BNPL Boom: Household Consumption Distortions and the RBI’s Tightening of Unsecured Credit

Policy Update
Rashi Kothari

Background

The Buy Now, Pay Later (BNPL) phenomenon in India has emerged as a prominent structural shift in retail credit since 2021, accelerated by the integration of the Unified Payments Interface (UPI) with instant digital underwriting. Unlike cyclical market expansions, this qualifies as a structural shift because it permanently overrides traditional physical underwriting barriers, embedding credit directly into daily micro-transactions for a previously unbacked demographic. 

Historically, traditional banking credit in India was restricted to a very small group, as less than 5% of adults had access to credit cards (Reserve Bank of India, 2021).  BNPL models were introduced by Fintech platforms and Non-Banking Financial Companies (NBFCs) to fill this systemic gap, targeting younger consumer segments and consumers across Tier-II and Tier-III urban clusters who lacked formal credit histories and possessed a high marginal propensity to consume but limited access to traditional banking channels.

The primary reason behind the rapid growth of these small, short-term unsecured loans was to provide instant, hassle-free credit at the point of sale. However, this convenience catalyzed an institutional transition from traditional income-led consumption to credit-led consumption. As individuals increasingly leveraged digital credit lines for non-essential lifestyle spending, the trend began to alter household balance sheets, prompting regulatory intervention by the Reserve Bank of India (RBI).

Functioning

The institutional framework of the BNPL ecosystem relies on a three-way arrangement involving Fintech Digital Lending Applications (DLAs), regulated balance-sheet lenders (such as Scheduled Commercial Banks or NBFCs), and merchant networks. Operating on alternative data underwriting, such as behavioral profiling, smartphone telemetry, and utility bill histories— Fintech platforms assess credit risk and provide instant credit approvals within seconds directly at the checkout interface.

The funding structures supporting this model primarily depend on First Loss Default Guarantees (FLDG) and co-lending partnerships. Under these setups, the unregulated fintech partner provides a contractually agreed loss cushion (typically capped at 5% under the RBI’s June 2023 Digital Lending Guidelines) to the regulated bank or NBFC providing the capital. On the ground, the consumer receives an interest-free credit period (ranging from 15 to 45 days) or split monthly installments. The structural revenue model is sustained not by upfront consumer interest, but by substantial Merchant Discount Rates (MDR) ranging from 2% to 6%, which merchants pay to platforms to minimize transactional friction and optimize order volumes (NITI Aayog, 2022).

Performance

Empirical indicators track a significant surge in consumer borrowing volumes alongside a notable shift in household financial dynamics. According to market data from the Centre for Advanced Financial Research and Learning (CAFRAL), the share of unsecured loans in total scheduled commercial bank credit rose from approximately 18% in March 2016 to 25.3% by March 2024, with unsecured personal loans comprising nearly one-third of the total personal loan portfolio.

Figure 1: India Buy Now Pay Later (BNPL) Services Market Size and Overview

Source: Mordor Intelligence, India Buy Now Pay Later Services Market Analysis (2026–2031).

While this rapid industry growth shows how quickly digital credit apps have scaled, it has also caused major shifts across the wider banking sector. To understand how these surging market numbers affect household finances and overall bank credit, Table 1 compares the latest metrics from official government and bank reports: 

Table 1: Macroeconomic Credit Shift and Retail Distribution Metrics (2024–2026)

Source: Compiled by the author based on composite disclosures from CAFRAL, RBI Annual Reports, and Sectoral Distribution Databases (2024–2026).

Macroeconomic Inversion

The expansion of this credit segment coincided with a profound structural shift in broader macroeconomic indicators. India’s net household financial savings plummeted to a multi-decade low of approximately 5.3% of GDP in FY 2022–23 before staging a minor recovery to approximately 6% by FY 2024–25. Concurrently, household liabilities escalated by 78% during the post-pandemic recovery phase, signaling a clear transition toward debt-financed consumption.

