How lenders now assess borrowers: Home First Finance CEO shares 5 key trends

Salary slips and income tax returns (ITRs) are no longer the sole basis for loan assessments in India, as lenders increasingly rely on cashflow patterns, UPI transactions, GST filings and financial behaviour to evaluate borrowers, according to Manoj Viswanathan, MD & CEO of Home First Finance Company.

Viswanathan identified five trends driving the evolution of credit assessment in the country, particularly for self-employed individuals, small business owners and gig workers whose earnings may not always be reflected through conventional income documentation.

Growing use of alternative data

Lenders are relying on data points such as UPI transaction histories, QR-code merchant collections, GST filings, banking transactions and credit bureau records to assess borrowers, Viswanathan said.

These inputs help institutions evaluate income consistency and repayment discipline beyond traditional documents.

Shift from static documents to cashflow analysis

Credit assessment is moving away from relying solely on document-based snapshots of income towards analysing ongoing cashflows.

This approach provides lenders with a more continuous view of a borrower's financial health and repayment capacity.

Wider adoption of Account Aggregator framework

The rollout of consent-based financial data-sharing infrastructure, including the Account Aggregator (AA) framework, has improved lenders' access to structured financial information.

This has enabled deeper analysis of cashflows, business continuity and income stability.

Digitisation expanding the data trail

Post-pandemic adoption of digital payments has increased the availability of verifiable financial data across borrower segments, including informal earners and small businesses.

At the same time, a growing credit footprint among first-time and emerging borrowers has strengthened lenders' ability to assess repayment behaviour.

Emergence of hybrid underwriting models

Viswanathan said underwriting models are combining traditional documentation with behavioural and transaction-level data.

Lenders are also deploying data science and AI-driven systems to analyse banking patterns and financial behaviour, while continuing to rely on human judgement for contextual validation.

The MD & CEO of Home First Finance Company noted that the broader shift in underwriting reflects a move away from occupation-based profiling towards evaluating the sustainability and stability of income, with both salaried and self-employed borrowers being assessed through similar parameters of repayment capability and financial discipline.

However, Viswanathan cautioned that expanding financial inclusion must remain aligned with responsible lending practices, with banking behaviour, bureau data and repayment history continuing to serve as key safeguards against excessive borrowing.