India's MSME Credit Gap: How AI Can Help Close It

India's MSME sector employs over 110 million people and contributes approximately 30% of GDP. Yet the formal credit penetration of this sector remains strikingly low — estimates of the formal credit gap range from ₹20–25 lakh crore, representing the difference between what MSME businesses need to grow and what the formal financial system currently provides.

The MSME credit gap is not primarily a demand-side or supply-side problem in the traditional sense. Lenders are willing to lend. Borrowers want to borrow. The gap exists because the analytical infrastructure required to assess risk responsibly — at the scale and ticket sizes the MSME sector requires — has historically been too expensive to build and operate.

The traditional credit assessment model relies on collateral (which MSMEs often lack), audited financial statements (which most MSMEs don't produce), and bureau credit history (which many first-time MSME borrowers don't have). Remove these three pillars and the conventional risk framework has no foundation.

The insight that's reshaping MSME lending is that while traditional data is absent, alternative data is abundant. Every formal MSME business has bank accounts — and 2–3 years of bank statement history is a remarkably rich source of credit signal. Many are GST-registered — providing another layer of business activity data. Most have some transaction footprint through UPI, payment gateways, or GST invoicing that creates a verifiable activity history.

The challenge has been converting this raw alternative data into credit signals that are reliable, auditable, and actionable at scale. This is precisely the problem that AI-powered financial analytics is designed to solve.

The key word is responsible. There have been well-publicised examples of fintech lenders extending credit to MSMEs on the basis of thin alternative data signals without adequate underwriting discipline — and experiencing predictable portfolio deterioration. The lesson isn't that alternative data doesn't work; it's that alternative data analysis needs to be as rigorous as traditional analysis, not less.

Responsible MSME credit using alternative data means: reconstructing cash flow statements from bank data (not just summarising inflows), understanding income regularity and business seasonality (not just average income), detecting all existing obligations (not just bureau-reported ones), and flagging behavioural risk signals (not just hard defaults).

Closing India's MSME credit gap requires the entire lending ecosystem to upgrade its analytical infrastructure. That means lenders adopting bank-statement-native underwriting workflows, regulators providing clear frameworks for alternative data use in credit decisions, technology platforms providing the analysis infrastructure as a service, and progressive standardisation of the output formats and risk signals so that downstream models can use them consistently.

Santulan's mission is to be part of this infrastructure — providing the analysis layer that converts raw financial documents into credit-ready intelligence, at the scale and accuracy that responsible MSME lending requires.