Think360.ai’s Alternate Data Study Indicates Credit Risk Signals among Low-Income Indians

Think360.ai, a CAMS company and a leading data science and AI firm enabling smarter credit decisions for India’s top BFSI institutions, has released a new study uncovering early signs of mounting credit stress among low-income earners. The report reveals that over 70% of individuals earning less than ₹25,000 per

month are concurrently servicing multiple small-ticket loans, a pattern that is increasingly correlated with rising EMI defaults across both salaried and self-employed segments.

The study, based on a year-long analysis of over 20,000 borrowers using Think360.ai’s Algo360 platform, found that 64% of self-employed and 76% of salaried individuals earning under ₹25,000 per month have missed at least one EMI underscoring the deepening financial strain among low-income segments.

The report, which leverages alternate data signals such as, spending patterns, behaviour, and repayment activity, draws a strong correlation between income stability and borrowing behaviour.

“Borrowers in this segment aren’t financially irresponsible; they’re financially constrained,” said Amit Das, Founder and CEO, Think360.ai. “Multiple concurrent loans, without the guardrails of enriched risk intelligence, raise default probabilities. The solution lies in contextual, AI-powered credit evaluation underpinned by alternate data. That’s exactly what Algo360 was built to do, help lenders go beyond traditional bureau scores to uncover the real credit story.” he added.

As traditional credit scores often fail to reflect the financial realities of underserved segments, platforms like Algo360 are increasingly being used to provide a more complete borrower profile. By integrating alternate data such as transaction behaviour, spending patterns, and repayment trends, Algo360 supports more inclusive and accurate credit assessments, especially for thin-file borrowers and those outside formal credit systems.

The report urges lenders, especially banks, NBFCs, and fintechs to adopt segment-specific, behaviour-driven underwriting frameworks that extend beyond traditional credit scores. Platforms like Algo360, which fuse alternate data, real-time transaction intelligence, and credit bureau insights, enable more inclusive and precise borrower profiling critical for customers with thin files, irregular income, or informal credit footprints.

This behavioural divide also presents strategic risk segmentation opportunities for lenders. On average, salaried borrowers hold three active loan accounts, while self-employed individuals manage four with a higher tilt toward informal and collateralised credit products. These diverging profiles signal the need for tailored risk monitoring frameworks and adaptive credit lifecycle strategies.

The need for timely intervention is amplified by macroeconomic headwinds. Salaried Indians now allocate over 33% of their monthly income to EMIs, according to recent studies. In this context, designing behaviour-responsive, cash flow-aligned credit products becomes central to preserving borrower health, advancing financial inclusion, and safeguarding financial system resilience.