AI is transforming finance—accelerating loan decisions, streamlining underwriting, detecting fraud, and optimizing risk. But as IndiaToday warns in “When the Machine Says No: The Unseen Risks of AI in Finance”, this automation comes with serious caveats. Without careful design, AI can lock in bias—rejecting applicants based on historical inequalities in data sets rather than real risk. What’s more, opaque models often make decisions that individuals can’t understand or challenge.

Accountability becomes problematic when there’s no clear human oversight. Is it the bank, the AI provider, or some regulatory body that’s responsible when things go wrong? For many users, being denied a credit line, insurance, or essential financial service feels like being talked to by a black box: no explanation, no recourse.

Beyond individuals, there are systemic dangers. When many institutions rely on the same vendor, same model designs, or shared data sources, correlated errors or vulnerabilities could ripple across the financial system. AI systems that are poorly audited can widen exclusion—especially for marginalized populations whose data is scarce or misrepresentative.

The editorial urges financial services to go beyond chasing efficiency. It calls for transparency, fairness, better regulation, human access to appeal, constant model testing, and a value-driven approach so that the machine saying “no” doesn’t become a silent injustice.