Many banks and microfinance companies claim that they treat everyone equally. But research shows something very different. Even when people from different castes have the same income, same land, and same education, a big part of the difference in who gets loans cannot be explained.
This unexplained gap is because of statistical discrimination — lenders assume someone is “high‑risk” just because of their caste, not because of their actual financial situation.
This problem is even worse in microfinance. Studies show that about 66% of Dalit applicants are rejected, even when they meet the same conditions as others.
There is also a difference between how Scheduled Castes (SCs) and Scheduled Tribes (STs) are treated.
SCs face general discrimination.
STs face a special kind of bias: even when they apply, their approval rate is only around 77%, while other groups get 85–88% approval.
A lot of this discrimination becomes hidden inside algorithms used by lenders. These systems are supposed to remove human bias, but they are trained using data entered by loan officers who may already have caste prejudice. So the algorithm ends up copying and strengthening the same bias, while making it look “objective.”
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