This historic credit explosion between 2021 and 2024 prompted defensive regulatory intervention. Recognizing that systemic asset quality risks were underpriced, the RBI deployed countercyclical macroprudential measures in late 2023 and mid-2024. By raising risk weights on consumer loans by 25 percentage points, the central bank deliberately tightened credit supply and altered the cost of capital, successfully cooling down the hyper-growth of short-term retail lending lines by early 2026.

Impact

The rapid adoption of BNPL has drawn varied assessments from market participants and regulatory bodies. Proponents and industry bodies highlight the positive role of alternative underwriting in advancing financial inclusion, noting that digital credit paths have extended formal financial access to millions of consumers who previously lacked a credit history.

However, independent academic research and structural trend reviews, including the RBI Financial Stability Report, present a more cautious perspective on this credit expansion. This behavioral shift represents a fundamental distortion of Milton Friedman’s Permanent Income Hypothesis, which models total income as:

Y = Y_p + Y_t

Where:

• Y represents total current income
• Y_p  represents permanent (expected long-term) income
• Y_t  represents transitory (temporary) income

Under classical framework assumptions, households utilize formal credit to smooth consumption against temporary negative shocks (Y_t ). BNPL, however, modifies this dynamic by fundamentally shifting the consumer’s perception of transitory income. By creating an illusion of immediate, frictionless liquidity at the point of sale, it masks a future liability as current disposable income. This induces an intertemporal optimization failure: consumers treat the digital credit line as a positive transitory income shock (+Y_t ), leading them to artificially elevate current consumption. Consequently, their long-term budget constraints are systematically mispriced, forcing the over-allocation of future permanent income (Y_p ) to service high-cost, short-term lifestyle debt.

Further analysis reveals that individuals with an annual income below ₹5 lakh hold the highest share of unsecured retail loans, leaving this segment vulnerable to macroeconomic shifts or income disruptions. While banking sector Non-Performing Assets (NPAs) reached a low of 2.6% in late 2024, unsecured lending accounted for a significant 51.9% of new additions to the retail NPA portfolio in early fiscal cycles, proving that rapid volume accumulation across digital credit platforms can rapidly compromise aggregate retail asset quality.

Emerging Structural Vulnerabilities

1. Concentration of Systemic Funding Counterparties: Fintech platforms frequently rely on a highly concentrated pool of partner NBFCs and banks for balance-sheet capital, creating systemic compounding vulnerabilities when funding dependencies concentrate across identical regulatory lines.
   
2. Alternative Data Disconnect and Underwriting Vulnerabilities: Relying on behavioral profiling and digital data instead of structured credit histories can lead to mispriced risk. This makes underwriting models highly unreliable during sudden economic slowdowns or unexpected job market disruptions.
   
3. Regulatory Arbitration via Unregulated Intermediaries: Despite the implementation of the Digital Lending Guidelines, certain platforms utilize complex corporate structures to navigate around the strict 5% FLDG cap protections, keeping hidden balance-sheet risks active.
   
4. Credit Overlap and Invisible Leverage: Because short-term BNPL balances are frequently reported to credit bureaus with operational lags, individual borrowers can accumulate multiple concurrent loans across different applications, obscuring their true leverage profiles.

Way Forward

1. Calibrating Countercyclical Macroprudential Buffers: The RBI should build on its risk-weight increases by implementing a dynamic, tier-based risk-weight framework that automatically adjusts capital requirements based on real-time tracking of retail loan defaults.
   
2. Mandating Real-Time Centralized Bureau Reporting: Regulators should require credit bureaus and digital lenders to transition from monthly batch updates to automated, real-time API reporting for short-tenor liabilities, helping prevent simultaneous over-leverage across multiple platforms.

3. Structuring Productive Embedded Lending Channels: Policy frameworks should encourage digital platforms to shift focus from financing depreciating lifestyle consumer goods toward embedded credit options for productive asset creation and high-yielding human capital investments, such as vocational education, healthcare access, and micro-enterprise operational capital.
   
