Two years ago, “AI in BFSI” mostly meant a chatbot bolted onto a website that could answer “what’s my account balance” and not much else. That phase is over. In 2026, the conversation inside banks, NBFCs, and fintech ops teams has shifted from “should we use AI” to “which workflow do we hand over next.” At Speqto Technologies, we’ve spent the last year building and deploying AI agents for lending, payments, and insurance clients, and the pattern is consistent: the teams getting real ROI aren’t the ones chasing the flashiest model, they’re the ones that picked a boring, repetitive, rule-heavy process and automated it end to end.
What’s actually different about “agents” vs. the old automation
RPA bots have existed for a decade. What’s new is that agents can read unstructured input, make a judgment call within defined guardrails, and take the next action without a human clicking “approve” at every step. The difference shows up clearly in document-heavy BFSI processes, where the bottleneck was never the system of record — it was the human reading PDFs, bank statements, and emails to decide what to do next.
A concrete example: one of our NBFC clients in Pune was processing loan applications where underwriters spent close to 40 minutes per file just assembling income proof, bank statement data, and bureau reports into a single view before they could even start assessing risk. We built an agent that pulls the bank statement, runs cash-flow analysis, cross-checks it against the ITR and bureau pull, flags inconsistencies (like declared income not matching average monthly credits), and drops a structured summary into the underwriter’s queue. The underwriter still makes the credit decision — but their per-file time dropped to under 8 minutes, and the flagged-inconsistency catch rate actually went up because the agent doesn’t get tired at file number 200 of the day.
Where this is showing up across BFSI and fintech right now
- KYC and onboarding: Agents cross-verify PAN, Aadhaar, and address proof against multiple databases, flag mismatches, and only escalate the edge cases — the “this name has three spelling variations across documents” kind of problem — to a human. One payments client cut average onboarding TAT from 2 days to under 4 hours for straightforward cases.
- Reconciliation: Treasury and payments teams running manual Excel reconciliation between settlement files, bank statements, and ledger entries are moving to agents that match transactions, auto-resolve known patterns (like rounding differences or delayed settlement timestamps), and surface only genuine breaks. For a mid-size payment aggregator we worked with, unresolved reconciliation items dropped from roughly 200/day to under 15.
- Collections and recovery: Instead of a fixed call list, agents now prioritize accounts based on repayment behavior signals, draft personalized communication (within RBI-compliant templates), and only route accounts to human agents once a response pattern suggests negotiation is needed.
- Compliance monitoring: Transaction monitoring agents scan for patterns that match suspicious activity typologies, write a first-draft STR narrative, and hand it to the compliance officer for review instead of the officer starting from a blank page.
The part most vendors don’t tell you
None of this works if you skip the boring groundwork. Every failed agent rollout we’ve seen — including a couple of early pilots we had to rebuild for clients — failed for the same three reasons: the source data was messier than anyone admitted, there was no clear escalation path for low-confidence cases, and nobody defined what “good enough” accuracy looked like before going live.
The fix isn’t exotic. It’s writing down, in plain language, the actual decision rules your best underwriter or ops analyst uses, then testing the agent against six months of historical cases before it touches anything live. For the NBFC example above, we ran the agent in shadow mode for three weeks, comparing its flags against what underwriters actually caught, before it was allowed to pre-populate a single case file.
What decision-makers should actually do in 2026
- Pick one workflow where the cost is measurable — hours per case, TAT, error rate — not something fuzzy like “customer experience.”
- Insist on a shadow-mode or parallel-run period before the agent gets any decision authority.
- Keep a human in the loop for anything touching credit decisions, compliance filings, or regulatory reporting — agents should draft and flag, not approve, until your audit trail and governance framework can account for it.
- Measure the thing you actually care about (cost per file, TAT, error rate) weekly, not quarterly — agent performance drifts as input data changes, and you want to catch that early.
The shift happening in 2026 isn’t that AI got smarter overnight. It’s that enough BFSI and fintech teams have now run these systems long enough to know where the guardrails need to go — and that’s what’s finally making wholesale workflow replacement, not just chatbot add-ons, a realistic project rather than a pilot that quietly dies after six months.
Speqto Technologies builds and deploys AI agents for lending, payments, and compliance workflows for BFSI and fintech teams. If you’re evaluating where to start, we’re happy to look at your current process maps and tell you honestly whether an agent is the right fit — or whether you need to fix the data first.
