How AI is rewriting India’s credit story

India has spent the last decade building one of the world’s most sophisticated digital public infrastructures for finance. The next decade will not be defined by digitising lending but will be defined by making lending intelligent. Artificial intelligence has the potential to fundamentally reshape who gets credit, how risk is assessed, and how quickly financial opportunity reaches millions of Indians.

This is more than automation. AI is changing the economics of lending by reducing the cost of evaluating borrowers, enabling lenders to responsibly serve customer segments that were previously difficult to assess.

India’s credit story is entering a new phase.  But the more interesting shift lies in understanding who is finally getting a slice of it. Large numbers of creditworthy Indians still sit outside the formal credit system because traditional scorecards simply don’t have enough data to assess them. This is the gap that AI-powered lending is built to close.

Rewriting the credit checklist

Traditional underwriting was built for salaried borrowers in the formal economy, relying on documents such as payslips, bank statements, and credit bureau scores. It works well for people who already have a credit history but fails, almost by design, for everyone else—including self-employed individuals, gig workers, and first-generation entrepreneurs.

AI-powered underwriting takes a different approach. It draws on alternative data such as digital payment transactions, GST filings, bank statements, and utility payments, converting everyday digital footprints into dynamic risk profiles.

The infrastructure to support this shift is now in place, and it is scaling fast. The Reserve Bank of India's Account Aggregator framework enables consent-based, secure sharing of financial data across institutions, reducing documentation requirements and loan turnaround times. As of December 2025, the ecosystem had enabled over 2.6 billion financial accounts for data sharing, with more than 252 million user-linked accounts, reflecting rapid adoption while still leaving significant room for further penetration. 

The Unified Lending Interface is doing the same for loan origination: 64 lenders, including 41 banks and 23 NBFCs, were live on it by December 2025, up from just 36 a year earlier, drawing on more than 136 data services—from digitised land records to satellite imagery—across a dozen loan journeys. 

Combined with near-universal digital identity and payments infrastructure, AI models can now build a real-time picture of a borrower’s financial behaviour.

The results are already visible in underwriting economics. Loan decisions that once took days are increasingly being made in minutes. That shift is reflected in real lives: a first-generation woman entrepreneur can take another step towards building her business, while a gig worker can finally finance a second-hand scooter to expand his earning potential.

A broader market

India’s credit expansion is no longer just existing borrowers taking bigger loans—it is pulling entirely new categories of borrowers into the formal system, changing what is financially possible for them.

Women’s participation in formal credit illustrates this shift well. According to a joint TransUnion CIBIL–NITI Aayog (WEP) report published in April 2026, credit penetration among women nearly doubled between 2017 and 2025, rising from 19% to 36%; women now hold a ₹76 lakh crore credit portfolio—26% of total system credit, and 4.8 times what it was in 2017.

Behind that growth is a very specific kind of borrower. A woman running a home-based tailoring unit or a small food business often has no property to pledge and no salary slip to present to a bank. What she does have is a steady stream of UPI receipts from customers, GST filings if her business is formalised, and a track record of repaying smaller loans on time. AI-based underwriting can interpret this information and extend a working capital loan that a traditional, checklist-driven process might have declined.

Gig work tells a similar story, but at a much larger scale. India’s gig workforce, according to NITI Aayog and the Economic Survey 2025–26, grew by 55%, from 7.7 million workers in FY21 to 12 million in FY25. Yet income volatility has kept many gig workers outside the formal credit system because their earnings do not resemble the fixed monthly salary that conventional underwriting models are designed to assess.

The very data that banks have historically overlooked—daily earnings, trip or delivery completion records, and transaction histories—is exactly what AI models are designed to analyse. In practice, this enables a delivery rider to finance the scooter that doubles his daily trips, or a cab driver to refinance an ageing vehicle loan at a better rate because platform earnings are recognised as evidence of repayment capacity, rather than just a bank balance.

Geography is shifting in a similar direction. Credit demand is no longer concentrated in metro India. Smaller towns and semi-urban markets are opening to formal credit for the first time, often through vernacular apps and low-documentation journeys enabled by Aadhaar-based e-KYC and Account Aggregator consent flows. For a first-time borrower in a Tier III town, this can mean the difference between weeks of bank visits and paperwork and receiving a lending decision on a smartphone within minutes, based on data the borrower has already chosen to share.

