A decision on your loan application is no longer based only on your salary slip or credit score, but a continuous stream of digital parameters that borrowers may not fully understand, according to experts.
Lenders operate in a “data economy”, where everyday financial behaviour such as bill payments, account activity and even subscription patterns shapes credit decisions in real time.
From paperwork to “financial character”
Traditionally, lenders relied on documents such as income proofs, bank statements and credit bureau scores. While these remain relevant, they are not sufficient.
“Most people assume lenders are looking for a reason to say ‘no’, but the data economy is actually designed to help us say ‘yes’ to the right people,” says Shakti Shekhawat, business head at BharatLoan, a non-banking financial institution (NBFC).
Instead of focusing only on income, lenders are now assessing what Shekhawat describes as “financial character”— patterns of how a person manages money.
Such patterns include
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Timely payment of utility bills and loan repayments -
Bank account activity -
Failed auto-debits or subscriptions -
Changes in contact details or banking behaviour
People with steady financial behaviour can be “up to 20 per cent more reliable than even higher-income individuals with erratic financial patterns,” said Shekhawat.
High income alone may not result in a loan approval if spending or repayment behaviour is inconsistent. Conversely, disciplined financial habits can strengthen the case for borrowers with modest incomes.
The rise of verifiable, real-time data
Another major shift is the move from self-reported or document-based data to verifiable financial information.
“The real story isn’t that lenders have more data — it’s that we finally have verifiable data,” says Kuldeep Yudhuvanshi, business head, Rupee112, another NBFC.
Subject to borrower consent, India’s Account Aggregator (AA) framework allows lenders to access financial data directly from banks, goods and services tax (GST) systems and other regulated entities to evaluate:
Real-time bank transaction patterns
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Cash-flow consistency -
GST filings for business owners -
Actual debt obligations and declared income
“When we see a borrower’s live GST filings or real-time bank metadata, the human error and fraud margin drops by nearly 40 per cent,” says Yudhuvanshi.
In practical terms, this means loan decisions can now be made quickly — often within minutes — and more accurately.
Live financial signals
Another significant change is the shift from backward-looking assessments to real-time evaluation.
Earlier, a lender’s view of a borrower was largely based on historical data such as a credit report or bank statements. Today, that view is increasingly dynamic.
“For the first time, we aren’t lending based on a snapshot of the past; we are lending based on a live stream of the present,” Yudhuvanshi explains.
This approach allows lenders to capture changes in financial behaviour. For instance:
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A recent increase in income or business turnover can be quickly recognised -
Irregular cash flows or rising obligations may trigger caution -
Seasonal income patterns, common in India, can be better understood
The result is a more responsive credit system, but also one that continuously evaluates borrower behaviour.
What this means for borrowers
For individuals, this evolving data ecosystem has both advantages and implications.
On the positive side:
Greater access to credit: Those without a formal credit history can still qualify based on behavioural data
Faster approvals: Real-time verification reduces processing time
Fairer assessment: Decisions are less dependent on subjective judgement or incomplete documentation
However, there are also important considerations:
Consistency matters more than ever: Irregular financial behaviour can directly impact loan eligibility
Digital footprint is critical: Everyday financial actions leave signals that lenders interpret
Transparency remains limited: Borrowers may not fully know which behaviours are influencing decisions
Bridging the trust gap
At its core, the data-driven lending model is attempting to solve a long-standing problem in Indian finance: trust.
Millions of borrowers, particularly those new to credit, have historically been excluded due to lack of formal documentation or credit history. By analysing behavioural data, lenders aim to bridge this gap.
As Shekhawat puts it, the goal is to identify “millions of Indians who are honest and disciplined but may not yet have a traditional credit history.”
Yet, the asymmetry has not disappeared entirely, it has simply evolved. While lenders now have deeper visibility into borrower behaviour, individuals often remain unaware of how these data points are interpreted.
In a system increasingly powered by algorithms, understanding one’s own financial behaviour may soon become as important as maintaining a good credit score.
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