Unified Lending Interface: RBI’s Digital Push for Faster Credit Access (2025)

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Policy Update
Sruti Halder

Background

India’s credit market has long suffered from a mismatch between the volume of formal financial data generated by citizens and enterprises and lenders’ ability to access it efficiently. Borrower information land records, tax filings, credit bureau scores, dairy cooperative payments, and Aadhaar-linked identity data has historically remained siloed across ministries, state governments, and private entities, forcing lenders to rely on manual documentation and prolonging loan appraisal from weeks to months.

Against this backdrop, the Reserve Bank of India conceived the Unified Lending Interface (ULI), envisaged as the third pillar of India’s “new trinity” of digital public infrastructure, alongside the Jan Dhan-Aadhaar-Mobile (JAM) framework and the Unified Payments Interface (UPI). Just as UPI standardized digital payments, ULI is meant to standardise digital credit delivery, serving India’s substantial unmet demand for credit and enhancing financial inclusion.

The policy’s roots trace to a pilot launched in September 2022 for digitising Kisan Credit Card (KCC) loans below ₹1.6 lakh across select districts in Madhya Pradesh, Tamil Nadu, Karnataka, Uttar Pradesh, and Maharashtra. This pilot enabled doorstep disbursement through assisted and self-service modes, eliminating paperwork and cutting processing times. Building on this, the RBI formally unveiled the underlying architecture as the “Public Tech Platform for Frictionless Credit” (PTPFC) on August 10, 2023, developed by the Reserve Bank Innovation Hub (RBIH) in Bengaluru. It was rebranded and scaled up as ULI when Governor Shaktikanta Das announced its national rollout at the “RBI@90 Global Conference” on Digital Public Infrastructure, Bengaluru, on August 26, 2024.

The core rationale is to address India’s persistent credit gap for farmers, dairy producers, and MSMEs, which conventional underwriting has historically excluded for lack of documentation and collateral. PwC estimates India’s overall credit gap at roughly USD 530 billion, with MSMEs alone facing an unmet demand of nearly ₹80 lakh crore. ULI’s objectives are threefold: enable consent-based access to financial and non-financial borrower data; standardise lender-data integration through common APIs, eliminating one-to-one technical tie-ups; and shrink turnaround times for small-ticket rural and MSME credit. Target beneficiaries are farmers seeking KCC and dairy loans, MSMEs seeking working-capital credit, and new-to-credit borrowers.

Key provisions include a “plug-and-play” API gateway spanning the entire loan lifecycle identity verification, eligibility, application, and disbursement with consent-based access to digitised land records, satellite imagery, GSTN records, milk-cooperative payment histories, and credit bureau scores. Since 2024, scope has widened steadily: twelve loan journeys now operate on the platform, spanning KCC, digital cattle, MSME unsecured, housing, personal, tractor, micro-business, vehicle, digital gold, e-Mudra, pension, and dairy maintenance loans. In June 2025, the Department of Financial Services (DFS) convened a high-level meeting with the RBI urging states to digitise land records and align sector-specific credit schemes with ULI, and the RBI’s Annual Report 2024–25 lists ULI’s expansion to business-to-customer (B2C) use cases as an FY26 priority.

Functioning

ULI is conceptualised by the RBI, with the Reserve Bank Innovation Hub handling design and technical stewardship. It operates as a “universal API gateway” intermediating between data providers (government departments, credit bureaus, account aggregators, NABARD) and lenders (banks and NBFCs). Rather than each lender separately negotiating access with each provider  a costly “many-to-many” arrangement ULI offers a single standardised interface that any onboarded lender can plug into once to draw on the entire pool of data services.

A borrower applying for a loan consents to specific data being pulled by the lender through ULI; the platform aggregates and routes it in real time, while the lender’s own underwriting engine takes the final decision. The RBI and RBIH function as infrastructure providers, while individual lenders bear integration costs and the responsibility for consent capture, data use and redress the RBI itself who does not store borrower data or manage consent centrally. It is a position that is confirmed in an RTI response reported by MediaNama in August 2025.

