tl;dr
Traditional bureau scores no longer reflect how India earns, spends, or manages money. Behavioural and transactional data—like bank flows, UPI activity, bill payments, gig income, and digital usage—offer a true, real-time view of borrower intent and ability to repay. These insights help lenders underwrite first-time borrowers, thin-file users, and gig workers with far more accuracy, fairness, and inclusion. This is the future of human-centric credit intelligence.
For decades, credit scoring has lived in black and white in a world defined by numbers, thresholds, and rigid risk bands. You were either “creditworthy” or “high-risk.” But humans don’t live in binary, and neither should lending decisions.
Enter behavioural and transactional insights, the new frontier of credit intelligence. Instead of treating borrowers as data points, lenders are now reading the story behind those data points: spending habits, saving rhythms, repayment consistency, and even how someone manages digital subscriptions or utility bills.
This evolution isn’t just about adding more data. It’s about adding more context. Where traditional bureau models see a score, behavioural models see a pattern and where old underwriting saw a profile, modern analytics sees a personality.
In a market as diverse and dynamic as India, this shift is transformative.
With over 300 million credit-invisible consumers, behavioural and transactional data are giving lenders a way to look beyond static credit histories and start seeing financial behaviour in motion.
The result? Smarter, fairer, and more inclusive credit decisions that are built not just on history, but on how people live and spend today.
From Scores to Signals: The Evolution of Credit Assessment
For decades, a three-digit score decided everything from whether someone could buy a bike to whether a small business could survive a cash crunch. The system worked well for those who had long credit histories, stable jobs, and neat financial records. But the problem lies in that number only represents a fraction of the country.
The rest, gig workers, first-time borrowers, small traders, and micro-entrepreneurs, live outside those predictable patterns. They may repay on time, but because they don’t fit into traditional reporting frameworks, they’re invisible to the system.
This is where behavioural and transactional data come into play. Instead of just looking backward at how someone borrowed before, lenders are now looking sideways at how they earn, spend, save, and manage money today.
The Shift in Model Thinking
Earlier models:
- Based on static, bureau-reported data.
- Worked on limited signals (repayment history, credit utilization, delinquencies).
- Missed out on real-time financial behaviours.
Now:
- Dynamic credit models analyse ongoing patterns like transaction frequency, salary consistency, payment behaviour, and even wallet usage.
- AI-driven underwriting systems detect anomalies and risk patterns in near real-time.
- Context replaces assumption, i.e. someone with a thin credit file but regular digital payments may be a far lower risk than their score suggests.
Instead of excluding millions for lack of history, lenders can now build adaptive, data-driven models that continuously learn from user behaviour. It’s not about replacing bureau data; it’s about enhancing it with signals that reveal a truer picture of financial health.
The Anatomy of Behavioural and Transactional Data
Not all data is created equal. What separates behavioural and transactional insights from traditional bureau data isn’t just their freshness, it’s their context.
While a credit bureau report tells you what happened (e.g., a missed payment), behavioural and transactional data tell you why and how it happened.
Let’s break it down:
Transactional Data: The Financial Pulse
This is the raw heartbeat of a customer’s financial life. It’s found in:
- Bank account transactions: frequency of salary credits, spending stability, and savings consistency.
- UPI and wallet patterns: how regularly users transact, and whether their inflows and outflows are balanced.
- Utility and bill payments: punctuality reflects financial discipline.
- Merchant payments: especially critical for MSMEs and gig workers, offering a transparent view of cash flow.
These are real-time, verifiable indicators of repayment capability, far more dynamic than a static score that updates monthly or quarterly.
When analysed over time, these signals reveal a customer’s financial rhythm including the peaks, troughs, and consistency that define how they actually manage money.
Behavioural Data: The Human Layer
Behavioural data captures intent, discipline, and financial maturity. It doesn’t come from financial institutions, but from digital footprints that reflect how people live and make decisions.
