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InFocus CXOs
It is 3 a.m. The branches are dark, the trading desk is quiet, the contact centre has long since gone home. And yet, somewhere inside the institution, work is happening.
A payment leaves an account at an hour the customer has never transacted, to a counterparty they have never paid, in a country they have never visited. No human sees it. But a system does — and instead of dropping the anomaly into a queue that someone will reach at nine, it acts. It pauses the transaction, pulls the customer's recent history, checks the device and the merchant, drafts a hold notice, and sends a verification prompt to the customer's phone. By the time the fraud team logs in for the morning, the matter is not a ticket waiting to be opened. It is a story that already has an ending.
This is not science fiction, and it is not a chatbot. It is the early shape of the agentic enterprise — and for banking, financial services, and insurance, it quietly rewrites the questions leaders ought to be asking about how their organisations actually run.
The quiet shift
For the past few years, most institutions have treated artificial intelligence as a gifted assistant. It drafted the memo, summarised the report, suggested the next sentence — and then waited for a human to decide and act. Useful, certainly. But fundamentally, it was a faster pencil.
What is arriving now is different in kind, not degree. An agent does not wait. Give it a goal and a set of boundaries, and it will break the goal into steps, reach into systems through APIs, make the small decisions along the way, and pursue the outcome over hours or days — surfacing only the moments that genuinely need a human.
The distinction sounds technical but it is organisational. A copilot makes your people faster at the work they already do. An agent takes the work. For an industry built almost entirely on process — reconciliation, underwriting, onboarding, claims, dispute resolution — that is not a productivity tweak.
Finance that no longer waits for the calendar
Walk into the office of a finance controller today and you will find a profession organised around dates. The overnight reconciliation. The month-end close. The quarterly forecast. Work arrives in batches and is cleared in batches, and the rhythm of the function is the rhythm of the calendar.
Now imagine the controller who arrives one Monday to find the close already drafted. Through the weekend, agents reconciled the ledgers, chased the exceptions, monitored cash positions across a dozen currencies, executed liquidity sweeps inside pre-approved limits, and assembled the package that used to consume the first week of every month. The controller's morning is no longer spent compiling the numbers. It is spent interrogating what they mean.
That is the real promise of autonomous finance: not a faster ledger, but a living one. Forecasts that update when reality changes rather than when the quarter turns. Working capital managed continuously instead of reviewed on Fridays. The constraint shifts, too — it is no longer how much the team can process, but how good the guardrails are within which the agents are trusted to act.
The watchman that never blinks
If autonomous finance is a story about speed, intelligent risk is a story about continuity.
For most of its history, risk management in BFSI has been episodic. Credit is assessed at origination and then largely forgotten until something breaks. Fraud is caught after the money has moved. Financial-crime screening runs in batches. The institution looks hard at risk on a schedule, and is otherwise looking elsewhere.
Agentic systems collapse that gap between sensing and responding. The agent that approved a loan can keep watching the borrower's cash flows and quietly adjust limits as conditions shift. The system that flags a suspicious transaction can chase the thread across accounts, counterparties, and external data, assembling a case file richer than any rules engine could — and sparing the investigators the flood of false positives that drains their week. In underwriting and claims, agents can gather and verify the inputs a human expert needs, so the expert spends their judgment where it matters rather than on data entry.
The prize is not merely efficiency. It is a risk posture that moves at the speed of the threat — but only for the institution that can explain, audit, and defend everything its agents do. Which brings us to the harder part of the story.
The paradox of the helpful machine
Here is the uncomfortable truth at the centre of the agentic enterprise: the more capable the machine becomes, the more fragile trust becomes.
Customers have a long memory for the moment an algorithm declined them and no one could say why. Automate carelessly, and every efficiency gain is paid for in resentment. Yet automate thoughtfully, and the same technology can deepen a relationship in ways a human-only model never could. The agent that notices an overdraft forming and offers a fix before it lands. The insurer's system that spots a coverage gap before the loss. The dispute resolved before the customer thinks to raise it.
The difference between these two futures is not how much automation an institution deploys. It is how transparent that automation is. Trust, in the agentic world, is not a slogan printed in a brochure. It is an architectural decision — the ability to tell a customer plainly what was decided, why, and how to push back, and the certainty that a human is reachable when the stakes are high. Every autonomous interaction either earns trust or spends it.
The danger nobody puts on the calendar
There is one failure mode that should worry every board, and it is not the dramatic one. The risk is not that an institution moves too fast and an agent runs amok. The greater risk is that it never really moves at all — that it mistakes a permanent state of pilots for progress, generating ever more elaborate proof-of-concept slides while a competitor quietly rewires its operating model.
Avoiding both extremes requires treating governance as the foundation rather than the afterthought. Agents, like employees, need identities, permissions, and spending limits. They need a clear line on what they may decide alone, what requires a human on the loop, and what must be escalated. They need audit trails that cannot be edited, the ability to be paused or reversed, and continuous watch for the day their behaviour drifts. The institutions that build this scaffolding early will not move slower for it. They will move faster, because they will have earned the right to.
The choice on the table
The agentic enterprise is not a destination to be planned for indefinitely. The opening scene of this article — the fraud resolved before dawn, the close drafted over the weekend, the customer warned before the loss — is built from capabilities that exist today.
So the question for senior leaders is no longer whether agentic AI will reshape BFSI. It is whether their institution will write that story or merely appear in someone else's. Three questions belong on the next leadership agenda.
The leaders who win will not be the ones who deploy the most agents. They will be the ones who treat autonomy, intelligence, and trust not as three separate projects, but as a single capability — and who begin building it while the night shift is still quietly proving what is possible.
The Journey Into Industry
Manish Sharma is a seasoned digital transformation leader with over two decades of experience across insurance, banking, and global financial services. A multi-certified professional, he specialises in driving enterprise-wide innovation through AI/ML, automation, and data-driven strategies. He has led large-scale transformation programmes, built high-performing global teams, and delivered measurable business outcomes across complex ecosystems. Recognised as a “Next100 IT Leader” and a “40 Under 40” high performer, Manish is also an invited speaker and author, passionate about enabling customer-centric growth and operational excellence
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