Paytm, PhonePe and Razorpay bet on AI, but meaningful revenue may wait until FY28

India’s leading payments players are rolling out AI tools across merchant services, lending and customer support. The near-term retail payoff is likely to be lower servicing costs, better use of transaction data and higher merchant engagement, while direct AI-led revenue is expected to take longer.

— Source publishedTue, 15 Sept, 2026, 12:55 IST·First seen Tue, 15 Sept, 2026, 13:58 IST·Source NDTV Profit

What happened

Paytm, PhonePe and Razorpay are rolling out AI tools for payments, merchants, lending and customer service, but Emkay expects meaningful AI revenue only from

Key facts

  • FY28 onwards
  • 32% of Global Fintech Fest 2026 sessions
  • around 3 trillion data points
  • 4 billion payments
  • 24.51 billion UPI transactions in August 2026
  • Rs 29.82 trillion UPI transaction value
  • more than 64 lenders
  • 136 data services

Why this matters

Corporate development teams should prioritize partnerships or acquisitions that add proprietary merchant data, AI-enabled servicing capabilities and distribution rather than near-term standalone AI revenue.

What to watch

  • Quarterly disclosures showing reductions in customer-support cost, fraud losses or merchant acquisition cost.
  • Growth in merchant payment volume and active-merchant retention following AI-tool rollouts.
  • Higher merchant-loan approval rates, lower delinquencies or increased lending cross-sell tied to transaction-data models.
  • New RBI guidance on AI use, digital lending, data localization, consent or algorithmic accountability.
  • Whether platforms begin charging subscriptions or premium fees for reconciliation, analytics, CRM and AI support tools.
  • Cloud and model-inference expenses rising faster than operating-cost savings.
  • Bundle AI merchant assistants with payment acceptance, reconciliation, GST workflows and CRM rather than sell them as standalone products.
  • Use AI-generated transaction insights to identify creditworthy merchants, but retain human review and conservative controls for lending decisions.
  • Prioritize vernacular support automation for smaller merchants, where service-cost reduction and engagement gains are likely highest.
  • Track AI feature adoption by active merchants and link product usage to payment volume, retention and loan conversion.
  • Build consent, data-governance and audit trails ahead of tighter RBI and digital-lending scrutiny.