
Aarav runs a small online business and sees steady monthly inflows into his bank account. Rent and utility bills are paid on time, but formal loans have never been taken out before. When a loan application is rejected due to a thin credit file, it can feel unfair because financial discipline exists, just not reflected on a credit report.
This is where alternative credit scoring changes the picture. Lenders can assess repayment behaviour beyond traditional CIBIL-only models by using broader financial signals.
In India, traditional credit assessment relies heavily on bureau data from agencies such as CIBIL. This works well for borrowers with long credit histories. However, many first-time borrowers, gig workers, and MSMEs may have invisible credit.
Alternative credit scoring expands assessment by adding alternative data to understand cash flow stability and payment discipline. This improves financial inclusion while helping lenders reduce false rejections.
An alternative data credit score uses non-traditional ways to evaluate your repayment capacity. They reflect your everyday financial behaviour that does not appear on bureau reports.
Common alternative data used in India:
For example, a regular monthly salary credit of ₹45,000, consistent UPI inflows, and on-time electricity payments over 12 months can indicate stable repayment capacity, even with a limited credit history.
An alternative CIBIL approach does not replace bureau scores. It complements them. Traditional CIBIL scoring captures past loans and cards. Alternative models add context using cash flow and payment behaviour.
The combined views help lenders approve deserving borrowers who would otherwise be declined due to thin files. This improves access to formal credit for borrowers without compromising risk controls.
A CIBIL alternative credit score is most effective when bureau data and alternative data are evaluated together.
Also Read: How to Generate Your CIBIL Score for First Time
Alternative credit scoring uses AI and machine learning to assess repayment behaviour and combines it with bureau data. It forms a complete risk profile without relying just on past loans or credit cards.
Your credit assessment becomes strong with on-time bill payments, a stable monthly income, and account balances. Irregular flows or overdrafts may indicate a higher risk.
This approach improves decision-making by detecting fraud and expediting approvals. It also makes outcomes easier to explain in complex cases for review.


Set clear approval and risk benchmarks by identifying first-time borrowers or gig workers with thin files.
Work with compliant providers for bank, UPI, utility, and GST data, and ensure consent-based, secure data sharing.
Combine traditional scores with alternative data to improve accuracy without weakening risk controls.
Run small pilots to track approvals and early delinquencies before scaling.
Update features and thresholds as borrower behaviour and data quality change.
If eligibility needs to be checked for personal credit, it can be done quickly through Hero FinCorp’s official journey.
Also Read: Business Credit Score vs Personal Credit Score
Responsible use of alternative data requires data collection based on content, purpose, and limitations, as well as the secure handling of personal data. Being transparent about how data is used builds trust.
Governing the model, ensuring explainability, and addressing consumer grievances are important for achieving compliance in India.
The future looks promising, with wider use of real-time bank data, deeper insights into UPI, and better fraud detection using device signals.
You will notice it more across MSME and gig workers as the frameworks for consent mature. However, the focus will be on explainable AI and privacy-focused designs.
It uses everyday financial behaviour and bureau history. Traditional scoring relies on past loans and cards.
Reliability improves when high-quality, consented data is blended with bureau inputs and supported by explainable models.
Stable bank inflows, regular UPI activity, and timely bill payments can strengthen assessment for thin-file borrowers.
Bank transactions, UPI patterns, rent and utilities, gig income, and GST data for MSMEs.
It reduces false declines and improves risk profiling when combined with bureau scores.
Data is used with explicit consent, secured storage, limited purpose, and clear disclosures.
Disclaimer: The information provided in this blog post is intended for informational purposes only. The content is based on research and opinions available at the time of writing. While we strive to ensure accuracy, we do not claim to be exhaustive or definitive. Readers are advised to independently verify any details mentioned here, such as specifications, features, and availability, before making any decisions. Hero FinCorp does not take responsibility for any discrepancies, inaccuracies, or changes that may occur after the publication of this blog. The choice to rely on the information presented herein is at the reader's discretion, and we recommend consulting official sources and experts for the most up-to-date and accurate information about the featured products.