When AI gets lending wrong, who takes the fall?
When AI becomes involved in lending decisions, the question of accountability arises. India's financial data moves through the system at lightning speed, making human oversight increasingly unnecessary. But when errors occur and a lending decision is based on faulty or biased data, who bears the consequences? Current lending processes rely heavily on software to judge the data, but there is no clear accountability mechanism in place.
The report suggests a framework with 23 factors to evaluate data intelligence products, but measuring these factors is not always straightforward. The report also highlights biases in models that prioritize formal banked customers over those without traditional banking arrangements. While some underwriters are being replaced by automated workflows, the industry is not yet ready to fully trust algorithms for complex cases.
The goal is to set standards that human underwriters must meet and catch exceptions. As AI becomes more prevalent in lending, buyers must demand better explanations and refuse subpar products.
Written by urgent.news from The Economic Times's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