For example:
- App usage frequency: consistency in using financial apps may signal engagement and responsibility.
- E-commerce behaviour: purchase timing, categories, and frequency can reveal spending tendencies.
- Telecom data: stable mobile usage, consistent recharges, and longer SIM tenures correlate with stability.
- Employment patterns: job tenure, role progression, or gig activity patterns show income reliability.
It’s like understanding the person behind the profile. Someone who manages monthly subscriptions efficiently and consistently recharges on time may be a much safer credit risk than someone with a patchy but technically “clean” bureau score.
The Power of data
Individually, these signals are powerful. Together, they redefine risk altogether.
When lenders merge behavioural and transactional data, they can build composite borrower personas including multidimensional views that tell the full story, not just a financial snapshot.
This fusion makes it possible to:
- Approve first-time borrowers with no bureau footprint.
- Identify responsible informal earners earlier.
- Detect fraud or instability faster through pattern deviations.
Building Fairer Credit Models — Where Inclusion Meets Intelligence
For years, financial inclusion in India has been measured in surface-level metrics but true inclusion is not about access alone; it’s about understanding. When credit decisions depend solely on bureau scores, millions of capable borrowers get filtered out, not because they’re risky, but because the system has no way to see them. No loan history translates to “unscorable.” Irregular income is mistaken for instability. And cash-based livelihoods are perceived as opaque.
Behavioural and transactional data are rewriting this outdated logic. These new-age signals allow lenders to move beyond assumptions and into evidence-based inclusion. Instead of relying on a one-size-fits-all score, credit models can now differentiate between someone who’s new to credit and someone who’s genuinely high risk. By analysing how individuals earn, spend, and repay, lenders can finally recognise that a lack of history is not the same as a lack of creditworthiness.
This shift has enormous implications. First-time borrowers, gig workers, and small business owners, i.e groups long sidelined by traditional frameworks, are now being assessed on the merit of their real-world behaviour. A gig worker with regular digital inflows, consistent UPI transactions, and timely bill payments can now be seen as a reliable borrower, not an unknown quantity. This makes inclusion measurable and meaningful and hence driven by visibility.
But inclusion is just one side of the story. Fairer models also make credit safer and smarter. When lenders use dynamic, data-driven insights, they’re not only expanding access but also reducing delinquencies, identifying early risk signals, and personalising loan limits based on real-time repayment capacity. The result is a lending ecosystem that’s more responsive and equitable where underwriting decisions evolve with the borrower, not in isolation from them.
Technology is the backbone of this evolution. With AI and machine learning embedded in credit assessment systems, risk evaluation is no longer a static exercise but a continuous process. Borrowers are understood in motion — their financial behaviour monitored with transparency and consent. The Account Aggregator (AA) framework has been crucial in making this transition responsible and privacy-led, ensuring that inclusion doesn’t come at the cost of ethics.
Conclusion
As India’s credit ecosystem evolves, one truth is becoming impossible to ignore, the next leap in lending won’t come from bigger databases or faster approvals. It will come from better understanding.
For decades, financial systems have treated credit as a matter of numbers. But people aren’t numbers; they’re patterns, choices, and circumstances. Alternate data, especially behavioural and transactional insightsm brings that reality into focus. It allows lenders to see the story between the transactions, to interpret intent as clearly as income, and to build trust based not on past privilege but on present behaviour.
This is the essence of human-centric credit intelligence. A space where technology doesn’t replace judgment but refines it. Where algorithms don’t enforce bias but erase it. Where inclusion is not a compliance checkbox, but a natural outcome of visibility and context.
The future of credit in India won’t be decided by who has data, but by who uses it responsibly. As Account Aggregator adoption scales, as AI-driven models become more explainable, and as lenders grow more comfortable blending traditional and alternate signals, we’re moving toward a credit ecosystem that finally mirrors the complexity of the real world.
Fairer, faster, more fluid and most importantly, more human.