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RBI Governor Sanjay Malhotra tells banks that AI trained on alternative data such as cash flows, GST filings, and utility payments can extend credit to borrowers that manual underwriting cannot currently assess
The pitch came bundled with caution, insisting that banks take safety and visibility measures including complete AI inventories, red-teaming before deployment, and the capacity to explain lending and fraud decisions
The RBI insists, however, that accountability stays human; banks, not their algorithms, will be answerable to customers, auditors, and the Reserve Bank when AI-driven decisions go wrong
The governor of the Reserve Bank of India (RBI) has told the country’s banks to put artificial intelligence at the heart of their lending patterns.
The approach is based on the assumption that models trained on unconventional data can identify and leverage data points that manual underwriters would struggle to spot or justify easily.
This would, as per RBI governor Sanjay Malhotra, both strengthen the credit market and enable financial inclusion by adding a new class of borrowers previously overlooked by conventional methods.
Cautious AI optimism from one of the world’s largest central banks
The Reserve Bank of India is not a small central bank by any measure, and remarks by its governor therefore can often shape not only domestic but global markets.
His position on unlocking credit markets for users with little or no financial history by adding raw compute that considers other signals such as cash flow, GST tax filings, utility payments, and one’s digital footprint considerably changes the landscape in a part of the world where banks are traditionally more conservative than their global peers when it comes to lending.
Malhotra also noted that this is exactly the data that a first-time borrower, a gig worker, or a small enterprise without formal books lacks, even as AI enables better monitoring of debtors’ financials.
“Predictive models can identify borrowers on the cusp of default early enough to counsel rather than merely recover,” he added. “Used well, AI may be the most powerful accelerator to financial inclusion.”
It is important to point out Malhotra wanted AI to be used as an assistant here – not that AI should overrule loan officers on applications they have already rejected.
His claim is narrower and arguably more persuasive: entire categories of borrowers are effectively invisible to conventional credit assessment because the paperwork it requires does not exist, and machine learning, over other data, can make those borrowers assessable to banks and vice versa.