Leading Indian Conglomerate Lender (unnamed)

India's SHG lending ecosystem serves over 9.1 million self-help groups and approximately 100 million women, making it one of the largest rural credit networks globally. For this leading Indian conglomerate lender—managing ₹50,000 crore in SHG credit across 30,000 branches—the operational infrastructure had not kept pace with portfolio scale. Approval cycles ran 10–15 days due to paper-heavy onboarding and inconsistent KYC processes. Without cross-bureau checks, hidden group overexposure accumulated silently in the portfolio, while manual field collections created recovery leakage. These compounding inefficiencies generated direct liquidity drag, elevated NPA exposure, and undercut trust in a credit segment where repayment discipline and regulatory compliance are foundational to viability.

The lender deployed ezee.ai's no-code loan origination system (LOS), purpose-built for SHG operations and rolled out across its 30,000-branch network without custom engineering. The platform embedded machine learning and predictive analytics at every credit touchpoint: eKYC/CKYC/Aadhaar-based digital onboarding replaced paper workflows, while cross-bureau de-duplication and rule-based exposure limits blocked overexposure at origination. Predictive risk scoring continuously surfaced early warning signals across the live portfolio, enabling proactive intervention before delinquencies materialized. Collection workflows were automated via UPI/NACH with attendance-linked disbursals, geo-tagged field execution, and purpose-tagged loan tracking. Live risk dashboards with geo-mapping gave portfolio managers real-time visibility into group health and field activity. The no-code architecture allowed RBI/NABARD compliance configurations—including CKYC and Aadhaar verification—to be deployed at national scale without prolonged engineering cycles.

The transformation compressed approval cycles from 10–15 days to 2–3 days, directly unlocking ₹1,200 crore in annual liquidity across rural markets. Collections stabilized at 95%+, sustained by automated digital reminders and UPI/NACH payment rails tied to group attendance discipline. Key outcomes across the ₹50,000 crore portfolio:

• NPAs reduced 15–20% through cross-bureau de-duplication and real-time scoring that pre-empted ~25% of potential delinquencies at origination
• Staff productivity rose 2–3x as unified dashboards replaced manual reconciliation bottlenecks
• Per-loan field costs fell 25–30% via mobile-first execution and geo-tagged verification
• 100% RBI/NABARD compliance achieved with CKYC, Aadhaar, and geo-tag audit trails

• Embedding cross-bureau de-duplication and predictive risk scoring at origination—before disbursement—can intercept a material share of delinquencies that manual review misses at portfolio scale.
• UPI/NACH automation tied to attendance-linked disbursals tightens the collection loop without increasing field headcount, improving both cost efficiency and repayment rates simultaneously.
• No-code LOS platforms can absorb complex regulatory requirements (RBI/NABARD) at national scale by treating compliance as a configuration parameter rather than an engineering rebuild.
• Geo-tagged field execution paired with live portfolio dashboards reduces information asymmetry between central risk teams and distributed branches—a critical control layer for large, geographically dispersed SHG books.