MSMEs remain credit-starved despite multiple government schemes. Examine how AI-driven digital lending by public sector banks like SBI can bridge this gap.

RBI's Expert Committee on MSMEs (2019) placed the sector's credit gap at ₹20–25 lakh crore [1] — a shortfall that priority sector norms and guarantee schemes have narrowed but not closed. AI-driven underwriting addresses its root cause: information asymmetry.

Why the gap persists despite schemes

• Information asymmetry: informal bookkeeping and thin credit files make small firms unassessable by conventional appraisal.
• Collateral bias: despite collateral-free cover — CGTMSE and the Mutual Credit Guarantee Scheme, which guarantees 60% of credit up to ₹100 crore [2] — bankers still prefer secured exposure, and awareness among micro units is limited.
• High transaction cost: manual appraisal of a ₹10-lakh loan costs nearly as much as a ₹10-crore one, so branches ration small tickets.
• Delay: working-capital needs are immediate; multi-week sanction cycles push firms to informal lenders.

How AI-driven lending can bridge it

• Alternative data replaces collateral: SBI's in-house Business Rule Engine fuses GSTN filings, income tax returns, bank statements and bureau scores to underwrite cash flows rather than assets [3].
• Demonstrated scale: SBI underwrote nearly ₹1 lakh crore in MSME loans of up to ₹5 crore each in FY26, covering new-to-bank borrowers — precisely the excluded segment [3].
• Speed and cost: its SME Digital Business Loans sanction within 45 minutes, with ₹74,434 crore processed since launch [5], collapsing per-loan appraisal cost.
• Human bandwidth: relationship managers freed from data-gathering can focus on monitoring and recovery [3].

Caveats Enterprises outside the GST and ITR net remain invisible to algorithms; and opaque models risk bias and weak grievance redressal — concerns RBI's FREE-AI framework flags through auditability, fairness and accountability guardrails [4].

AI thus converts India's digital public infrastructure into collateral, shifting lending from asset-backed to cash-flow-backed. Paired with Udyam Assist for informal units, Account Aggregator consent architecture and FREE-AI safeguards, it can make formal credit both inclusive and accountable.