A key implementation channel is the e-Kisan Credit Card (e-KCC) platform run by NABARD, through which ULI has reached district central cooperative banks and regional rural banks institutions serving the last mile of agricultural credit but traditionally lagging in digital infrastructure. KCC loan processing, which conventionally took four to six weeks, has in several digitised journeys been compressed to under an hour, and in some accounts to around ten minutes for eligible borrowers. Table no. 1 summarises the platform’s institutional scale-up over roughly a year, based on the RBI’s Report on Trend and Progress of Banking in India.

MetricDecember 2024December 2025Change
Lenders onboarded3664 (41 banks + 23 NBFCs)+78%
Data services available~50136++172%
Loan journeys supportedFewer (expanding)12—
Cumulative loans disbursed6,00,000+Not separately republished as of writing—
Cumulative value disbursed₹27,000 croreNot separately republished as of writing—

Source: RBI, Report on Trend and Progress of Banking in India 2024–25; Business Standard, December 2024.

Implementation has not been friction-free. Several banks flagged difficulty integrating ULI into existing loan-origination systems, and adoption through much of 2025 was described as slower than the RBI anticipated, prompting direct reviews with lenders. A further concern is that while the RBI builds the pipes, data quality, particularly digitised land records, depends on state governments, whose digitisation levels and error rates vary considerably.

Performance

Assessing performance requires triangulating RBI’s trend and progress reports with independent commentary, since a granular public MIS dashboard is not yet available. Per the RBI’s December 2024 disclosure, of the roughly 6,00,000 loans and ₹27,000 crore disbursed via ULI, about 1,60,000 loans worth ₹14,500 crore went to MSMEs, a significant early share relative to agricultural credit. By December 2025, the lender base had grown nearly 78 percent year-on-year and live data services had nearly tripled, evidence of accelerating institutional participation, though updated transaction-level disbursement figures had not yet been separately republished at the time of writing.

The Economic Survey 2025–26 situates ULI within a broader digital-credit narrative. It noted UPI now handles around 15 billion transactions monthly, backed by over 80 crore smartphone users, and observed that trusted UPI transaction data is enabling banks and fintechs to assess creditworthiness, turning bank-account ownership into deeper financial engagement, especially in smaller towns. The fastest credit expansion occurred in areas with low internet costs and high account penetration, without a corresponding rise in default rates. While these findings pertain mainly to UPI-linked credit scoring, they reflect the same DPI logic underpinning ULI and suggest a favourable enabling environment.

Figure 1: How a loan moves through ULI 

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Source: Author’s own illustration, based on RBI, Report on Trend and Progress of Banking in India 2024–25

Sector-specific indicators add context: agricultural credit deepened in FY25, with ground-level credit reaching ₹28.69 lakh crore, KCC coverage at 7.72 crore accounts holding ₹10.20 lakh crore outstanding, and the share of non-institutional lending declining to 23.4 percent. Since KCC digitisation was ULI’s original testbed, this decline in reliance on informal moneylenders is a plausible, though not yet formally attributed, downstream benefit.

Table 2: Selected Agricultural and MSME Credit Indicators, FY25

IndicatorValue
Ground-level agricultural credit₹28.69 lakh crore
KCC accounts7.72 crore
KCC outstanding credit₹10.20 lakh crore
Share of non-institutional agri-lending23.4%
MSME credit gap (est.)~₹80 lakh crore
Formal credit access among MSMEs~14%

Source: Economic Survey 2025-26 (via IBEF)

The RBI Annual Report 2024–25 records ULI as an explicit FY26 priority, with Deputy Governor T. Rabi Sankar stating the platform “could surpass the transformative impact of UPI.” Formal parliamentary or CAG scrutiny of ULI specifically remains limited in the public domain, reflecting its young operational history.

Impact

ULI’s objective of converting fragmented data into a single rail for inclusive credit shows early progress along two dimensions: reduced turnaround time and broadened institutional participation. Shorter KCC processing and the extension of digital credit rails to cooperative and regional rural banks via NABARD’s e-KCC platform represent genuine efficiency gains for underserved borrowers previously reliant on paper-heavy processes. The rapid rise in onboarded lenders and data services between December 2024 and December 2025 indicates ULI has moved beyond proof-of-concept into live digital public infrastructure.

