Ask a bank about AI a few years ago and you'd get a chatbot that could reset your password and not much else. Ask the same question in 2026 and the answer looks different. Banks and NBFCs are now running software that doesn't just answer questions. It pulls your credit file, checks it against lending policy, decides whether to approve you, and only calls in a human when something falls outside the rules it's been given.
The industry calls this agentic AI, and the distinction from older automation is worth understanding if you've ever wondered why a loan decision that used to take days now takes minutes.
Automation versus an agent
Loan origination, transaction monitoring and account servicing have run on rule-based automation for years: fixed approval paths, static thresholds, high-volume processing of predictable tasks. That's not new. What's new is the layer sitting above those rules. Instead of following one fixed path, an AI agent can interpret a goal, pull data from several systems, reason through it, and take an action, then explain what it did and why.
In lending specifically, that shows up as agents that orchestrate credit bureau calls, collect and verify documents, run a risk score, and issue a decision end to end, escalating to a human underwriter only when the case doesn't fit standard policy. The same pattern is spreading into KYC and AML checks, customer disputes, and regulatory reporting, where an agent pulls data from multiple systems and fills in what used to be a manual template.
How fast this is moving
Wolters Kluwer estimates that 44% of finance teams will use agentic AI in 2026, a jump of more than 600% from where adoption stood before. KPMG puts global enterprise spend on agentic AI at roughly $50 billion in 2025 alone. Inside banking specifically, 2026 is being described by several major institutions as the year this moves from pilot projects into production, with agents wired directly into core banking, CRM, fraud and compliance systems rather than tested in isolation.
The part that should give you pause
Speed isn't the whole story. A 2025 EY review found that while most financial services firms disclose their use of AI to customers, controls in other areas lag behind: roughly 30% of the firms studied had limited or no controls to check their AI for bias. A separate industry study by Infosys found that only 2% of companies had what it considered adequate AI guardrails in place, and 95% of respondents had experienced at least one AI-related incident, ranging from privacy issues to outright inaccurate decisions.
That gap between adoption speed and governance is exactly why regulators have started drawing firmer lines around where agentic AI is allowed to act on its own and where a human has to stay in the loop, particularly for anything that counts as a regulated credit decision.
What it means if you're the one applying for a loan
For a borrower, the practical upside is real: faster eligibility checks, fewer branch visits, decisions that used to take a week now taking minutes. The thing worth asking any lender, digital or otherwise, is what happens when the automated decision gets something wrong. A well-run lender keeps a human reachable for exactly that scenario, with a defined escalation path rather than a support queue that goes nowhere. That's a fair question to ask before you accept any offer, automated or not.