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USA · Banking
Agentic AI for banks in the USA
US banks face detailed expectations on model risk, fair lending, privacy and cyber security. We help banks and credit unions deploy AI agents that take on real operational work while staying inside those expectations, with explainable decisions and human approval where it matters.
Key facts
How can banks in the USA use AI agents safely?
Pick work with clear rules and a human decision at the end: BSA/AML alert research, dispute files, loan servicing checks and member service. Document each agent for model risk review before it goes live.
Updated October 2026
- Regulators
- Model risk, Consumer protection, Privacy and cyber, AML: see below
- Deployment options
- AWS, Azure and Google Cloud US regions, government clouds, or on-premises
- First agents we build
- BSA/AML alert research, Card and transfer disputes, Credit file preparation
- Controls on every agent
- Own identity and least-privilege access, human approval for material actions, full audit trail, prompt-injection testing
Where banks put agents to work first
BSA/AML alert research
Gather KYC, transaction history and public information for each alert and draft the narrative an investigator needs for a SAR decision.
Learn moreCard and transfer disputes
Collect evidence, track Regulation E and Regulation Z deadlines and draft the response for an analyst to approve.
Learn moreCredit file preparation
Assemble the file and draft a recommendation with specific reasons, so underwriters can decide and adverse action notices stay accurate.
Learn moreCommercial loan servicing
Read borrower financials, test covenants and flag breaches for the relationship manager.
Learn moreMember and customer service
Handle routine requests for banks and credit unions through approved systems, with a clean handover to staff.
Learn moreTreasury and cash operations
Reconcile accounts, forecast cash positions and propose sweeps for treasury approval.
Learn moreWho sets the rules in the USA
Model risk
Federal Reserve SR 11-7 and the matching OCC guidance on model risk management, which reach AI models that support decisions.
Consumer protection
The CFPB expects specific, accurate reasons for adverse credit decisions, including when AI is involved.
Privacy and cyber
GLBA safeguards, FFIEC guidance and, for New York-licensed firms, NYDFS Part 500.
AML
Bank Secrecy Act and FinCEN requirements for monitoring and reporting.
Read our NIST AI RMF guide for AI agents →
42technology partners
5markets: Middle East, Africa, USA, India, Canada
17AI, data and security services
6ready accelerators
6 hrsreply time on every enquiry
Three ways to begin, with clear timelines
Agentic Discovery Sprint
Prioritised use cases, architecture options and a costed plan your board can approve.
- Workflow and data assessment
- Risk and regulation map for the USA
- Business case and roadmap
Accelerator Pilot
A governed agent in production on your data, with security and audit built in.
- Agents live in your systems
- Human approval and audit trail
- Handover or managed AgentOps
Not sure where you stand? Take the 2-minute AI agent risk scorecard →
Banks we serve locally: New YorkDallas and HoustonSan Francisco Bay AreaWashington, DC
Frequently asked questions
Are AI agents subject to SR 11-7?
Agents that make or support material decisions usually are. We document purpose, data, testing and limits so model risk teams can validate them.
Can AI support credit decisions under CFPB rules?
Yes, but the lender must still give specific and accurate reasons for adverse actions. We design credit-related agents so every recommendation can be explained and a person makes the final call.
Where should a US bank start?
With work that agents prepare and people approve: fraud case drafting, KYC file preparation, servicing requests or reconciliation.
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Talk to our banking team about the USA
Get a free consultation from our AI, data and security specialists. Honest advice, clear options and a reply within 6 hours.
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