4. Standardizing Transparent Consumer Disclosure Models: The Ministry of Finance and the RBI should enforce a uniform, simplified digital disclosure format across all DLAs. This would require an explicit, prominent display of the Annual Percentage Rate (APR) and late-fee calculations prior to transaction authorisation, reducing the risk of hidden charges.

References

Centre for Advanced Financial Research and Learning (CAFRAL). (2026). Financialization of Indian households: Trends in savings and borrowing. CAFRAL Research Papers. https://www.cafral.org.in
https://www.cafral.org.in/sfControl/content/Speech/129202464026PMNKDec2024.pdf

Friedman, M. (1957). A theory of the consumption function. Princeton University Press. (University of Delhi Core Curriculum Readings).
https://www.scirp.org/reference/referencespapers?referenceid=1218502

Shroff, M., Sarkar, A., & Ojha, S. (2025, February). RBI eases risk weight on consumer credit and bank loan: Relief for NBFCs and Microfinance Institutions. Khaitan & Co. https://www.khaitanco.com/thought-leadership/relief-for-NBFCs-and-Microfinance-Institutions

Ministry of Finance. (2026). Economic survey 2025–26: Industrial performance and digital infrastructure. Government of India.
https://www.indiabudget.gov.in/economicsurvey/doc/Infographics%20English.pdf

Mordor Intelligence. (2026). India buy now pay later services market size & report analysis, 2031. Industry Reports.
https://www.mordorintelligence.com/industry-reports/buy-now-pay-later-services-market

PwC India. (2025). The Indian payments handbook 2025–2030. PricewaterhouseCoopers Regulatory Publications.
https://www.pwc.in/assets/pdfs/indian-payments-handbook-2025-2030.pdf

Reserve Bank of India. (2023, November 16). Regulatory measures towards consumer credit and bank credit to NBFCs. RBI Official Communications. https://www.rbi.org.in

Reserve Bank of India. (2023). Annual report 2022–23. RBI Central Disclosures.  https://rbidocs.rbi.org.in/rdocs/AnnualReport/PDFs/0ANNUALREPORT20222322A548270D6140D998AA20E8207075E4.PDF

Reserve Bank of India. (2024). Financial stability report, December 2024. RBI Central Publications https://www.fidcindia.org.in/wp-content/uploads/2024/12/RBI-FINANCIAL-STABILITY-REPORT-30-12-24.pdf

Reserve Bank of India. (2022). Report of the working group on digital lending including lending through online platforms and mobile apps. RBI Central Disclosures
https://www.rbi.org.in/commonperson/english/scripts/FAQs.aspx?Id=3413

Ray, P., Bandyopadhyay, A., & Basu, S. (Eds.). (2024). India banking and finance report 2024. National Institute of Bank Management (NIBM) / Academic Foundation.
https://www.nibmindia.org/staticfile/pdf/India%20Banking%20and%20Finance%20Report%202024%20for%20author.pdf

About the Contributor

Rashi Kothari is a Research Intern at the IMPRI Impact and Policy Research Institute, pursuing an intense focus on public policy frameworks, economic infrastructure optimization, and sustainable urban development models.

Acknowledgement

I would like to express my sincere gratitude to IMPRI (Impact and Policy Research Institute) for providing the opportunity to draft this policy update article and for offering a rigorous environment that connects research with policy practice. Special thanks go to the editorial board and coordinators for their insightful feedback and guidance in structuring this piece in the required format.

Reviewed by: Lubina Dua and Asmatwali 

Published by: Ms. Shivani Chauhan

Disclaimer All views expressed in the article belong solely to the author and do not necessarily represent the views or policies of the organisation.

Read more at IMPRI

Production Linked Incentive (PLI) Scheme for Telecom and Networking (2025)

SNA-SPARSH: Reforming India’s Fund Flow for Centrally Sponsored Schemes