At the same time, independent analysis qualifies the inclusion narrative. MediaNama’s review, drawing on RBI’s Trend and Progress report and an RTI response, found the RBI does not manage consent, data-use boundaries, or grievance redress at the platform level these rest entirely with individual lenders, with no uniform standard.

This creates a meaningful distinction between access to credit and the terms on which it is offered: because ULI feeds land records, satellite imagery, and verification data directly into automated underwriting, gaps or legacy errors in any dataset may not cause outright rejection but can push a borrower into a higher-risk pricing tier, often invisibly. This shift described as one “from outright exclusion to pricing-based exclusion” complicates any straightforward claim that ULI has closed India’s credit gap; it may instead be reshaping the terms of inclusion.

A further consideration is data-quality dependency. The Ministry of Rural Development reported in October 2024 that 95 percent of rural land records had been digitised under the Digital India Land Records Modernisation Programme, with 68 percent of cadastral maps completed. However, land disputes account for a majority of India’s civil litigation, meaning digitisation does not eliminate underlying disputes; it formalises them within an automated lending pipeline, with limited borrower recourse if erroneous historical data feeds into a credit decision.

Emerging Issues

Several structural concerns have surfaced as ULI has scaled. Governance and accountability remain diffuse: the RBI has disclaimed a central role in consent management or grievance redress, leaving borrowers dependent on individual lender policies of varying maturity.

Data quality and legacy-error risk are significant, particularly for land records, where digitisation can entrench historical inaccuracies into automated risk scoring without a clear correction pathway. The RBI has not defined firm boundaries on the data categories lenders may draw upon in future; open-ended regulatory language leaves room for expansion into behavioural or alternative data already used elsewhere by fintechs, raising the risk of opaque, hard-to-contest credit decisions at scale. 

Integration friction with lenders’ existing technology stacks has slowed adoption relative to RBI’s expectations, particularly among smaller institutions and regional rural banks. Transparency around loan-level and state-wise disbursement data remains limited, constraining independent evaluation of whether the platform genuinely reaches underserved rural and MSME borrowers rather than simply channeling more credit through already-served urban segments. Finally, the RBI’s parallel framework for Responsible and Ethical AI in financial services remains under development, leaving a regulatory gap around algorithmic accountability in lending decisions built partly on ULI-sourced data.

Way Forward

Realising ULI’s ambition of matching UPI’s transformative reach in credit calls for several measures. Platform-level minimum standards for consent capture, data validity periods, and grievance redress even while operational responsibility stays with individual lenders would bring consistency to borrower protections without diluting the RBI’s light-touch infrastructure role. 

A regular, granular MIS dashboard with state-wise and sector-wise disbursement, rejection, and turnaround-time data would let researchers, parliamentary committees, and the RBI itself track whether the platform meets its inclusion objectives rather than merely scaling institutional participation. Accelerating and quality-auditing land-record digitisation at the state level, with a defined dispute-resolution mechanism before such records feed automated credit decisions, would reduce the risk of borrowers being penalised for legacy administrative errors.

Fast-tracking the RBI’s Responsible and Ethical AI framework and aligning it explicitly with ULI’s underwriting architecture would help pre-empt algorithmic bias before it becomes entrenched at scale. Finally, continued technical and financial support for cooperative and regional rural banks to integrate with ULI building on the NABARD e-KCC precedent is essential to ensuring the platform’s efficiency gains reach the last-mile rural borrowers it was designed to serve, rather than disproportionately benefiting larger, better-resourced lenders and their existing urban customer bases.

References

About the Contributor

Sruti Halder is pursuing an MSc in Economics at the Gokhale Institute of Politics and Economics. She is committed to leveraging data-driven research and evidence-based policymaking to promote inclusive and sustainable socio-economic development.

Acknowledgement 

The author expresses sincere gratitude to IMPRI (Impact and Policy Research Institute) for providing the opportunity to prepare this policy update article and for fostering a rigorous learning environment that connects research with public policy practice.

Reviewers: Kavin, Ameya Satam

Publisher: Pallavi Lad

Disclaimer: All views expressed in the article belong solely to the author and not necessarily to the organisation.

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