Model Bias

Reserve Bank of India Governor Sanjay Malhotra told banks at the FIBAC conference in Mumbai on August 11 to use AI to extend credit to first-time borrowers, gig workers, and small businesses that lack formal financial records. The pitch: models trained on cash flows, GST filings, utility payments, and digital footprints can reach borrowers that manual underwriting never could, and do it cheaply. India has hundreds of millions of underbanked residents and rural literacy below 80%, so the case for AI-powered voice interfaces in local languages is, at least, grounded in a real problem.

Malhotra was not simply handing banks a blank check. He named the risks plainly — opaque decision-making, embedded bias against certain communities and occupations, and systemic fragility if a small cluster of AI providers all ship flawed models at once. His sharpest line was also his clearest rule: "'The model decided' can never be an acceptable answer to a customer, an auditor, or the Reserve Bank." Banks were told to inventory every AI system they run, red-team it before deployment, and keep humans able to explain, intervene, and override any decision that could cause material harm.