Banking and Financial Services
How Agentic AI Works in Investment Banking Compliance
Regulators are not asking whether broker-dealers can use autonomous AI agents. They are asking how firms keep a human in control of every decision those agents touch.
Agentic AI in investment banking compliance uses autonomous, multi-step AI agents to draft trade surveillance alerts, screen KYC/AML data, and prepare regulatory filings, always under human review. FINRA's 2026 guidance on AI agents treats these systems as high-risk precisely because they can act without human validation, so firms keep a human approving every regulated decision.
regulatory context
How FINRA and the SEC Are Actually Treating Agentic AI Right Now
FINRA has been direct about the ground rule: its rules are, in its own words, intended to be technology neutral, and the securities laws more generally continue to apply when member firms use generative AI or similar technologies, whether built in house or licensed from a vendor. There is no separate rulebook for AI agents. Existing supervision, communications, recordkeeping, and fair-dealing obligations apply to an autonomous agent exactly as they would to any other tool a firm puts between an employee and a regulated decision.
What changed is the risk analysis, not the rulebook. FINRA's 2026 Regulatory Oversight Report, published December 9, 2025, named agentic AI as an emerging risk category distinct from general generative AI, and FINRA's own January 27, 2026 blog post on AI agents, written by Greg Ruppert, EVP and Chief Regulatory Operations Officer, defines AI agents as systems that can plan, make decisions, and take actions to achieve goals without predefined rules or logic programming, unlike a chatbot that only responds to a prompt. The SEC has moved in the same direction: AI is a stated focus of its 2026 Examination Priorities, released November 17, 2025, for the third year running, with examiners now asking less whether a firm uses AI and more how it supervises the tool once it is live.
risks
The Seven Risks FINRA Names for Autonomous Agents
FINRA's 2026 report and its AI-agents blog post lay out specific reasons an autonomous agent is a different supervisory problem than a generative AI chatbot. These are the risk categories a governed compliance deployment has to answer for, not abstractions:
1. Autonomy
An agent acting without human validation and approval, which is the core property that separates it from a tool that only drafts text for a person to send.
2. Scope and authority
An agent acting beyond the user's actual or intended scope, taking actions nobody explicitly authorized for that task.
3. Auditability and transparency
Complicated, multi-step agent reasoning that can make outcomes difficult to trace or explain after the fact, complicating both internal review and an examiner's request for a record.
4. Data sensitivity
An agent that unintentionally stores, explores, discloses, or misuses sensitive or proprietary information it was given access to complete a task.
5. Domain knowledge
A general-purpose agent lacking the tacit, industry-specific knowledge to consistently carry out a securities compliance task the way a trained analyst would.
6. Reward misalignment
A poorly designed reward or objective function that could negatively impact investors, firms, or markets even when the agent is technically doing what it was told.
7. Inherent generative AI risk
Bias, hallucination, and privacy risk, the same risks that apply to any generative AI tool, still present underneath the additional autonomy layer.
applications
Where Agentic AI Actually Shows Up in Compliance Workflows
Trade surveillance
An agent can triage large volumes of trading activity, chain together market data and communications context, and draft a surveillance alert or escalation summary for a human reviewer, rather than a person manually correlating every data source. The agent proposes; a named supervisor still decides whether the trade pattern gets escalated, closed, or referred.
Communications monitoring
Agentic tools can scan and classify email, chat, and voice communications for policy violations or insider-information risk across a volume no manual review team could cover alone, then route flagged items into a queue a compliance officer actually reviews and closes, keeping the recordkeeping and review obligations FINRA rules already require.
KYC and AML screening
An agent can pull and reconcile customer records, sanctions lists, and adverse-media data into a single case file and flag likely matches, but a compliance analyst still makes the actual determination on whether a match is genuine and what action follows, consistent with existing anti-money-laundering program requirements.
Regulatory reporting
Agents can assemble draft regulatory filings or exception reports from underlying transaction and account data, cutting the manual data-gathering step, while a named individual remains accountable for reviewing and certifying what actually gets filed.
requirements
What a Governed Agentic Deployment Requires
1. An agent inventory tied to a named owner
Every agent in production compliance use documented with its intended scope, the data it can access, and who is accountable for its output, the same discipline FINRA Rule 3110 already requires for a firm's broader supervisory system.
