Mintoak sees AI-led payment data widening MSME credit access beyond India’s metros

Mintoak co-founder Kabeer Jain argues that embedding AI in payment, lending and risk systems can help MSMEs build financial identities from transaction data and receive more timely working-capital offers.

— Source publishedSat, 25 Jul, 2026, 07:45 IST·First seen Sat, 25 Jul, 2026, 07:49 IST·Source YourStory

What happened

Mintoak co-founder Kabeer Jain argues that AI embedded in payment, lending and risk infrastructure can expand credit access for Indian MSMEs, especially beyond

Key facts

  • 8.7 crore MSMEs registered by June 2026
  • 32.8 crore people employed
  • 23.5% growth in outstanding MSME bank credit during FY26

Why this matters

Banks, lenders and payment networks should assess partnerships with payment-data platforms that can provide embedded underwriting signals and distribution into underserved MSME segments.

What to watch

  • Growth in MSME loan originations sourced through payment platforms and merchant-acquiring apps.
  • RBI guidance on digital lending, AI model governance, consent architecture, data localization and first-loss-default-guarantee structures.
  • Default and delinquency trends for unsecured MSME working-capital loans, especially after seasonal demand declines.
  • Adoption of Account Aggregator data, GST-linked underwriting and interoperable consent frameworks by lenders.
  • Bank and NBFC partnership announcements with payment aggregators, POS providers and QR-payment platforms.
  • Evidence that merchants consolidate payment acceptance or, conversely, increasingly multi-home across payment providers.
  • Changes in UPI merchant economics, MDR policy or payment-settlement rules that affect fintech monetization incentives.
  • Build consent-driven merchant data stacks combining payment settlements, bank-account feeds, GST filings, invoicing, device signals and repayment history.
  • Offer pre-approved, small-ticket revolving working-capital lines tied to settlement flows rather than one-time term loans.
  • Partner with banks and NBFCs for regulated lending capacity, co-lending structures, collections and risk-sharing while retaining merchant-facing UX.
  • Develop explainable underwriting and adverse-action workflows to reduce regulatory, partner-bank and merchant-trust risks.
  • Target underserved tier-2 and tier-3 merchant clusters with vertical-specific models for retail, food service, distributors and service businesses.
  • Use payment data to expand beyond credit into cash-flow forecasting, supplier payments, inventory financing and merchant insurance.

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