First they came for the emails, I did not speak out because I was not writing any emails. Then they came for the graphic designers, but I did not speak out because I was not a graphic designer. Now, they have come for my credit portfolio.
And this time, it isn't just jobs on the line, it's how risk itself is being calculated, priced, and distributed across India's banking system.
Indeed, artificial intelligence is fundamentally reshaping credit portfolios of Indian financial institutions, according to a recent report from Canara Bank Research, which paints a dual picture on how AI is permeating India's banking ecosystem.
On one hand, AI is accelerating credit portfolio improvements through better risk tools. This is backed up by numbers as well, with AI-driven portfolios showing 2x higher growth and return-on-asset potential.
On the other hand, they carry 1.5x to 2x higher credit risk, as per RBI, CRISIL and global rating benchmarks.
This can be seen through various lenses. On the opportunity side, AI-driven lending is transforming underwriting speed from 3-10 days to near-instant approvals. This, in turn, is fuelling an explosion in unsecured retail loans, BNPL and micro-credit.
The shift from CIBIL scores and financial statements to real-time data, including GST flows, UPI transactions and digital footprints, is expanding credit access to MSMEs, gig workers and then-file borrowers, who were previously excluded from the format credit system.
Where's the risk?
There are substantial risks involved in this rapidly changing credit ecosystem. The Canara Bank report notes that the country's $280 billion-plus IT/BPO sector faces immense threat from AI automation, with job losses in tech corridors liek Bengaluru, Hyderabad and Pune threatening to elevate NPas across retail and corporate credit.
The Economic Survey 2025-26 warned that AI risks to white-collar jobs could prove worse than the 2008 crisis in terms of ripple effects on banking.
Not to mention, AI model serves as a contraceptive for black-box decision-making. Bias in training data could potentailly lead to mis-pricing of risk and procyclicality, where these models tighten credit instantly in downturns, potentially triggering credit freezes.
Keeping that in mind, the report calls for the controlled adoption of AI with hybrid underwriting. It believes human oversight is essential to cap risks.
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