According to a study by TransUnion CIBIL, a large number of people, roughly 480 million, or half of India's adult population under 65, are considered "credit unserved", which means that they have no credit score. Gig workers, students, micro-entrepreneurs, and individuals in underbanked communities are among them. Since traditional credit scoring relies on borrowing and repayment history, it does not cover individuals who never took a loan or utilised a credit card in the past. Even good credit-worthy individuals might be denied access to credit simply because they do not have a formal credit history.
How Banks Use Alternative Data
In an attempt to plug this gap, banks have started using alternative data—data that provides information on a person's financial behaviour. These include rent payments, mobile bill payments, electricity usage, wallet bills, UPI payments, and online shopping. For example, a person who pays his or her rent and utility bills on time every month and who makes frequent digital transactions shows evidence of fiscal responsibility. Although not monitored by credit bureaus, such behaviour speaks volumes about repayment ability.
Such data would typically be collected directly from service providers, websites, or even permission-based access to a borrower's bank transaction history or phone bill. Without a credit score, such actual financial conduct gives banks a new form of "credit signal."
Technology's Role In Interpreting Risk
AI and machine learning (ML) algorithms now form the core of lending systems. The algorithms analyse vast volumes of digital behaviour for patterns that are related to creditworthiness. For example, a person who consistently makes digital payments, maintains a sound balance in their savings account, and fulfils small BNPL dues on time may be rated as low-risk—without any history of borrowing.
Banks feed historical repayment data to these models and train them, and the system can subsequently learn to estimate the probability of repayment from new applicants. The result is a credit decision that doesn't rely on credit scores but remains data-driven.
Credit Profiling Reaching Beyond Formal Loans
Some banks and fintech lenders even consider social and professional signals. A borrower's educational level, job status, or profession can be utilised to build a risk profile. A salaried employee employed by a reputed company, or a verified supplier on an e-commerce platform, may receive a good risk score even if they do not have a credit score.
Similarly, digital footprint like app activity or mobile recharge frequency is being used more and more to ascertain lifestyle stability. As flawed as these proxies are, they can be employed to make an initial assessment for credit cards or small-ticket loans.
Increasing Financial Access Through New Channels
This shift is beneficial to those who have historically been excluded from traditional credit systems. Gig workers, freelancers, street vendors, small business owners, and college students can now borrow money based on their online behaviour rather than documentation or collateral. With more tailored products, quicker disbursals, and simpler terms, credit is more accessible.
For the lenders, too, the method is advantageous. It allows them to expand their customer base and offer more specialised loan products. The data-based models also get rid of human bias and are capable of lowering default rates over time because they learn and get better with feedback and in real time.
Challenges In A Data-Based System
Despite its promise, this model is not problem-free. Too many individuals still do not leave adequate digital trails to be scored adequately. A person who pays bills in cash or does not shop online may be too "thin-file" to score—despite being a good financial bet. And then there is the question of accuracy. Misinformation or omission of rent payments or electronic transactions can distort a risk profile.
Moreover, there is also digital exclusion to be overcome. Individuals who lack smartphones, continuous internet connection, or familiarity with online interfaces may still not be included. It also raises questions about data privacy, especially when intimate personal information is collected to make credit decisions based on.
A Shift Towards Data-Driven Credit Inclusion
Whereas the constraints are clear, the growing use of alternative data foreshadows a broader shift in the nature of creditworthiness. Rather than relying on a single figure, banks are building multi-dimensional borrower profiles that better reflect true behaviour. In an increasingly digitising economy, this approach may be both necessary and transformative.
As this evolution unfolds, banks are refining their models to balance speed and sensitivity and risk and inclusion. For the kinds of borrowers who were formerly off the radar of the credit system, this new approach might bring them the financial access and dignity they've long been denied.
