In India, having a steady income, paying bills on time, and managing the money properly do not always lead to a loan being approved. Millions of people who are considered creditworthy are not given the loan just because they do not have a formal CIBIL score. New borrowers, freelancers, small traders, and young workers are often categorized as having “thin-file” credit histories, meaning they are financially disciplined, but this is not recognized in the traditional credit systems. This gap is being bridged by using NFBC alternative data credit scoring India to evaluate real-world financial behavior. This practice makes it possible to conduct fairer assessments, lower the number of people excluded from credit, and increase the number of borrowers who are able to get loans that were previously denied under traditional lending practices.
Why Traditional Credit Scores Aren’t Enough Anymore
Lenders have been dependent on credit scores from servicing companies such as CIBIL for several years. Today, how NBFCs assess creditworthiness has evolved beyond bureau scores, with lenders analysing cash-flow patterns, digital payment behaviour, income stability, recurring bill payments, and transaction trends to determine real repayment capacity, enabling faster, fairer, and more inclusive lending decisions. In India, a vast number of people, i.e. the financially responsible ones, the first-time borrowers, the gig workers, the small traders, and the young earners, are being left out, because they do not have a formal credit history. This demonstrates the drawback of the existing credit scoring system that does not involve CIBIL. The adoption of a wider, behaviour-based assessment model by NBFCs for digital finance is paving the way for quicker, more just, and more inclusive lending decisions.
What Is Alternative Data in Credit Scoring?
Non-traditional financial data in the scoring process of credit refers to the utilization of alternative data in lending, such as when data from credit reporting agencies is insufficient or non-existent. Rather than relying solely on an individual’s credit history, lenders are looking at their actual financial conduct, such as the movement of money in and out of bank accounts, payment of rent and utilities on time, use of digital payment platforms, and earnings from freelance or gig job. Providing such information lenders acquire a more precise and up-to-date perception of a borrower’s financial discipline which in turn leads to more equitable credit decisions and increase in informal lending access.
Types of Alternative Data NBFCs Use
In order to cater to the vast new-to-credit and thin-file population of India, NBFCs, besides relying on bureau reports, are also using alternative data for the purpose of evaluating not only the intention of borrowers but also their stability and financial behaviour. Some of the most crucial data points are:
- Banking & Cash-Flow Data: The inflow of income, the spending habits, the average balances, and the cash withdrawals are analyzed to decide the liquidity of the borrower and his/her capacity to repay in real-time.
- Utility, Rent & Recurring Payments: The payment of regular electricity, water, mobile, broadband, and rent shows the financial discipline and reliability of the customer.
- Employment & Income Signals: Validation of the stability and continuity of income through salary credits, gig-platform earnings, EPFO records, and GST filings.
How NBFCs Convert Alternative Data into a Credit Score
NBFC loan approval process utilize artificial intelligence (AI) based credit assessment systems to turn alternative data into a credit score that can be used, always focusing on the borrower’s behaviour and not on the traditional paperwork. The workings of the process are as follows:
- Data collection with consent: The alternative data, such as bank transactions, cash-flow patterns, bill payments, and others, are collected securely.
- Analysis through machine learning: AI algorithms analyze the financial behaviour, stability of the income, and risk signals in the hundreds of variables.
- Scoring by prediction: The behaviour patterns are turned into a dynamic risk score that assesses one’s ability to repay.
Is Alternative Data Safe & RBI-Compliant?
If used professionally, alternative data is secure and within the limits of India’s regulatory framework. The Reserve Bank of India has often and in different ways said that trust is the most important factor in banking, and this is more so with the introduction of AI-based credit scoring. The guidelines of the RBI remove the customer’s prior consent, require high-quality data to be used, put in place privacy safeguards that are very strict, and finally demand human supervision for AI-assisted lending decisions.
RBI is making more, in the form of promoting explainable AI, strong governance, and clear accountability, to overcome risks like algorithmic bias, under the radar models, and cyber threats. The recent policy directions, including the FREE-AI framework, are a major boost to transparency, auditability, and security across the credit lifecycle.
How Credit Assist Helps Low & Thin-File Users
It is not uncommon that low-risk and thin-file borrowers find their applications denied, not because they are unacceptably risky, but rather due to the incapacity of conventional underwriting methodologies to unveil their actual financial behavior. The following is a point of differentiation for Credit Assist:
- Behavior-first assessment: It goes past the credit bureau records and scrutinizes the cash flows, GST filings, bank transactions, and regular bill payments.
- Bias-aware Design: It stops the unfair penalties based on location, job type, or lack of standard documentation.
- Explainable Insights: It gives clear, contextual reasons for every risk flag or approval signal.
Benefits of Alternative Data for Borrowers
Lenders can evaluate a person’s creditworthiness through alternative data by observing his/her actual financial behaviour instead of relying solely on past loans or credit cards. In this scenario, borrowers, particularly those with limited or no credit history, are highly advantaged.
Key benefits for borrowers include:
- Access to Credit Improved: People who have no credit record, self-employment, or gig work, can still apply for and get loans even though they do not have the traditional credit history with them.
- More just Evaluations: The payments for rent, utilities, and telecoms made regularly show good financial management, better than previous models that were still groundbreaking.
- Lower rates of Rejection: A reevaluation of the individual’s credit based on a wider data view results in the removal of many unnecessary denials caused by thin credit or incomplete files.
The Future of Credit Scoring in India
India’s credit scoring system is shifting from static, history-based models to dynamic assessments that reflect changing consumer behaviour. Driven by digital finance and evolving regulations, lenders now rely on continuously updated, data-driven risk models using real-time signals rather than only past records. These models evaluate cash-flow trends, digital payment activity, and repayment behaviour to build adaptive credit profiles. This evolution supports faster approvals, customised credit terms, and broader inclusion making low CIBIL loan approval increasingly possible, especially for new and self-employed borrowers. At the same time, the Reserve Bank of India promotes explainable AI, strong consent frameworks, and human oversight to ensure fairness and trust.
FAQs
- What is alternative data in NBFC lending?
Alternative data refers to non-conventional sources of data that include bank transactions, bill payments, and income patterns, among others, which are being used by the NBFCs to determine creditworthiness besides the bureau scores.
- Can I get a loan without a CIBIL score?
Yes, many NBFCs take into consideration behavioural and cash-flow data in their evaluation process for granting loans to borrowers who are new to credit or who have limited credit history.
- Is alternative data safe to share?
Yes, it is safe to share alternative data as long as it is done with the consent of the individual. Lenders regulated by the RBI are required to adhere to very strict rules concerning privacy, security, and the limitation of purpose.
- Does alternative data improve loan approval chances?
It considerably assists well-behaved borrowers with little credit history to receive fair treatment.
- How does Credit Assist use alternative data?
Perfios has created Credit Assist, which uses alternative data analysis to recommend the assessment, clarify the outcomes, and provide support for credit-aid, all while ensuring compliance with the guidelines set by the Reserve Bank of India.
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