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By Hitesh Agrawal, Founder & Managing Director, THEM Consulting
Agentic AI enables banking systems to plan, execute and adapt across multiple tasks, moving beyond traditional prompt-response automation and fixed workflows.
Banks and financial institutions are deploying agents across fraud detection, AML, credit underwriting, KYC, customer disputes, insurance claims and payment workflows.
Autonomous financial decisions require robust permissioning, audit trails, explainability, cybersecurity, human oversight and workforce capabilities to manage emerging risks.
Artificial intelligence in Indian banking has largely meant chatbots that answer balance queries, recommendation engines that suggest the next product, and fraud models that flag suspicious transactions for a human to review. That era is ending quickly. What is emerging now is agentic AI: a class of systems that does not just respond to a prompt, but plans, decides and acts across multiple steps without waiting for a human to approve each move.
The shift is not cosmetic. It changes how credit gets underwritten, how KYC gets verified, how fraud gets stopped, and how service teams spend their day. The numbers behind it are large enough to demand attention. The operational details are messy enough to demand caution.
Traditional automation in banking, including robotic process automation and early generative AI tools, worked on a simple pattern: a human triggers a task, the system executes a fixed script, and the output goes back to a human for the next step. Agentic AI breaks that pattern. An agent is given a goal, something like "resolve this customer dispute" or "underwrite this loan application," and then independently queries APIs, pulls data from multiple systems, applies reasoning and takes the next action, often chaining several steps together before a human ever sees the result.
This matters technically because RPA breaks the moment an interface or a data format changes. Agentic systems, built on reasoning loops that plan, act and adjust based on outcomes, can handle edge cases a rules-based script cannot. That flexibility is precisely why banks are willing to give these systems more autonomy, and precisely why the risk profile looks different from anything that came before.
The global market for agentic AI in financial services is estimated at close to 7.8 billion dollars in 2026, up from around 5.5 billion a year ago, on a path toward more than 43 billion by 2031. That implies a compound annual growth rate above 41 percent. Even accounting for the usual noise in market-sizing reports, growth at that scale signals genuine capital commitment rather than marketing enthusiasm.
More telling than the headline number is the adoption curve inside institutions. Around 44 percent of financial services teams report actively using agentic AI in 2026, several hundred percentage points higher than early 2025. Among the largest global banks, more than seven in ten now have programs that have moved past proof-of-concept into production or advanced piloting. Fintechs and neobanks, unburdened by decades-old core banking systems, are moving faster still. A parallel market has emerged around governance: platforms that monitor, audit and constrain what autonomous agents are allowed to do are already valued near 9 billion dollars in 2026 and expected to cross 14 billion within five years. That the oversight layer is growing almost as fast as the core technology tells you banks understand autonomy without control is not a viable strategy in a regulated industry.
Fraud detection and anti-money laundering account for the largest functional share of agentic deployments, roughly a third of all use cases by some estimates. Fraud patterns shift constantly, transaction volumes are enormous, and the cost of a missed signal is high. Agents that correlate cross-border transaction patterns, flag anomalies and take a first action such as placing a temporary hold offer a meaningful edge over static rule engines.
Credit underwriting has moved from experimental to operational. Rather than a loan officer manually pulling GST filings, bank statements and alternate data sources, an agent queries multiple verification APIs in parallel and produces a draft underwriting memo within seconds. The human role shifts from gathering data to reviewing a structured recommendation.
Onboarding and KYC verification follow a similar pattern. Multi-agent architectures orchestrate biometric de-duplication, registry lookups against systems like CKYC, and fraud checks simultaneously instead of sequentially, cutting what used to take hours down to near real time. For insurers, agentic workflows validate first-notice-of-loss claims, cross-check damage documentation and trigger payouts on small claims without human intervention.
This is no longer confined to individual institutions. NPCI is developing a Unified Agent Protocol that would let verified AI agents execute UPI payments without the user approving every transaction, having already piloted agentic payments publicly at the last Global Fintech Fest. When the country’s payment rails are themselves being redesigned for agents, the question for banks is not whether to participate but how quickly they can be ready.
Across these use cases, institutions report autonomous resolution rates exceeding 80 percent for structured tasks such as dispute triage and routine compliance checks. That is a real productivity gain, but it carries a specific risk. When 80 percent of the easy work disappears, what remains is disproportionately the hard, ambiguous and emotionally charged work that agents are not yet trusted to handle alone. Support staff are left with an inbox of complex disputes and fraud complaints from customers who have already been through an automated flow that failed them. Industry estimates put the AI and data skills gap inside BFSI global capability centres at around 42 percent, meaning a significant share of the workforce lacks the combination of financial domain knowledge and technical fluency these systems demand.
Most of this conversation treats agentic AI as an engineering problem. It is at least as much a design problem. Autonomy only creates value if a customer can understand what an agent is about to do on their behalf, and a supervisor can see why it did what it did. That is interface work: consent screens that make agent permissions legible rather than buried inside a mandate flow, explanations that surface the two or three factors behind a credit decision instead of a confidence score, and override controls that are obvious under pressure rather than three taps deep.
In Indian retail banking, where a large share of users transact in a second language on an entry-level device, this is not polish. It is the difference between an agent people trust with a payment and one they switch off. Banks that ship autonomy without designing the visible layer around it will find adoption stalling for reasons no amount of model retraining can fix.
Giving an autonomous agent the ability to query core banking systems, move funds or freeze accounts introduces a new category of cybersecurity concern. Agent permissioning, meaning precisely defining what each agent may do and rate-limiting how often it can act, is now a primary vulnerability that did not exist in the same form when humans initiated every transaction. Meanwhile fraud itself is becoming more sophisticated, with a large share of advanced attempts involving generative AI or synthetic voice, pushing banks to deploy defensive agents that evaluate behavioural biometrics and liveness signals in real time.
Regulators in India have been explicit that autonomy does not remove accountability. Under SEBI’s updated Cybersecurity and Cyber Resilience Framework, regulated entities such as brokers, asset managers and depositories remain directly liable for the actions of their AI systems, closing off any defence built around blaming the algorithm. The Reserve Bank of India has taken a similarly clear position that high-stakes decisions, including credit rejections, loan restructuring and account closures, must retain a human in the loop and remain explainable, even as agentic systems handle the surrounding workflow. India’s data protection framework adds another layer, requiring that every autonomous action, data query and API call an agent makes be logged into an auditable trail a human can review after the fact.
Agentic AI in BFSI is not a distant trend. It is already running fraud checks, assembling credit memos and triaging disputes inside real institutions today, and it is moving into the payment rails themselves. The technology has proven it can compress work that used to take hours into seconds. What remains unresolved is whether BFSI organizations can build the governance, workforce readiness and design discipline to match the pace of deployment.
The institutions that get this right will be the ones treating Agentic AI as a governance, people and design problem as much as a technology problem, measuring success not by how much work an agent completes on its own, but by how well the humans on both sides of it, customers and supervisors alike, can understand, correct and take responsibility for what it does.
