For the last few years, AI in banking has mostly meant AI that recommends. A model flags a suspicious transaction, scores a credit application, or suggests the next best product, and a human banker still presses the button. That line is now blurring fast.
Agentic AI systems don’t just recommend anymore. They act. An agentic underwriting tool can pull a customer’s data, run the credit decision, and issue a conditional sanction letter, with no human in the loop until something goes wrong. A treasury agent can rebalance a liquidity position across accounts. A collections agent can decide who gets a call, a reminder, or a restructuring offer. The efficiency gains are real. So is a question boards and risk committees are only starting to grapple with: when the agent acts and the outcome is wrong, who is accountable?
Why this question is different from ordinary model risk
Traditional model risk management assumes a human decision maker downstream of the model, someone who can pause, question, or override the output before it becomes an action. That assumption is what most existing governance frameworks are built around, and it’s exactly the kind of foundation covered in RMAI’s Fintech Risk Management & Governance course, which looks closely at how platform based, algorithmic decision making changes the risk picture for banks and NBFCs.
Agentic AI removes that human checkpoint assumption. The model doesn’t just score risk, it executes on it, sometimes chaining several actions together without a human checkpoint in between. That changes accountability from a single “who approved this model” question into a chain: who designed the agent’s permissions, who set its guardrails, who monitored it in production, and who was supposed to catch the failure before the customer did?
Where accountability actually breaks down
A few patterns show up repeatedly in early agentic AI deployments across BFSI:
- The permissions gap. An agent is given broader system access than the task strictly requires, “to make it useful.” When it takes an unexpected action within that access, no single team owns the decision to grant that scope in the first place.
- The monitoring gap. Real time human oversight of agent actions is assumed but not actually built. Dashboards exist, but nobody is watching them at the moment the agent acts.
- The vendor gap. Many agentic tools are layered on top of third party models or platforms. When something goes wrong, banks and vendors each point to the other’s terms of service.
- The documentation gap. Because the agent’s reasoning chain isn’t always logged in a way that’s auditable after the fact, reconstructing why it took a specific action becomes difficult, which is exactly when regulators and customers want an answer.
What good governance looks like
A few practical anchors are emerging as sensible baseline practice for Indian banks and NBFCs experimenting with agentic AI:
- Named human accountability at every autonomy level. Every agentic workflow should map to a specific role, not just a committee, that owns outcomes, the same way a credit officer owns a lending decision today.
- Bounded action permissions, reviewed like access controls. Treat what an agent is allowed to do, not just what it’s allowed to see, as a formal access control decision, revisited on a schedule.
- Action level audit trails. Every agentic action, not just the final output, should be logged and explainable in plain language, not just as a model confidence score.
- Circuit breakers, not just monitoring. Agents operating in production should have hard coded thresholds that force a pause and human review, rather than relying on someone noticing a dashboard anomaly in time.
- Board level visibility. This is now squarely a governance topic, not just a technology one, and it belongs on the same agenda as enterprise risk framework decisions.
Professionals who want a structured grounding in these fundamentals, from risk registers and accountability mapping to enterprise frameworks like COSO and ISO 31000, can start with RMAI’s Mastering Risk Registers course, which walks through assigning ownership and building audit ready escalation mechanisms.
Building the right skills for this shift
Agentic AI accountability sits at the intersection of credit risk, operational risk, and technology governance, so it rewards professionals who understand more than one of these domains. RMAI’s Online Certificate Course in Credit Risk Management is a useful starting point for anyone whose agentic AI exposure runs through underwriting and lending decisions, while the Market Risk Management course is relevant for teams using agents in treasury and trading functions.
Conclusion
Agentic AI doesn’t remove accountability from banking, it just moves it earlier, into the design and permissioning stage, and spreads it across more roles than a single “model owner” sign off used to cover. Banks that treat this as a governance and risk ownership question now, and invest in building the right skills across their risk teams, will be in a far stronger position than those that discover the gap only after an agent has already taken an action nobody meant to authorise.