2. A human validation gate before any regulated action
The agent proposes; a named person with the authority to do so approves. FINRA's own framing of autonomy risk, acting without human validation and approval, is the exact gap this gate closes.
3. An audit trail built for the agent's own reasoning
Logging that captures not just the agent's final output but enough of the intermediate steps that a supervisor or examiner can trace how it got there, directly answering the auditability and transparency risk FINRA names.
4. Access restricted to what the task requires
Scoping an agent's data and system access narrowly enough that the data-sensitivity and scope-and-authority risks cannot silently expand as the agent is reused for adjacent tasks.
5. Written supervisory procedures that name the agent explicitly
WSPs updated to state which agent performs which task, the reviewer responsible, the review frequency, and how the review is documented, the same components FINRA Rule 3110 requires of any supervisory system.
differentiation
How Kriv AI Helps
Kriv AI is a boutique, implementation-focused firm, not a Big 4-style advisory practice. Our work for broker-dealers and investment banks builds the agent inventory, human-validation gate design, and audit-trail structure this page describes, mapped directly to FINRA Rule 3110 supervisory obligations and the specific risk categories FINRA has named for autonomous agents, rather than a generic AI governance framework repurposed from another industry. See our companion page on AI model risk management for banks for how this fits alongside SR 26-2, which explicitly excludes agentic AI from its own formal scope, leaving FINRA's rules and a firm's internal governance to cover the gap.
Enterprise and regulated-industry engagements start at $200 per hour, a fractional AI governance lead who owns your program on an ongoing basis runs $300 to $400 per hour, and specialized advisory work runs $400 to $700 per hour, all with an $8,000 minimum engagement. See current rate detail on the pricing page, or book a discovery call to scope your specific agentic AI deployment.
Sources
Cited sources
- FINRA Publishes 2026 Regulatory Oversight Report (December 9, 2025)
- FINRA Blog: Emerging Trend in GenAI, Observations on AI Agents (January 27, 2026)
- FINRA Rule 3110: Supervision
- FINRA Key Topics: Artificial Intelligence (AI)
- SEC 2026 Examination Priorities (November 17, 2025)
- Federal Reserve SR 26-2, Revised Guidance on Model Risk Management (April 17, 2026)
Straight answers
Frequently asked questions about How Agentic AI Works in Investment Banking Compliance
How does agentic AI work in investment banking compliance?
Agentic AI uses autonomous, multi-step AI agents to draft trade surveillance alerts, screen KYC and AML data, and prepare regulatory filings, always with a named human reviewer approving the final regulated decision. FINRA treats these systems as a distinct, higher risk category from a generative AI chatbot because they can plan and act without human validation.
Does FINRA have a separate rule for AI agents?
No. FINRA states its rules are intended to be technology neutral and continue to apply when member firms use generative AI or similar technologies. There is no separate agentic AI rulebook; existing supervision, communications, recordkeeping, and fair-dealing rules apply directly.
What risks does FINRA name specifically for autonomous AI agents?
FINRA's 2026 Regulatory Oversight Report and its January 2026 blog on AI agents name seven risk categories: autonomy without human validation, scope and authority creep, auditability and transparency gaps, data sensitivity, insufficient domain knowledge, reward misalignment, and the inherent generative AI risks of bias, hallucination, and privacy.
Does SR 26-2 cover agentic AI used by investment banks?
No. SR 26-2, the Federal Reserve, OCC, and FDIC's bank model risk management guidance, explicitly excludes generative and agentic AI models from its formal scope. FINRA's rules and a firm's own internal governance are what actually cover agentic AI used in compliance workflows.
What does FINRA Rule 3110 require for an AI agent used in compliance?
FINRA Rule 3110 requires a reasonably designed supervisory system with written supervisory procedures naming who is responsible for each review, the review frequency, and how it is documented. Applied to an AI agent, that means naming the agent's task, its human reviewer, and the review cadence in the firm's written supervisory procedures.
How much does agentic AI governance advisory cost?
Kriv AI's enterprise and regulated-industry engagements start at a $200 per hour floor, a fractional AI governance lead runs $300 to $400 per hour, and specialized advisory work runs $400 to $700 per hour, with an $8,000 minimum engagement.
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