
Indian Digital Payment Landscape Perceives Over 65,000 Crore Transactions

According to reports, Indian digital payment landscape witnessed over 65,000 crore transactions amounting to more than Rs 12,000 lakh crore in the past six financial years.
Minister of State for Finance Pankaj Chaudhary stated in a written response in the Lok Sabha that the government has been collaborating closely with various stakeholders, such as the Reserve Bank of India (RBI), National Payments Corporation of India (NPCI), fintech companies, banks, and state governments, to boost the adoption of digital payments across the country, particularly in tier-II and tier-III cities.
In 2021, RBI established a Payments Infrastructure Development Fund (PIDF) to promote the implementation of digital payments acceptance infrastructure in tier-3 to 6 cities, as well as in North-Eastern States and Jammu & Kashmir.
As of May 31, 2025, approximately 4.77 crore digital touch points have been implemented via PIDF.
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In response to another inquiry, Chaudhary stated that the New Digital Credit Assessment Model for MSMEs was introduced in the Union Budget for 2024-25.
The model utilizes verifiable data obtained digitally and creates automated processes for MSME loan evaluations through objective decision-making for every loan application and model-driven limit assessments
The model utilizes verifiable data obtained digitally and creates automated processes for MSME loan evaluations through objective decision-making for every loan application and model-driven limit assessments for both Existing to Bank (ETB) and New to Bank (NTB) MSME borrowers.
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The model may utilize digital footprints such as Pan authentication with NSDL, OTP-based mobile and email verification, Application Programming Interface (API) integration, obtaining GST data via service providers, bank statement assessment using account aggregators, ITR upload and verification, API-driven retrieval from commercial and consumer bureaus, due diligence through Credit Information Companies (CICs), fraud detection via APIs, and more.