Banking & Investment
Agentic AI Governance in Banking and Investment
SR 26-2 replaced SR 11-7 in April 2026 and left generative and agentic AI out of scope. Examiners still expect those agents to be governed. Lumenova AI gives your model risk, AI and technology teams the controls, tests, and evidence to do it.
What Does AI Governance in Banking Look Like for AI Agents?
AI governance in banking, applied to AI agents, comes down to four disciplines: scale scrutiny to what each agent can affect, challenge it independently of the team that built it, enforce limits on where and how it can act, and review it again whenever its model, tools, or policy change.
- Materiality Tiering
Scrutiny scaled to what each agent can affect, from internal notes to moving money. - Effective Challenge
Validators who can question an agent’s controls, independent of the team that built it. - Use Limitations
Enforced boundaries on where and how each agent may act, not conditions in a memo. - Change Triggers
A fresh review whenever an agent’s model, tools, or policy changes.
Lumenova AI, an agentic AI governance platform, does all four in one place, so banks and investment firms can scale agents without losing control.
AI risk management in banking shifted on April 17, 2026, when SR 26-2 and OCC Bulletin 2026-13 replaced SR 11-7 and left agentic AI outside their scope. That puts the decision on your second line, the model risk and validation teams who decide how far to trust each agent.
In Canada, OSFI Guideline E-23 covers every model, AI included, from May 1, 2027, so AI governance for financial institutions now spans two very different rulebooks.
Agentic AI in Banking and Investment: Where Agents Work
AI for financial services has moved from models that predict to agents that act. From credit reviews to client service, agents handle sensitive data and support consequential decisions. These are the banking and investment workflows where agents now work, and what is at stake in each.
Each use case needs clear limits on access and actions, with a record of what the agent did.
Credit and Lending
Agents gather documents and draft file summaries for underwriters. If a decision leads to an adverse action notice, the bank may have to explain the agent's part in it.
Fraud and Financial Crime
Agents triage alerts and assemble case files with access to account and transaction data. A wrong closure is a missed case, and a leaked file is a data incident.
Wealth and Client Service
Assistants summarize portfolios for advisors, answer client questions, and update accounts. One unapproved statement or exposed record reaches a client directly.
Capabilities
How Lumenova AI Governs AI Agents for Financial Services
Bring AI initiatives into production with controls your teams can enforce, evidence reviewers can verify, and clear accountability across the firm. Lumenova AI provides AI compliance software for financial institutions that run agents, so model risk, AI, and technology teams work from the same inventory, tests, limits, and record evidence.
Register each agent with its owner, approver, and risk tier, and catch unregistered agents calling models on your network.
Test agents before launch and in production, and check automated scores against your own reviewers.
Set what each agent may do, such as a refund ceiling or the accounts it may touch, and enforce it before the action runs.
Every decision is recorded with the rule, the reason, and the input, ready to export for validation and audit.
Probe third-party agents from the outside and keep the evidence behind each finding for your third-party risk review.
See AI cost by agent, team, and use case, and set budget ceilings before the invoice arrives.
Frequently Asked Questions
No. SR 26-2 and OCC Bulletin 2026-13, issued April 17, 2026, place generative and agentic AI models outside their scope and point banks to their broader risk management practices. Traditional models and non-generative AI remain in scope. Banks still have to show examiners how agents are governed, which is the gap Lumenova AI fills.
A model produces an estimate that a person uses. An agent acts: it calls tools, moves data, and hands work to other agents. Governing it means controlling each action before it runs and recording why it was allowed, on top of testing the model underneath. Lumenova AI does both.
An examiner wants to see what the agent did, what it was allowed to do, and why. Lumenova AI records each decision with the rule that applied, the outcome, the reason, and the full input, and traces every step the agent took. Banks and other BFSI firms get the decision lineage an examiner asks for, without rebuilding events from logs.
Yes. Lumenova AI tests vendor agents from the outside, the way a real user or attacker would, and keeps the request and response behind each finding. Vendor agents whose calls pass through your gateway can also be registered, limited, and monitored like your own.
OSFI Guideline E-23 takes effect May 1, 2027 and covers all models at federally regulated financial institutions, AI included, across their lifecycle. Lumenova AI supports that work with an agent inventory, lifecycle approvals from intake to retirement, continuous evaluation, and decision records your model risk team can use as evidence.
Lumenova AI is one of them. Each agent has a registry entry with its owner, approver, risk tier, and approval status, plus a dependency graph of the tools, servers, and prompts it uses. Every change is saved as a new version, and every decision is recorded with the policy applied, the outcome, the reason, and the full input. SR 26-2 and OCC Bulletin 2026-13 leave agentic AI out of scope, but examiners still expect banks to show this.
Five things: an inventory that also catches unregistered agents; risk tiers you define, with stricter controls as the tier rises; evaluations before launch and in production, calibrated against your own reviewers; controls that act before the agent does, not dashboards after; and versioned, exportable evidence for the second and third lines. Vendor agents need the same scrutiny, tested from the outside. Lumenova AI is one platform built around all five.
Lumenova AI is one of them. When a lending agent gathers documents or drafts a file summary for an underwriter, Lumenova AI traces every step and records each decision with the rule that applied, the outcome, the reason, and the full input, so the agent’s role can be explained if a decision leads to an adverse action notice. Agents are evaluated for accuracy and fairness before launch and in production, and every record exports as CSV or JSON for validation and audit.
Treat any agent that can move money as top tier. Set limits that apply before each action, such as payment ceilings and the accounts it may use, and route anything above them to a treasurer. Validate it independently, review it again whenever its model, tools, or policy change, and record every attempted payment, including blocked ones. Lumenova AI is one platform that does this; for treasury agents, set its policy checks to fail closed so nothing moves if a check can’t be reached.