AI fintech solutions power UPI’s next growth phase
NPCI CEO Dilip Asbe said last month at Mumbai Tech Week 2026 that AI will be central to UPI’s next phase, from onboarding new users to fraud detection, mule tracking, and credit distribution. That matters because UPI already processes more than 750 million daily transactions, and the next jump toward a billion a day will depend less on app polish and more on operational accuracy. According to TechCrunch’s interview coverage, NPCI sees AI as core infrastructure, not a side feature.
NPCI says AI will shape UPI’s next phase
The main signal from Asbe’s comments is straightforward: India’s payments stack is entering the stage where AI in finance has to do real work inside production systems. That includes user growth, fraud control, and support operations, not just chat interfaces.
Asbe put it plainly: “AI will be used very effectively when we look at the next wave of UPI,” including fraud, mule detection, credit access, and multilingual onboarding. At UPI scale, that reads like an operations brief. If you are moving hundreds of millions of transactions a day, every extra false positive, every missed fraud ring, and every failed onboarding flow becomes a system-level cost.
I’ve seen the same pattern in live automation projects: the flashy model demo gets attention, but the hard part is stitching model decisions into payment rails, case management, analyst review queues, and customer support without slowing the core workflow.
Where AI fits into payments operations
The source points to five practical areas: onboarding, fraud detection, mule detection, multilingual voice flows, and credit distribution. Those are sensible places to start because each has a measurable operational output.
For onboarding, AI for banking can help classify documents, detect form anomalies, route users by language, and reduce abandonment in edge cases. For risk, AI fraud detection systems can score transactions, devices, account links, and behavioral patterns faster than manual review. For mule detection, graph signals usually matter more than a single transaction score: repeated counterparties, device reuse, timing clusters, and sudden cash-out behavior are where the models earn their keep.
The other useful signal is that NPCI is not talking about one giant general model. It is talking about embedded functions inside a regulated workflow. That is much closer to how high-volume payment systems actually get deployed.
A good operator benchmark here is Visa’s fraud disruption work, where network-level patterns matter as much as any one merchant interaction. The same logic applies to UPI: the model is only one layer; the surrounding controls decide whether the system is usable.
In that kind of environment, teams usually need boring but essential integration work before the model helps. That is why many firms start with AI business process automation to connect scoring, routing, review, and audit trails into one operating loop.
Why voice still looks early in India
Asbe was more measured on voice assistants AI than on fraud or onboarding. That restraint is probably right. NPCI launched Hello UPI in 2023, but adoption has not broken out yet, and accuracy remains the gating issue.
In payments, voice fails differently from chat. A chat mistake can often be corrected on screen. A voice mistake during authentication, payee confirmation, or consent capture creates a trust problem immediately. In multilingual markets, the failure modes multiply: accent variance, code-switching, noisy environments, and homophones around names or amounts.
Research from the Bank for International Settlements has repeatedly framed financial AI adoption as a risk-management problem as much as a productivity one. Voice in payments is a good example. The use case may eventually work, but only in narrow flows first: balance checks, status updates, simple mandate actions, or guided support trees.
AI could also change credit and dispute workflows
The more interesting part of the interview, in my view, is not voice. It is the combination of digital footprints, credit, and deterministic dispute handling. That is where AI fintech solutions can create compounding value because the outputs feed into revenue, retention, and risk at the same time.
Asbe said AI should help provide credit to users and merchants with digital footprints. That lines up with a broader shift in AI payments: using transaction behavior, repayment patterns, merchant activity, and support history to improve underwriting inputs. The trade-off is obvious, though. Better prediction is not enough on its own. Credit workflows need transparent rules, consent handling, and appeal paths.
NPCI already has one concrete example in production. Its FIMI model, covered by The Economic Times, is being used for disputes such as mandate cancellation and issue resolution. That matters more than another model launch headline, because dispute systems generate feedback loops fast. You can measure resolution time, escalation rate, repeat contact rate, and bad outcome rate within weeks.
This is also where custom AI agents start to make sense, but only if they are tightly bounded. In finance, an agent that can explain a dispute status or collect missing case details is useful. An agent that takes loosely governed payment action is a very different risk class.
UPI competition may depend on business models
The competition section of the story is easy to underestimate. UPI’s market still appears heavily concentrated, with PhonePe and Google Pay together holding more than 80% share, as noted in the source and in broader reporting on the December 31, 2026 market-share cap timeline.
Asbe’s point was that low switching costs and weak commercial incentives help explain why concentration persists. I think that is right. AI risk analytics and customer support automation may help smaller players operate leaner, but they do not fix distribution economics by themselves.
BHIM is a useful case. NPCI spun it out in 2024 to improve competitiveness, yet its market share is still around 1% by the figures cited in the source. That tells me product sovereignty and security matter, but user acquisition, merchant incentives, and habit loops still dominate. AI can reduce support load or improve onboarding conversion, but it cannot paper over a missing business model.
For context, the Reserve Bank of India’s digital payments reports have long shown that payment growth depends on trust, acceptance infrastructure, and recurring usage, not just feature breadth.
What Indian fintechs should watch next
The next thing to watch is not whether every payments app launches an AI assistant. It is whether narrow, regulated workflows start posting better operating numbers: lower fraud loss, faster dispute resolution, fewer manual reviews, and cleaner onboarding across languages.
If NPCI keeps pushing AI into those infrastructure layers first, that is the more durable path. In payments, the winners are usually the teams that make AI boring enough to survive production, then accurate enough to expand safely.
Martin Kuvandzhiev
CEO and Founder of Encorp.io with expertise in AI and business transformation