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Why India’s Credit System Is Failing Creditworthy Borrowers — And How Alternative Data Can Fix It

Why India’s Credit System Is Failing Creditworthy Borrowers — And How Alternative Data Can Fix It

Millions of Indians are denied loans despite strong incomes and repayment capacity due to outdated credit underwriting models. Experts say blending traditional credit scores with alternative behavioural data is key to building a more inclusive credit ecosystem.

A loan rejection despite a high salary and an excellent credit score is no longer an exception in India’s lending ecosystem. In one such case, an applicant earning ₹1.3 lakh per month with a credit score of 774 was denied a personal loan simply because their employer did not feature on the bank’s internal list of “approved companies.” The rejection had nothing to do with repayment risk and everything to do with rigid underwriting rules.

This example highlights a deeper structural issue in India’s credit system. Despite significant progress in financial inclusion, millions of individuals remain either credit unserved or underserved. Many are unaware of their credit score, while others are not adequately captured by traditional credit bureaus at all. Studies suggest nearly half of Indians have never checked their credit score, often due to the misconception that frequent checks reduce the score. As a result, borrowers frequently learn about poor or non-existent credit histories only after their loan applications are rejected.

The challenge is particularly pronounced among young borrowers. According to a TransUnion CIBIL report, nearly 30% of Indians aged 21–30 have a credit score below 650. In many cases, this is not due to defaults or missed payments, but because they lack any formal credit history. Compounding the issue, Global Findex data shows that 16% of Indian account holders do not have active accounts, deepening the problem of credit invisibility.

When formal lending institutions turn away these borrowers, they are often forced to rely on informal credit sources. These channels typically come with higher costs, weaker consumer protections, and the risk of long-term debt traps. Data from the Centre for Monitoring Indian Economy (CMIE) underscores the gravity of the issue, showing a 4.2% contraction between 2018–19 and 2022–23 in the number of economically weaker borrowers accessing formal credit annually.

This raises a critical question for the lending industry: are traditional credit scores sufficient to assess creditworthiness in a rapidly evolving economy? While bureau scores remain valuable, they offer only a partial view of a borrower’s financial behaviour. Over-reliance on them risks excluding a vast pool of capable borrowers.

The solution lies in augmenting traditional credit assessments with alternative data. Insights drawn from income flows, bank transactions, digital wallet usage, utility bill payments, and other behavioural signals provide a richer, more dynamic picture of financial responsibility and intent. For borrowers with thin or non-existent credit files, such data can bridge the information gap.

Alternative data-driven underwriting models are already demonstrating impact. Technologies leveraging large-scale behavioural datasets are enabling lenders to approve loans they previously could not assess confidently. For instance, models trained on millions of borrower data points have helped increase loan approvals for new-to-credit customers by over 25%, while maintaining risk discipline.

The future of lending in India does not lie in choosing between bureau scores and behavioural data, but in combining both. A holistic underwriting approach—one that blends the reliability of traditional credit scores with the depth of alternative data—can unlock inclusive growth and ensure credit access is determined by ability and intent to repay, not outdated filters.

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