Micro, small, and medium enterprises (MSMEs) have historically been underserved by formal banking channels. Traditional underwriting models rely on financial statements and credit bureau scores—data that many small businesses simply do not have. AI is changing this by enabling lenders to assess thin-file customers using alternative data sources, expanding financial inclusion while managing risk effectively.
State Bank of India (SBI) used AI and digital data to underwrite nearly ₹1 trillion of loans of up to ₹5 crore each to MSMEs in FY26. The bank's underwriting process draws on the digital footprint available for MSMEs, including GST Network and filing data, bank-account information, credit-bureau scores and other structured and unstructured information. The bank combines these datasets through its business rule engine (BRE) to assess borrowers. For a detailed look at this landmark deployment, this Mint report on SBI's AI underwriting covers how the country's largest lender is using AI to transform small business lending.
In unsecured lending, SBI uses AI to assess thin-file customers such as small businesses and proprietorships, where limited information may be available beyond account and UPI data. This is helping the bank expand financial inclusion while meeting priority-sector lending requirements. The AI models can ingest consented alternative data—UPI transaction history, GST filings, account behavior—to build a credit profile for borrowers who would otherwise be excluded from formal credit. This is the practical application of the "See" role of AI in financial services: detecting patterns in data that humans cannot see at scale.
The impact on underwriting speed and accuracy has been significant. The AI has shifted work away from relationship managers, who previously spent significant time collecting information and conducting preliminary analysis. "It has actually freed the bandwidth of the people who were otherwise the relationship managers," said SBI Managing Director Rama Mohan Rao Amara. This bandwidth freed up allows relationship managers to focus on higher-value activities—building relationships, advising clients, and managing complex cases.
The bank has also seen lower delinquency in the portfolio underwritten through the BRE. For existing borrowers, SBI uses AI to identify vulnerable exposures before conventional warning signs such as days past due (DPD) emerge. The models can ingest market- and sector-specific information and other publicly available data to generate early-warning signals, allowing the bank to intervene proactively. This is the "Act" role of AI: acting inside limits to prevent problems before they occur.
How to use ai in finance is a sequence, not a lab day. Begin with data-heavy, auditable activity (reconciliation, support drafts, document review, MSME pre-underwriting). Keep generative models grounded. Never make an inexplicable score the sole reason a loan is accepted or denied. The role of AI in financial services is four jobs: See—fraud patterns, AML alerts, document fields; Decide—with rules: credit policy engines, limit checks, routing; Write and talk—copilot memos, customer replies, call summaries; Act inside limits—post a ticket, request KYC, schedule a callback. For a practical implementation framework covering MSME lending, this guide on how to use AI in finance and banking services provides the sequence that works.
The business case for AI-powered MSME lending is compelling. Industry estimates place the annual banking value of generative AI globally in the low hundreds of billions of dollars. For a detailed exploration of specific use cases delivering real results, this Backbase guide to generative AI in banking covers ten applications that are producing measurable outcomes in 2026, including personalized product recommendations and conversational banking assistants. The lessons from SBI's MSME lending transformation are applicable across the financial services industry.