The research, carried out with D91 Labs, exhibits how digital lenders are utilizing cell data, SMS information, and GST transaction histories to bridge the credit hole for underserved and new-to-credit (NTC) debtors.
The report, “State of Alternative Data in India – The Lending Use-Case,” highlights a significant shift within the
lending ecosystem.
One in 4 lenders now depend on alternative data for over 75% of their mortgage books.
About 70% of establishments surveyed use GST and transactional data to judge creditworthiness.
“Alternative data, when paired with knowledgeable buyer consent, helps enhance entry to formal credit and helps accountable lending,” stated Sugandh Saxena, CEO of FACE.
She added that the pattern aligns with India’s push for digital monetary inclusion.
Practically 54% of fintech NBFCs’ portfolios consist of prospects with low credit bureau historical past—double the share seen at traditional banks and NBFCs.
This section advantages from data similar to cell funds, expense patterns, and employment data.
Fraud prevention is one other main use case.
Round 77% of lenders use alternative data to cut back fraud and non-performing property (NPAs) in absolutely digital mortgage journeys. Lenders cite FOIR (Fastened Obligation to Earnings Ratio), money circulation, and behavioral data as key danger metrics.
The ecosystem is shortly embracing automation.
The report finds that 96% of respondents have automated their credit evaluation processes, and 60% work with third-get together service suppliers for data enrichment, scoring, and fraud detection.
Nonetheless, the report additionally flags challenges.
These embody the necessity for standardised, excessive-high quality data and considerations round reliability. It recommends stronger help by way of regulatory frameworks just like the Account Aggregator, Unified Lending Interface (ULI), and implementation of the Digital Private Data Safety (DPDP) regulation.
First Printed: Apr 17, 2025 2:43 PM IST
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