AI-native digital lending in India: what’s really changing
Aakriti spent eleven years as a credit underwriter until digital lending platforms changed what her mornings looked like. The silence felt strange at first. No sticky notes reminded her to cross-check GST filings against declared revenues for the textile merchant in Surat.
Her screen showed something she had never seen in a decade of lending work: twenty loan applications already processed, categorized, and waiting only for her final judgment on the cases that genuinely needed human expertise.
AI-powered systems had absorbed the mechanical parts of her job, and the change happened so gradually that she almost missed the transformation. The shift gave Aakriti something most underwriters rarely get—time to think.
A recent RBI assessment projects that generative AI could lift Indian banking productivity by up to 46 percent, with credit operations seeing the strongest gains. Financial institutions across India are discovering what Aakriti learned: artificial intelligence works best when it handles volume while humans handle nuance.
AI in digital lending: what’s actually different now?
Recent evidence shows that AI has moved into the core of credit decisioning. A fresh industry study of 109 Indian credit-risk leaders reports that lenders using machine learning see higher approval rates and better profitability; 79 percent say ML helps them serve new segments such as thin-file and new-to-credit customers.
Moreover, an industry conference highlighted that banking productivity in India rose by only about 1 percent per year over the last 15 years, and identifies AI and GenAI as the main route to break this cost and productivity ceiling.
A separate review of disclosures finds larger, better capitalised private banks leading AI adoption, which signals that AI is becoming part of their operating fabric, not an innovation side project.
How financial institutions are scaling intelligent credit
A large public-sector bank has deployed AI-driven risk and fraud systems across its retail franchise to match scalability. Consequently, it reported about 68 percent stronger risk prediction and operational efficiency, which reflects how such tools clean up portfolios while institutions expand outreach.
Similarly, a leading private bank uses data and machine learning across the SME lifecycle. It pulls information directly from verified digital public-infrastructure sources instead of requesting documents from customers.
Furthermore, a diversified retail NBFC has built a digital, ML-enabled engine that automates around 70 percent of loan approvals, with typical personal-loan turnaround time close to 24 hours.
When AI stops being a buzzword: inside the stack
Several fintech partners are now quietly wiring AI into the workflows of institutional lending. One platform uses domain-trained large language models to read bank statements, GST data, bureau files, and financials, and applies agentic AI to reconcile and triangulate these sources. Their stack can cut underwriting time by up to 85 percent for complex credit assessment memos.
Another partner embeds an AI assistant inside a loan-origination system that guides underwriters through policy, highlights anomalies in applicant behaviour, and recommends cross-sell offers.
ScoreMe’s AI layer for compliant, scalable digital lending
ScoreMe positions AI inside the decision core of lending institutions. Its Bank Statement Analyzer applies AI-based purpose identification and transaction annotation at scale. By turning unstructured bank lines into clear income, obligation, and behaviour categories, ScoreMe gives underwriters a sharper view of each borrower and portfolio in real time.