AI-Based Lending Models Can Bridge USD 130170 Bn MSME Credit Gap: Govt

(MENAFN- KNN India) New Delhi, May 15 (KNN) Artificial intelligence (AI)-driven lending models have the potential to close an estimated credit gap of USD 130–170 billion for India's micro, small and medium enterprises (MSMEs), according to a government release issued this week.

The PIB backgrounder noted that AI-based credit systems are reshaping India's lending landscape by extending formal credit access to MSMEs, informal workers, and first-time borrowers who typically lack conventional credit histories.

Rather than relying solely on CIBIL scores, AI-powered models assess creditworthiness through alternative data - including digital payment transactions, GST filings, bank statements, and utility bills - while also reducing MSME dependence on informal lending channels.

Digital Infrastructure as the Backbone

India's expanding Digital Public Infrastructure (DPI), comprising Aadhaar, Jan Dhan accounts, UPI, and the Unified Lending Interface (ULI), is building the digital ecosystem required for AI-led financial inclusion, the release said.

The ULI enables lenders to access multiple data sources - including land records, satellite data, authentication services, and other financial and non-financial datasets - to support faster and more inclusive loan processing.

As of December 2025, 64 lenders, comprising 41 banks and 23 non-banking financial companies (NBFCs), have been onboarded onto the ULI platform, which already supports over 136 data services across 12 loan categories.

Account Aggregator Framework Gains Scale

The backgrounder also highlighted the growing role of the Account Aggregator (AA) framework in enabling consent-based financial data sharing. With over 2.6 billion accounts enabled for data sharing, approximately 252.9 million users have linked their accounts on the AA framework to date.

AI in Fraud Detection

Beyond credit access, AI is also being deployed in financial security. The RBI Innovation Hub's MuleHunter initiative is specifically designed to detect mule bank accounts associated with cybercrime and money laundering, the release noted.

(KNN Bureau)

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