Use AI to reach credit invisibles: RBI DG Shirish Chandra Murmu to banks

Share of fresh businesses entering formal credit system fell to 42 per cent in FY26 from 52 per cent in FY23 even as outstanding commercial credit grew 14 per cent

The share of fresh businesses entering the formal credit system fell to 42 per cent in 2025-26 from 52 per cent in 2022-23, even as outstanding commercial credit grew 14 per cent, said Shirish Chandra Murmu, deputy governor, Reserve Bank of India (RBI), flagging that banks’ stronger data and analytical capabilities were not translating into greater reach among borrowers they had not previously served.

 

“This did not happen for want of information: lenders today have access to richer data, and to materially stronger analytical capability, than at any point before,” Murmu said at the CNBC-TV18 Banking Transformation Summit in Mumbai on Wednesday. “It points to something more structural — that our data and our technology may be getting better at serving those the system already understands, faster than they are developing the capacity to understand those it has never served,” he said.

 

 

Outstanding commercial credit stood at ₹65.8 trillion in 2025-26, according to data cited by Murmu.

 

He said banks should use alternative data and artificial intelligence (AI) to bring “credit invisibles” into the formal credit system.

 

Traditional lending has relied heavily on collateral, financial statements and credit bureau history, but banks now have access to cash flows, Goods and Services Tax filings, utility payments, e-commerce records, mobile usage, agricultural and geospatial data, Murmu said.

 

“This alternative data offers a genuine opportunity to bring ‘credit invisibles’ into the formal system, and AI can help close that gap,” he said.

 

Where a lender genuinely lacks reliable information about a borrower, the absence of information should not by itself be treated as negative information, Murmu said.

 

AI could help banks interpret information that does not arrive in conventional formats, he said. This could include an audio recording in which a borrower describes the purpose of a loan and the revenue of their business, an image of a crop, inventory or physical asset, or weather patterns that affect seasonal cash flows. Such information should be captured with consent, tested for reliability and bias, and used responsibly to strengthen human judgement, Murmu said.

 

Murmu said the Indian banking system was well-capitalised, and stress tests suggested that the banking system was well placed to absorb adverse shocks. However, he said the contribution of banking to economic growth should not be measured only by aggregate credit expansion or the size of bank balance sheets.

 

“It must also be judged by whom finance actually reaches, whether rising consumer expectations are met, and how banks support the wider economic activity,” he said.

 

Additionally, he said, the next phase of financial inclusion should deepen digital inclusion while ensuring that people who need assistance or other modes of access are not left behind. AI, Murmu said, could help the financial system recognise capability across differences in language, location, livelihood, gender and channel, and use that data prudently to make financial services more accessible.

 

Murmu also said productivity gains from AI should not be measured merely in terms of output per employee or the cost-to-income ratio. “If AI compresses costs without widening reach or improving the customer’s experience, we shall have automated the existing system rather than improved it,” Murmu said.

 

AI could also be used for pattern recognition by bringing together audit observations, incident narratives, customer complaints and system logs to identify connections that may not be visible to a single function, he said. This could help banks identify emerging weaknesses in fraud, conduct, operations or cyber risk before they become disruptions.

 

At the same time, banks would need to retain the judgement, capability and alternative arrangements required to intervene when AI systems fail or behave in unexpected ways, Murmu said.

 

He said AI could improve consistency, reduce processing time and identify patterns that conventional analysis might miss, but its performance in familiar circumstances may not carry over to new customer segments, new activities or changed economic conditions.

 

Murmu also flagged risks arising from the increasing use of common data sources, models, technology providers and infrastructure across financial institutions. “Where several institutions lean on the same data sources, the same models, the same technology providers or the same infrastructure, a single error or disruption can affect them all together,” he said.

 

He called for effective challenge, limits on undue concentration and credible alternatives.

 

As banks grow, their technology, processes and oversight capacity also need to keep pace, Murmu said. “Resilience, I would argue, should be built into the design of growth — not bolted on after the expansion has already happened,” he said.