Taken together, these are precisely the segments where AI-based underwriting has the greatest potential. New-to-credit borrowers may be thin on formal documentation but are increasingly rich in digital behavioural data. Research on fintech lending elsewhere has a name for this cohort—invisible primes—borrowers who are genuinely creditworthy but unscored by traditional bureau-based models. 

AI doesn't just process applications faster, it makes previously invisible borrowers visible to the credit system. In doing so, it opens doors that were previously closed, whether to a first loan, a larger loan, or financing on fairer terms than would otherwise have been possible.

Beyond the approval

Underwriting is the most visible use case, but it is far from the only one. The RBI’s Master Direction on KYC treats Video-based Customer Identification Process (V-CIP) as legally equivalent to a face-to-face customer identification process, provided prescribed standards are met. The framework requires V-CIP systems to incorporate face liveness/spoof detection and face-matching technology and expressly permits and encourages the use of AI to strengthen the robustness of these checks. 

The same layer of AI is being turned on document fraud: Optical Character Recognition technology and forensic image models that catch altered PAN or Aadhaar scans, forged salary slips, and synthetic identities built to pass a single database check but not a cross-referenced one. As deepfake-based KYC attempts have grown, this detection layer has become as important to lending safely as the credit model itself.

AI's role does not end at disbursal. Early-warning systems now track a live borrower's transaction patterns, GST filings, and repayment behaviour for signs of stress—falling account balances, bounced payments, a sudden drop in turnover—well before a loan would traditionally be flagged as at risk. Indian banks and NBFCs are already running such systems in production. 

The value is straightforward: a lender who can see stress building has room to restructure a loan or tighten a credit line before it becomes a write-off, rather than discovering the problem only when the account turns into an NPA.

On the other end of the loan’s life, AI is reshaping collections. Instead of contacting every overdue borrower with the same call or SMS, lenders are using models that predict which borrower is likely to pay if reminded gently, who needs a phone call, and who needs to be escalated to a recovery agent—matching the contact method and timing to the borrower instead of working a flat list. Done well, this is also fairer to borrowers: someone who missed a payment because a customer paid them late is treated differently from someone who has gone silent.

The same data that decides whether to approve a loan is increasingly deciding its price. Risk-based pricing—charging a lower rate to a lower-risk borrower rather than a flat rate across an entire product category—was always the theory of lending; AI is what makes it practical at the scale of millions of small-ticket loans, because it can price each file individually instead of sorting borrowers into a handful of broad buckets.

The next frontier: Agentic AI

The next phase of AI in lending will move beyond decision support to autonomous workflow orchestration. AI agents will increasingly collect documents, verify identity, compare lender policies, prepare applications, coordinate approvals, communicate with customers and update internal systems, while humans focus on exceptions and oversight. For lenders, this promises lower operating costs, faster turnaround times, and a more consistent experience.

The real test ahead

AI-powered lending isn’t a separate, futuristic chapter in India’s credit growth story. It is rapidly becoming the mechanism through which the next phase of growth will be delivered.

The headline credit numbers point to a market expanding at a healthy pace. The deeper transformation explains why. More women, more first-time borrowers, and more self-employed, and gig-economy workers are entering the formal credit ecosystem because lenders can finally assess and price risk for people the traditional system couldn’t.

The real opportunity is not simply to make lending faster or more efficient. It is to redefine who gets access to credit in India. AI can transform lending at scale only when it is built on trust, with privacy-first data practices, explainable decision-making, and strong regulatory alignment at its core. If the industry gets these fundamentals right, AI-powered lending will not only accelerate India’s credit growth but also bring millions of underserved yet creditworthy Indians into the formal financial system.

Closing thought

India’s next credit revolution will not be measured by how quickly loans are approved. It will be measured by how many previously invisible borrowers become visible to the formal financial system. AI is not merely accelerating lending—it has the potential to democratise access to credit while keeping trust and responsible lending at the centre.

The author is CEO of rupyy, CarDekho Group’s fintech business.