India’s micro enterprises sector, comprising 98% of MSMEs, remains on the margins of formal finance, facing a massive credit gap of ₹30 lakh crore, where the supply of formal credit meets only a fraction of the actual demand. Women-led enterprises are hit hardest, as they get 35.5% less credit relative to deposits compared to men's 58%, despite data consistently showing they are more reliable borrowers with better repayment rates. Alarmingly, research indicates that over 85% of women entrepreneurs face significant barriers in accessing loans from public sector banks, exposing unconscious biases in how we assess creditworthiness and inclusivity in the financial system. Union minister of state for MSME has stated in Parliament on December 2, 2025 that 2.64 crores women lead units with a total of 6.69 crore Enterprises including Informal micro enterprises were registered on Udyam Registration Portal since its launch in July 2020. The minister further advised that 40,000 units were closed and deregistered during the last financial year with majority from Maharashtra, Tamil Nadu, Gujarat, Rajasthan, and Karnataka.
The current credit system still hinges on collateral, formal banking records, and documented income. These are criteria that often exclude the country’s microenterprises, many of which operate from homes or as single-person ventures without formal records. As a result, over 80% of micro and small businesses remain outside formal finance, turning instead to informal lenders charging interest rates above 24% annually. Credit bureaus largely focus on salaried workers, leaving microentrepreneurs as thin-file borrowers. While SHGs and JLGs have bridged some gaps, rising NPAs (16%, up from 8.8%) strain smaller loans under ₹50,000. Additionally, the National Industries Research and Development Council (NIRDC) has launched InDApp, a unified single-window digital platform to streamline MSME growth.
Behavioural data, in this context, reveals the digital footprints microentrepreneurs leave daily through payment patterns, transaction frequency, supplier referrals, and bill payment regularity, signaling discipline and reliability. For instance, a rural Odisha vendor's timely stock payments via UPI, or a home tailor's consistent/regularly issued and realised digital bills, give a real-time picture of cash flow management and responsibility. Global pilots show the strength of this approach. Platforms in Southeast Asia that use alternative data have recorded drops in default rates ranging from 7-32%.
Learning from global case models would serve India well. The UK's Competition and Markets Authority (CMA) established the Open Banking Implementation Entity (OBIE), which mandated standardised, secure APIs for banks to share account and transaction data uniformly. This ensured near-perfect uptime and enabled fin-techs to develop instant loan approval systems using detailed transaction histories, even for customers without formal credit records. It changed the UK lending landscape with rich innovation and competition. Here are five ways we can solve this.
- Thin-file inclusion: Over 53 million Indians lack formal credit records, yet many leave digital traces daily. A home-based cook selling via a messaging app, paying digitally, and gaining referrals builds trust unseen in formal systems.
- Gender gap: Women-led enterprises face a 35% higher credit shortfall than men, though they default less. Behavioural data enables a fair chance at financial independence, which is linked with higher education levels for daughters and better health outcomes.
- Real-time monitoring: Behavioural data offers continuous insights, flagging stress when payment success or transaction volume drops, provided robust, real-time systems access relevant financial flows.
- Speed and accessibility: Manual checks on collateral, and group formation, take one to two months. For microentrepreneurs who depend on daily cash cycles, this delay is harmful. Behavioural data allows screening within hours.
- Accurate risk segmentation: A street vendor with 200 small, successful digital transactions monthly presents a different risk profile from one with fifty irregular transactions, allowing lenders to price fairly and intervene early when signs of stress emerge.
Learning from global case models would serve India well. The UK's Competition and Markets Authority (CMA) established the Open Banking Implementation Entity (OBIE), which mandated standardised, secure APIs for banks to share account and transaction data uniformly. This ensured near-perfect uptime and enabled fin-techs to develop instant loan approval systems using detailed transaction histories, even for customers without formal credit records. It changed the UK lending landscape with rich innovation and competition. Here are five ways we can solve this.
- Thin-file inclusion: Over 53 million Indians lack formal credit records, yet many leave digital traces daily. A home-based cook selling via a messaging app, paying digitally, and gaining referrals builds trust unseen in formal systems.
- Gender gap: Women-led enterprises face a 35% higher credit shortfall than men, though they default less. Behavioural data enables a fair chance at financial independence, which is linked with higher education levels for daughters and better health outcomes.
- Real-time monitoring: Behavioural data offers continuous insights, flagging stress when payment success or transaction volume drops, provided robust, real-time systems access relevant financial flows.
- Speed and accessibility: Manual checks on collateral, and group formation, take one to two months. For microentrepreneurs who depend on daily cash cycles, this delay is harmful. Behavioural data allows screening within hours.
- Accurate risk segmentation: A street vendor with 200 small, successful digital transactions monthly presents a different risk profile from one with fifty irregular transactions, allowing lenders to price fairly and intervene early when signs of stress emerge.
Much of the lending discourse around alternative data remains superficial, limited to basic metrics. Some institutions have begun exploring deeper models. SBI, for instance, has introduced a home loan product based on borrowers’ digital footprints. With over 490 million UPI users, India holds immense potential, yet user and transaction counts reveal only part of the story. The next step is deeper behavioural analytics.
We must encourage microentrepreneurs to build strong behavioural credit profiles through regular UPI business transactions, timely bill payments, and e-commerce/quick commerce usage that captures order fulfillment behaviour. Regulators must issue guidance recognising behavioural data as a valid alternative to traditional credit history for loans below ₹50,000, where collateral assessment is impractical.
India's account aggregators must expand/enhance their systems to include behavioural signals from non-bank platforms like delivery apps and payment providers, with customer consent.
India is uniquely positioned to pioneer a behavioural-data-driven microenterprise credit system. We have a strong digital base, a microfinance legacy that understands social collateral and group dynamics, a regulatory framework open to experimentation and, most importantly, we face a pressing credit gap that demands new solutions. A behavioural data-led approach can unlock opportunities for millions of microentrepreneurs and bring trust, fairness, and dignity to their financial lives.
This article is authored by Lakshmi Venkataraman Venkatesan, founding and managing trustee, Bharatiya Yuva Shakti Trust.