Step 4
Monitor continuously
Track vendor AI changes, model updates, risk changes, incidents, and evidence over time.
Run it day to day
Inventory → Obligations → Approvals
Monitoring → Evidence → Reporting
Downloadable guide
Get a practical blueprint for building evidence-rich oversight across AI systems, vendors, models, use cases, controls, workflows, monitoring, and reporting.
